Network cable form used in any infrastructure of network serves as the most significant networking aspect in several industries.

Types of Network cables

Coaxial cable

A single conductor of copper is there in the middle of coaxial cable. Insulation is provided by plastic layer between center conductor and braided metal shield. The outer interference that comes from the fluorescent lights, motors and other computers is blocked by the metal shield.  Installation of coaxial cable is complex and offers extreme signal obstruction resistance. Enormous lengths of cable between devices of network are handled by it compared to twisted pair cable. Thick coaxial and thin coaxial are the two forms of coaxial cables.

The coaxial cable has the unique design that allows its installation near metal objects as well without any losses in power that takes place in lines of transmission. The transmission capability of coaxial cable is 80 times more in comparison to the twisted pair cable. The main use of such cables is in the feedlines that connect radio receivers and transmitters with antennas, cable television distributed signals and connections of computer network.

Unshielded twisted pair

This form of network cable is the most admired one in the world and is perfect for both computer networking and conventional telephone networking. Following are the various schemes of wiring available for UTP:

• CAT 1 useful for the telephone wire
• Speeds of upto 4 Mbps are supported by CAT2 and are frequently used for the token ring networks.
• For the token ring networks that have higher speeds of network, the wiring scheme used is CAT 3 and CAT4.
• CAT5e has replaced CAT5 since they offer improved crosstalk specification that allows support speeds of up to 1 Gbps. This is world’s most used specification of network cabling.
• Speeds of over 1 Gbps are supported by CAT6 for over 100 meters of length and for 55 meters, the speed is 10 Gbps. A distinct cable analyzer must be used by companies using CAT6 cabling for requesting overall test report.
• A fresh pattern of copper cable is CAT7 that supports 10 Gbps speeds and 100 meters length.

STP or shielded twisted pair cable

This distinct copper telephone wiring form is used in the installations of businesses. The general telephone wires of twisted pair are added with external shield that acts as the ground. STP serves to be the most suitable solution if you are in search of cable for the place having electrical current risk in UTP or potential interference. With the help of shielded cables, the distance between cables could also be expanded.

Fiber optic cable

Center glass core is there in the fiber optic cable and several protective material layers surround it. The electrical obstruction problem is removed by this cable with the transmission of light in place of electronic signals. This is the reason why they serve as the perfect options for some atmospheres having electrical interference in enormous amount. For networks connection between buildings, it serves as the standard choice on account of its resistance to moisture and lighting.

A glass threads bundle is present in the fiber optic cable, each having the capability of spreading the messages that are modulated on the light waves. The structure and design of fiber optic cable is complicated. Outer optical casing is there in this form of cable that surrounds light and catch it inside central core. Configuration of inner section of cable must be done in two ways: multi-mode and single mode. While the difference is small, the fiber optic cables usage and performance are affected tremendously.

Comparison Table

Download the comparison table: types of network cables comparison

Copper, Fiber or both? Aligning choice with business outcomes

Copper cabling (currently Category 6A for most new builds) remains the workhorse for desk‑top and wireless access‑point connections. It delivers 10 Gbps up to 100 metres, supports Power over Ethernet ++ for IoT endpoints, and is economical when routes are short and patching flexibility is needed. Fiber, by contrast, offers virtually limitless bandwidth, intrinsic immunity to electromagnetic interference and longer reach, making it ideal for risers, data‑centre trunks and sprawling industrial estates.

Most enterprises now deploy a hybrid architecture: multi‑core OM4 or OS2 fiber for backbone links and copper for the horizontal. The split is not merely technical — it reflects risk tolerance, budget horizon and growth plans. A site anticipating high‑density Wi‑Fi 7 or augmented‑reality workflows may justify Cat 8 to each consolidation point, whereas a professional‑services office with stable head‑count may be better served by Cat 6A and a disciplined refresh cycle.

Standards, Compliance and the UK Regulatory Backdrop

British businesses must navigate a web of standards that govern safety, performance and fire behaviour. BS 6701 sits at the heart of UK cabling practice, referencing the ISO/IEC 11801 series for generic cabling design and BS EN 50173 for performance classes. Since 2017, the EU Construction Products Regulation (CPR) has forced manufacturers to declare reaction‑to‑fire classes (Eca through B2ca) for cables used in fixed installations — a requirement that still applies in Great Britain post‑Brexit. Choosing the correct Euroclass is more than a checkbox; it dictates evacuation time in an emergency and may influence insurance premiums.

A compliant design will also respect BS 7671 (IET Wiring Regulations) for segregation from power circuits, and Adopted Guidance from The Joint Code of Practice (JCoP) on hot works and penetrations. Skimping on these details passes risk down the supply chain, exposing facilities managers to contractual disputes and costly remedials.

Planning for Capacity

Unlike laptops or access points, cabling should last through at least two device refresh cycles — typically 15 years. Forward‑looking surveys therefore model not today’s bandwidth but tomorrow’s. Questions worth asking include:

• Device density: How many IoT sensors or wireless radios could occupy each zone in five years?
• Power budget: Will you be driving LED lighting or pan‑tilt‑zoom cameras over PoE++?
• Pathway headroom: Are tray and basket routes sized for 40 % spare fill to accommodate moves and adds without invasive works?
• Building fabric: Are there heritage constraints that limit containment routes or require non‑intrusive fastening methods?

By treating cabling as a strategic asset rather than a sunk cost, organisations avoid the expensive game of perpetual catch‑up.

Installation Best Practice

Cutting‑edge components can be hobbled by poor workmanship. The following disciplines separate robust networks from intermittent nightmares:

• Bend‑radius discipline: Both copper pairs and fiber strands lose performance when over‑bent. Adhere to the manufacturer’s minimums; train installers to spot and rectify tight loops during fix.
• Separation from noise sources: Maintain at least 200 mm clearance from fluorescent ballasts, high‑voltage conduits and lift motors, or employ shielded solutions where proximity is unavoidable.
• Cable management and labelling: Neatly dressed looms with velcro (never plastic ties) allow airflow, simplify audits and cut Mean Time To Repair. A structured labelling scheme — typically floor‑rack‑port — can shave hours off troubleshooting.
• Containment integrity: Fire‑stopping around penetrations using intumescent pillows protects compartmentation, a legal requirement under the Regulatory Reform (Fire Safety) Order 2005.

Testing and Certification

Lives and livelihoods run over cabling; proof matters. A credible contractor will test 100 % of links with calibrated field testers, typically a Fluke Networks DSX or similar, capturing not just pass/fail but parameters such as insertion loss, NEXT, return loss and propagation delay. Results should be supplied in an open format (PDF or XML) and retained with O&M manuals. For fiber, OTDR traces complement light‑source and power‑meter tests, revealing micro‑bends and splice anomalies that visual inspections miss. Certification at hand‑over shifts risk away from the client and accelerates sign‑off for landlords or main contractors.

Lifecycle and Sustainability

Corporate sustainability targets now reach into the comms room. While cables themselves are low‑energy consumers, their production and disposal carry a carbon footprint. Best practice includes:

• Modular containment that can be re‑used when walls are repositioned.
• Halogen‑free sheathing to minimise toxic emissions if a fire occurs.
• Take‑back schemes where off‑cuts and decommissioned looms are recycled into new polymer or copper products.

Embedding these principles early aligns the network with Environmental, Social and Governance (ESG) objectives and scores points in tenders that evaluate life‑cycle impact.

Avoiding Common Pitfalls

1. Design‑by‑spreadsheet: Relying solely on CAD counts without a physical walk‑through leads to surprise obstructions and under‑budgeted containment.
2. Over‑specification without justification: Deploying MPO‑terminated OS2 trunks for a low‑rise office drives up costs without tangible benefit. Align specification with a documented business case.
3. Lack of change control: Untracked moves and additions erode the accuracy of as‑built drawings, hampering future upgrades. Institute a simple permit‑to‑patch process.
4. Ignoring warranty conditions: Using non‑approved patch cords can void 25‑year system warranties. Verify component interoperability beforehand.

What forward‑thinking firms are exploring

• Single‑Pair Ethernet (SPE): Promises lower‑cost networking for sensors over 1,000 metres, potentially supplanting RS‑485 in factories.
• Wi‑Fi 7 densification: Access points capable of 30 Gbps aggregate throughput will demand multiple Cat 6A or a single Cat 8 link — plan pathways now.
• Edge Compute and Micro‑DCs: As enterprises localise processing for AI workloads, fiber backbones and high‑density cabinet patching will become decisive.
• Smart‑building convergence: Lighting, security and HVAC increasingly ride the IT cabling plant, collapsing departmental silos and intensifying PoE power draws.

Monitoring these trends keeps the cabling conversation strategic rather than reactive.

Data Cabling as Critical Infrastructure

Data cabling rarely features on glossy strategy decks, yet it underpins every digital initiative from hybrid work to analytics at the edge. Approached correctly, it is a once‑a‑decade investment that repays itself through reliability, agility and regulatory peace of mind. Ignore it, and you invite operational bottlenecks, compliance headaches and costly re‑work. Whether you manage a heritage listed HQ or a new build logistics hub, placing data cabling on the agenda today is a concise gesture towards tomorrow’s competitiveness.

In today’s topic we will look more in detail about the network security model which exhibits how security service is designed, its components, how it works and its features.

Network Security Model

Network security model describes how a security service is designed to prevent cyber attackers from causing threat to confidentiality and authenticity of information transmitted or exchanged over the network. Exchange of messages happen between a sender and receiver and before transmission they need to mutually agree on sharing the message with this comes in picture is the communication channel or information channel which is an Internet service.

When message is transmitted over the network between sender and receiver, it involves three components from security service perspective namely: 

• Transformation – of information which needs to be sent or received to be encrypted so that a cyber attacker is unable to intercept it. It may involve addition of code during the transmission of information which will be used to verify the identity of the receiver.
• Secret information / key sharing between sender and receiver is used to encrypt messages at sender end and decrypt at receiver end. 
• Trusted third party is the one which should take the responsibility of distributing secret information / key to both sender and receiver involved in communication without the involvement of intruder or cyber attacker.

Network Security Model Architecture

The network security model above depicts two parties in communication where sender and receiver mutually agree to exchange information. Sender wants to send some messages to the receiver but cannot transmit them in clear text format as it would have risk of interception by intruder. So before sending a message to the receiver via an information channel, it should be transmitted in an unreadable format. 

• Secret information / key is used to transmit a message to receiver along with key to make message readable to the receiver and thus a third party comes into the picture who would be responsible for distributing the secret information / key to both the parties involved in communication.
• An encryption key used in conjunction with transformation to scramble message before transmission and then unscramble it on receiving it.
• Encryption provides data protection while key management is required to enable access to data which require protection from unauthorized parties.

Cryptography

Cryptography is used to store / transmit data in a specific format so that only those from whom it is intended are able to process it. The cleartext is scrambled into ciphertext (known as encryption) and then back again, known as decryption. There are in general three types of cryptographic schemes commonly used: secret key (or symmetric) cryptography, public-key (or asymmetric) cryptography, and hash functions.

Types of Cryptography

Secret Key (or symmetric) Cryptography

A single key is used for encryption and decryption both as depicted in figure below.

Sender A uses Key M to encrypt plaintext message L and sends the ciphertext O to the receiver. The receiver applies the Key M to decrypt the ciphertext O and recover plaintext L. Key must be known to both sender and receiver here. 

Public-key (or asymmetric) Cryptography

Encryption is performed using different keys – public and private key. Sender uses the public key of the receiver to encrypt plaintext message L and sends ciphertext O to the receiver. The receiver replies with its own private key to decrypt the ciphertext O and recover plaintext message L.

Hash functions

A digital signature is an authentication mechanism to enable the creator of a message to attach a code which acts as signature. Signature is formed by taking the hash of the message and encrypting the message with the creator’s private key. Signature is used to guarantee message source and its integrity.

In today’s topic we will learn about virtual firewalls and three use cases of virtual firewalls in detail. 

About Virtual Firewall

A virtual firewall provides network security for virtualized environments such as cloud. Virtualization process allows creation of multiple virtual instances of a physical device or a server and allows more efficient utilization of underlying physical resources and more flexibility for network management. Virtualization technologies brought some new set of security risks as well such as unauthorised access to virtual resources and increased data breaches.

The virtual firewalls become the gatekeeper or keeper of perimeter security again like their physical avatars. Virtual firewalls operate at the virtualization layer and protect virtual machines (VMs) or any other virtualized resources in cloud networks. Virtual firewalls provide additional functions such as VPN connections, intrusion detection and prevention and malware protection.  

Virtual firewalls secure cloud deployments and so they are also called cloud firewalls. They can scale with virtual environments and protect against north-south traffic and allow fine grained network segmentation within virtual networks. 

Benefits of using a Virtual / Cloud Firewall

• Cloud native virtual firewalls centralize security and apply policies consistently to all virtual machines and applications
• Virtual firewall upgrades are easier compared to management and upgrades of physical firewalls
• Virtual firewalls are safest way to quickly rollout cloud applications 
• More cost effective as compared to their physical counterparts
• Provide cloud native threat detection and prevention capabilities to secure data and applications.

Virtual Firewall Use Cases 

Use Case 1: Securing Public Clouds 

Public clouds such as Google cloud platform (GCP), Amazon web services (AWS) and Microsoft Azure host virtual machines to support different types of workloads, virtual firewalls secure these workloads. 

Virtual firewalls are deployed to implement advanced security capabilities such as threat detection and segmentation to isolate critical workloads to meet regulatory requirements such as GDPR, HIPAA, PCI-DSS etc.

To secure flow of traffic moving laterally within cloud networks Virtual firewalls implement inline threat prevention mechanism.

Use Case 2: Security Extension to branches and SDNs

Virtual firewalls help in securing systems at branch offices and for software defined networks. In SDN environments data routing and networking is controlled with software virtualization. Deployment of virtual firewalls in SDN environments allow organizations to secure their perimeter, segmentation of network and extend protection to remote branches.

Advanced firewalls in SDN networks provide consistent network security and help to manage branch network security from a centralized console, segmentation of networks to support isolation, secures the live network flow and sets the stage for secure migration of applications to cloud. 

Use Case 3: Protection of Cloud Assets 

Virtual firewalls enhance security of private cloud assets. They come with policy based, auto provisioning of security capabilities for networks and help in securing private cloud assets quickly and support in workload isolation from one another. 

List of top High-Income Tech Skills

Generative AI (GenAI)

GenAI involves creating new content or data patterns using artificial intelligence. Professionals skilled in GenAI are in high demand across various industries.

Average Salary**: AI engineers earn approximately $132,855 annually. 

Top Certifications

Data Analysis

Data analysis entails collecting, processing, and interpreting data to inform business decisions.

Average Salary**: Data scientists can expect around $164,818 per year.

Top Certifications 

Software Development

Software development focuses on designing, coding, and maintaining software applications.

Average Salary**: Software engineers typically earn about $162,834 annually. 

Top Certifications

User Experience (UX) Design

UX design centers on enhancing user satisfaction by improving usability and accessibility.

Average Salary**: UX designers have an average salary of $126,035. 

Top Certifications

Web Development

Web development involves building and maintaining websites, encompassing both front-end and back-end development.

Average Salary**: Full-stack developers earn around $125,048 per year. 

Top Certifications

Cloud Computing

Cloud computing focuses on delivering computing services over the internet, including storage, processing, and networking.

Average Salary**: Cloud architects can earn between $108,250 and $129,750 annually. 

Top Certifications

Cybersecurity

Cybersecurity involves protecting systems, networks, and data from digital attacks.

Average Salary**: Cybersecurity engineers can earn salaries exceeding $500,000, especially in specialized roles. 

Top Certifications

Related: Artificial Intelligence vs Machine Learning

Machine Learning

Machine learning is a subset of AI focused on developing algorithms that enable machines to learn from data.

Average Salary**: Machine learning engineers have an average salary of $142,301. 

Top Certifications

Project Management

Project management entails planning, executing, and overseeing projects to achieve specific goals within constraints.

Average Salary**: Technical program managers earn around $174,000 annually. 

Top Certifications

AI Ethics

AI ethics focuses on the moral implications and responsible use of AI technologies.

Average Salary**: AI ethicists can earn salaries over $500,000, reflecting the growing importance of ethical considerations in AI development.

Top Certifications

**Average Salary varies by region, seniority, and company size**

References:

https://www.coursera.org/articles/high-income-skills

https://www.cio.com/article/230935/hiring-the-most-in-demand-tech-jobs-for-2021.html

What is Cisco ThousandEyes?

Cisco Thousandeyes is a cloud-based platform that allows businesses to measure, monitor, and troubleshoot network performance. It provides a comprehensive view of the entire infrastructure, including public and private networks. ThousandEyes offers visibility into the performance and health of applications, websites, networks, and cloud services. It helps organizations ensure reliable and secure user experience and network performance across the globe.

ThousandEyes provides insights into the performance of applications, websites, and networks, as well as the health of cloud services. It offers network intelligence, visibility, and analytics, allowing organizations to monitor the user experience and application performance. ThousandEyes also provides tools for troubleshooting and diagnosing network performance issues, allowing businesses to quickly identify and solve problems.

Benefits of Cisco ThousandEyes

• Improved network performance: Thousandeyes provides insights into the performance of applications, websites, and networks, as well as the health of cloud services. This allows organizations to monitor the user experience and application performance, and ensure reliable and secure network performance.
• Comprehensive visibility: ThousandEyes provides a comprehensive view of the entire infrastructure, including public and private networks. This allows businesses to visualize the performance of their networks and identify potential performance issues.
• Real-time insights: ThousandEyes provides real-time insights into application and network performance. This allows businesses to quickly identify and troubleshoot performance issues.
• Easy to use: ThousandEyes is easy to use and provides a user-friendly interface. This makes it easy for businesses to monitor and troubleshoot network performance.

Features

ThousandEyes provides a range of features to help businesses measure, monitor, and troubleshoot network performance. Some of the features include:

• Network monitoring: It provides network monitoring capabilities, allowing businesses to visualize the performance of their networks and identify potential performance issues.
• Application monitoring: It provides application monitoring capabilities, allowing businesses to monitor the performance of applications and websites.
• Cloud monitoring: It provides cloud monitoring capabilities, allowing businesses to monitor the performance of cloud services.
• Troubleshooting: It provides tools for troubleshooting and diagnosing network performance issues.
• Analytics: It provides analytics capabilities, allowing businesses to track performance trends and identify potential issues.
• Visualizations: It provides visualizations of performance data, allowing businesses to quickly identify and troubleshoot performance issues.

ThousandEyes Platform Architecture

ThousandEyes is built on a distributed architecture. It is designed to be highly available and scalable, allowing businesses to monitor and troubleshoot network performance in real time. The platform is composed of several components, including:

• Agents: are installed on customer sites and collect performance data.
• Data collectors: are responsible for collecting performance data from the agents and sending it to the Thousandeyes platform.
• Platform: is responsible for collecting, storing, and analyzing performance data.
• Dashboards: provide visualizations of performance data, allowing businesses to quickly identify and troubleshoot performance issues.

ThousandEyes Platform Pricing

ThousandEyes offers a range of pricing plans, depending on the features and services needed. The pricing plans range from basic to enterprise, and the prices vary depending on the number of agents and data collectors needed.

It also offers a free trial for businesses to test the platform. The free trial allows businesses to use the platform for 30 days and access all of the features.

Thousandeyes Platform Integration

ThousandEyes integrates with a range of third-party applications and services, allowing businesses to monitor and troubleshoot network performance in real time. The platform integrates with popular services such as Amazon Web Services (AWS), Microsoft Azure, Rackspace, Google Cloud Platform (GCP), and more.

It also integrates with popular analytics and reporting tools such as Splunk, Grafana, and Kibana. This allows businesses to track performance trends and identify potential issues.

Use Cases

Thousandeyes can be used by businesses of all sizes to measure, monitor, and troubleshoot network performance. The platform can be used to monitor the performance of applications, websites, networks, and cloud services. It can also be used to troubleshoot performance issues and track performance trends.

Thousandeyes can be used by businesses in a variety of industries, including:

• IT and telecom: It can be used by IT and telecom companies to monitor the performance of their networks and ensure reliable and secure user experience.
• Retail: It can be used by retail companies to monitor the performance of their websites and applications, and identify potential performance issues.
• Manufacturing: It can be used by manufacturing companies to monitor the performance of their networks and identify potential performance issues.
• Healthcare: It can be used by healthcare companies to monitor the performance of their networks and ensure reliable and secure user experience.

Comparisons between Thousandeyes and other similar platforms

Thousandeyes is similar to other performance monitoring and troubleshooting platforms, such as AppDynamics, Dynatrace, and New Relic. However, there are some key differences.

• AppDynamics focuses on application performance and provides a comprehensive view of application performance. ThousandEyes, on the other hand, provides a comprehensive view of the entire infrastructure, including public and private networks.
• Dynatrace focuses on cloud performance and provides insights into the performance of cloud services. ThousandEyes, on the other hand, provides insights into the performance of applications, websites, networks, and cloud services.
• New Relic focuses on application performance and provides analytics capabilities. ThousandEyes, on the other hand, provides analytics capabilities, as well as tools for troubleshooting and diagnosing network performance issues.

Services and Support

ThousandEyes provides a range of services and support to help businesses get the most out of the platform. The services and support include:

• Professional services: It provides professional services to help businesses set up and configure the platform.
• Training: It provides training to help businesses learn how to use the platform.
• Support: It provides 24/7 support to help businesses troubleshoot and diagnose network performance issues.
• Documentation: It provides comprehensive documentation and tutorials to help businesses get the most out of the platform.

Conclusion

Cisco ThousandEyes is a comprehensive platform for measuring, monitoring, and troubleshooting network performance. It provides a comprehensive view of the entire infrastructure, including public and private networks. ThousandEyes offers network intelligence, visibility, and analytics, allowing businesses to monitor the user experience and application performance.

It also provides tools for troubleshooting and diagnosing network performance issues, allowing businesses to quickly identify and solve problems. ThousandEyes is easy to use and integrates with a range of third-party applications and services, making it an ideal choice for businesses of all sizes.

Today we look more in detail about next generation firewalls such as Juniper SRX firewall and Fortinet firewalls, how they are different from each other, and their features. 

Juniper SRX Firewall

Juniper SRX is a single appliance having NGFW functionality, unified threat management (UTM) capability, and secure switching and routing. The SRX firewalls provide network wide threat visibility.

• It provides NGFW capabilities such as full packet inspection, appliance aware, UTM.
• It has inbuilt intrusion prevention to understand application behaviour and weaknesses.
• It defends the network from viruses, phishing attacks, malware, and intrusion.
• Adaptive threat intelligence is performed using spotlight secure to consolidate threat feeds from various sources to provide actionable insights into SRX gateway.
• Role of router and firewall into one appliance with switching capabilities.
• Juniper uses Junos Services Redundancy Protocol (JSRP) to enable it to set up two SRX gateways for high availability. 

Fortinet Firewall

Fortinet NGFW works at high speed and inspects encrypted traffic, identifies, isolates, and defuses live threats and protection from threats. Fortinet also provides web filtering, sandboxing, anti-virus, and intrusion prevention system (IPS) capabilities. Performing high speed secure socket layer (SSL) or transport layer (TLS) inspection. Consistent enforcement policies using central policy and device management having zero touch deployments. 

What is common between Juniper SRX firewall and Fortinet Firewall?

• Secure routing where inspection happens to analyze if traffic is legitimate before being forwarded across network 

Comparison: Juniper SRX firewall vs Fortinet Firewall

Download: Juniper SRX firewall vs Fortinet Firewall Comparison table

Continue Reading:

Palo Alto vs Fortinet Firewall: Detailed Comparison

Juniper SRX Firewall vs Palo alto Firewall

Today we look more in detail about comparison – FortiAnalyzer vs Panorama, understand their purpose, capabilities, and key differences.   

What is FortiAnalyzer?

FortiAnalyzer is a centralized network security management solution having logging and reporting capabilities for Fortinet network devices at network security fabric layer. It performs functions such as viewing and filtering individual event logs, security reports generation, event logs management, alerting based on suspicious behaviour, and investigation activity via drill down feature. 

FortiAnalyzer can orchestrate security tools, people, and processes to have streamlined execution, incident analysis and response. It can automate workflows and trigger actions with playbooks, connectors, and event handlers. Response in real time for network security attacks, vulnerabilities, and warnings of compromise suspicion.

What is Panorama?

Palo Alto Panorama is a centralized management platform to have insight into network wide traffic logs and threats. Reduction in complexity by simplification of configuration, management, and deployment of Palo Alto network security devices. Panorama provides a graphical summary of applications on the network, users, and potential security impact.

You can deploy enterprise-wide policies along with local policies to bring in flexibility. Delegation of appropriate levels of administrative control at network device level and role-based access management is available. Central analysis of logs, investigation and reporting on network traffic, security incidents and notifications is available.

Comparison: FortiAnalyzer vs Panorama

Download: FortiAnalyzer vs Panorama Comparison Table

Continue Reading:

Cisco SD-WAN vs Palo Alto Prisma: Detailed Comparison

Fundamentals of FortiGate Firewall: Essential Guide

Are You Preparing For Your Next Interview

If you want to learn more about Palo Alto or Fortigate (Fortinet), then check our e-book on Palo Alto Interview Questions & Answers and Fortinet Interview questions & Answers in easy to understand PDF Format explained with relevant Diagrams (where required) for better ease of understanding.

Firewalls sit on the boundary of the network entry point and provide protection against malicious threats originating from the public net or Internet. A traditional or simple firewall is a stateful filter security device which simply scans incoming packets and rejects or accepts data packets. 

Next generation firewalls (NGFW) are advanced cousins of traditional firewalls, which not just scan data entering into the network but also provide additional features which a traditional firewall will not have. They integrate with other security features such as malware protection, intrusion prevention, URL filtering etc. due to their capability to operate at application layer. 

Unified threat management (UTM) is a well-advanced security system having the capability to unify security features of a traditional firewall, instruction prevention, Anti-malware protection, content filtering and VPN – all delivered from a single platform. 

What is a Firewall

Traditional firewalls operate at layer 3 (network layer) of OSI model and provide IP address, protocol and port number-based filtering services. Firewall is a basic network security device which sits at the network perimeter and provides protection against malicious traffic trying to enter an organization network. It has a basic functionality where a set of rules on firewall determine whether traffic will be accepted, rejected or dropped.

What is a  NGFW

NGFW are the successor of traditional firewalls and designed to handle advanced security threats in addition to features of a traditional firewall by operating at network + application layer (layer 3-7)  of OSI model. Stateful inspection and packet filtering is something it borrowed and carried forwarded along with enhanced capability to filter traffic based on applications and perform deep inspection of packets. 

What is UTM

Unified threat management (UTM) is a comprehensive threat management solution and its need arose due to the expanding threat landscape over the years. As the severity of cyber threats increased the need was felt for a single defense system which under its umbrella manages complete network security including  hardware, virtual and cloud devices and services. UTM devices are placed at key positions in the network to monitor, manage and nullify threats. UTM devices have capabilities of anti-malware, instruction detection and prevention, spam filtering, VPN and URL filtering. 

Comparison: Firewall vs NGFW vs UTM

Download the comparison table: Firewall vs NGFW vs UTM

In networking systems are addressed with a unique numerical value which is known as IP address. IP address is used to locate a system in the networks and basis of communication between systems. However, IP address alone is not enough as it is difficult to remember, each IP address has an associated host name. DNS or domain name systems map this host name to its corresponding IP address. DNS server or service is prone to a variety of cyber attacks DNS rebinding is one such mechanism. 

In today’s topic we will learn about DNS rebinding attack, how rebinding attacks works, Mitigation and preventive measures against DNS rebinding attacks.

DNS Rebinding Attack

DNS rebinding attack leverages the fact that when an exploit such as cross site scripting – XSS happens to compromise the domain the domain name server is also hijacked. In DNS binding attacks the DNS requests go to a specially crafted website by sending requests to name servers of compromised domains rather than the requesting address of a legitimate website. All traffic sent to different IP addresses is relayed back to the web server even if it is not a malicious URL or anything else used commonly during phishing scams and other kinds of attacks which occur online. 

When a DNS rebinding attack happens then there is no control over the nameserver and all requests to resolve hostname are redirected to an alternate nameserver which is under attacker control. Sometimes end users are tricked into creating phishing websites using these websites and all traffic that is redirected to the hijacked URL is sent back to the original server, which forces users to install phishing pages as a result.

DNS rebinding attacks let attackers access sensitive information such as credentials and confidential emails. 

How DNS Rebinding Attack works

The DNS rebinding attack happens to bypass security controls and policies which restrict someone from accessing a network device to which they have no authorization to access over a network. 

1. The attacker creates an A record in DNS for his hostname to point to his internet facing web server. The TTL (time to live) record is set for a very limited time such as a few seconds. 
2. The user visits malicious host name 
3. The attacker changes DNS A record of that hostname to point to its target IP address 
4. The JavaScript component in a malicious website tries to connect to a malicious hostname but since TTL is set with low value, the user system will again make a DNS request to the malicious hostname. This time the IP address is resolved as set by the attacker in step 2. 

The attacker can also create a CNAME record to an internal hostname to rebind their hostname to the internal hostname. DNS rebinding can be used to circumvent the same original policy. Internal websites are more prone to such attacks due to hosting sensitive information. Internal websites usually do not use HTTPS and there won’t be SSL mismatch errors which could hamper the attack. 

DNS rebinding can be used to target web servers or any other network devices. 

Mitigation & Prevention of DNS Rebinding Attacks

DNS pinning is one common technique to prevent these attacks. This makes the browser ignore TTL or DNS records and set itself TTL. This however can be bypassed as well if the attacker implements a firewall in front of the web server. 

Another way to protect web servers from rebinding attacks is configuring the webserver to check HTTP host header in the incoming request. If the host header does not match, the request will be dropped. The firewall can be configured to prevent external host names for resolution of internal IP addresses. 

• Data redundancy (protection against disk failures)
• Throughput/Performance (Fast read and write)
• Storage productivity

In spite of being redundant, robust and proficient in storing important data, there are scenarios where RAID array becomes inaccessible, failure, corrupted, or lost due to other issues. In order to address the corrupted RAID system, there are array of options, depending on type of failure like hardware failure, configuration error or else logical corruption. Some of the solutions capable of resolving RAID related challenges are as under –

Methods used to recover RAID data

1.Reconstruction of Virtual RAID

In this approach, the RAID array original parameters are reference for virtually rebuilding it. Tailor made and Specialized tools like RAID Data Recovery Software is one of the widely used tools in this process.

2.Logical Data Recovery

This process is instrumental in scenarios where disk Array suffers from logical issues and not physical damage. The issues may be deleted volume, corrupted file system or even partition loss.

3.Disk Imaging and Recovering of clone

The methodology Encompasses creating clones in a sequential sector wise fashion for each RAID drive. The actual disk remains untouched, while the recovery is performed on the cloned copies.

4.Hardware-Level Recovery

During the state where we see a physical damage to one or more drives, Hardware-Level recovery is performed. The essential requirement for this approach is to have cleanroom facilities and professional repair tools.

5.Backup Restoration

One of the simpler, secure and straightforward way to restore data is via a recent backup is system.

6.Encrypted RAID Data Recovery

When RAID arrays are encrypted, data recovery is dependent on access to the encryption keys and decryption credentials

Using Stellar Data Recovery Technician to recover RAID data

Stellar Data Recovery Technician is a tailor-made toolkit engineered to handle data recovery across diverse RAID array types, including Single or dual parity like RAID 5, 6, and nested RAID levels like RAID 10, 50, and 60. Another trademark of its trait include compatibility and support for multiple file systems such as NTFS, FAT32, exFAT, EXT4 showcasing its versatility for divergent storage environments.

Key Features

• All-inclusive RAID Recovery: Capability to recover data from both hardware and software RAID arrays, even without a RAID controller card.
• Multi-file system support: Handles NTFS, FAT32, exFAT, EXT4, and XFS file systems.
• Bootable Recovery Media: Permits setup of creation of bootable USB drives to recover data from non-booting systems.
• Recovery from SSD RAID Arrays: Capable of recovering data from SSD-based RAID configurations, addressing issues like controller failure or drive corruption.
• NAS Device recovery: Capability to address Network-Attached Storage (NAS) data recovery for devices configured with RAID.

Stepwise process for RAID 5/10 Data Recovery Using Stellar Data Recovery Technician

Prerequisite

1. Stop the RAID array immediately.
2. Labelling of each device essential to maintain the correct order.
3. Access to each RAID disks individually via a working PC (May leverage SATA/USB adapters).
4. On a dedicated system, Download and install Stellar Data Recovery Technician.

Sequential Recovery Process

Step 1: Launch Stellar Data Recovery Technician

• Open the app and select “RAID Recovery” under “Recover from” options.

Step 2: Select ‘Recover Data from RAID Hard Drives’

• Choose RAID Recovery and Click Next

Step 3: Construct Your RAID (Manual/Auto)

Option A: Automatic RAID Reconstruction

• The software auto-detect RAID parameters (RAID level, stripe/block size, parity rotation).
• Select drives and click Build RAID.

Option B: Manual Configuration (If Auto Fails)

• Enter:
  • RAID type (RAID 5 or RAID 10)
  • Stripe/block size (usually 64KB or 128KB)
  • Parity order and rotation
  • Disk order
• Use trial-and-error combinations if unsure (Stellar helps with RAID parameter guesses).

For RAID 5: It can tolerate 1 disk failure.
For RAID 10: Requires a minimum of 4 disks, and it’s pair-dependent.

Step 4: Scan the Reconstructed RAID Volume

• Stellar creates a virtual RAID volume after RAID reconstruction
• Choose between – Quick Scan and Deep Scan and Click Scan to begin.

Step 5: Preview Recovered Files

• Browse recovered files in a tree view (File Type / Tree View / Deleted List).
• Use the preview feature to validate file contents (especially for images, docs, videos).

Step 6: Save the Recovered Data

• Select the files/folders to restore.
• Click Recover, then:
  • Choose a different storage drive (not on the original RAID).
  • Ensure the target drive has enough space.

Pros

• Intuitive and convenient user Interface: The intuitive interface makes it accessible even to basic users with foundational technical skills.
• Versatile Recovery: Wide range of RAID configurations and file systems supported, enriching its applicability across different scenarios.
• RAID Controller not required: Can recover data without need of original RAID controller, streamlining the recovery process.

Cons

• Performance Unpredictability: Based on complexity of data loss, tool recovery may become time consuming.
• Limited Support for Non-Windows Platforms: Primarily scoped for Windows systems, it has limited usage across other operating environments.

Commercials

Following price options are available for Stellar Data Recovery Technician:

• Technician ( 1 Year) : $199
• Lifetime: $399
• Toolkit: (1Year) : $299
• Lifetime: $599

Performance

During actual and challenging scenarios, Stellar Data Recovery Technician has proved its hallmark of recovering date from intricate situation of RAID failures, corrupted arrays or formatted RAID. The tool involves deep scan features albeit being time consuming is a comprehensive wholesome experience for users and at the same time being qualitative and effective.

Final Verdict

Stellar Data Recovery Technician outshines for its proficiency in recovering amongst myriad of RAID configurations and file systems. Not only is it human centred and effortless, its rich feature support makes it the valuable tool for IT professionals and organizations caught in situations of data loss. While we see that every product including “Stellar Data Recovery Technician” has some scope for improvement (like time intensive during scans, platform support expansion possibility etc.), nonetheless, the software’s strengths make it a compelling choice for RAID data recovery needs.

Intrusion prevention systems or IPS provide security for the networks and hosts within a network. They can detect and block network-based attacks. IPS sensors can be enabled based on IPS signatures, IPS patterns and IPS filters. Many service providers provide separate hardware or software for IPS functionality. However, certain high-end firewall providers bundle IPS capability into their firewall box itself which is actually a complete threat management solution in itself. 

In today’s topic we will learn about how to configure Intrusion prevention (IPS) on a FortiGate firewall. 

What is FortiGate Firewall IPS

FortiGate intrusion prevention is designed to provide real time threat protection for networks. It leverages signature-based behaviour and anomaly-based detection techniques to detect and prevent security threats. FortiGate applies intrusion prevention using a variety of operational modes. All three modes have their own benefits and limitations, which one to choose is based on the placement.  

• L3 (NAT/route mode): In this mode FortiGate places an L3 network where traffic is routed. IP addresses are configured statistically or dynamically on each interface. MAC based policies are applicable for IPS policy source address in NAT route mode.
• Virtual wire mode: In this mode it is deployed between two network segments. It operates like a virtual wire and does not perform routing or NAT. 
• Transparent mode: In this mode it acts like a bridge. All interfaces in the same VDOM are in the same L2 forwarding domain.

Configuring IPS on FortiGate Firewall

To configure IPS on FortiGate firewall 

Step 1

Choose endpoint policy🡪 Infranet Enforcer

Step 2

Click on New Infranet Enforcer and select FortiGate firewall in platform from drop down

Provide name of Intranet Enforcer: ‘FortiGate 12D’ 

Enter FortiGate firewall IP address

Enter shared secret 

Enter port number 

Step 3

Click on Save changes and create policies on FortiGate firewall for enforcement of traffic

FortiGate has IPS sensors which are collections of IPS signatures and filters which define what IPS engine will scan when the sensor is applied. An IPS sensor could have multiple signatures or filters. Custom IPS signatures can also be created to apply to an IPS sensor. 

Step 4

From Security profiles 🡪 Intrusion prevention pane – create new sensor and also view list of predefined sensors. FortiOS has a predefined list of sensors having associated signatures. 

IPS engine does not examine network traffic by default for all signatures. It examines network traffic for signatures mentioned in IPS sensors. You need to create an IPS sensor and specify which IPS signature it is going to use. 

Step 5

To view IPS sensors go to security profiles🡪 intrusion prevention and to create new sensor click on ‘New’

Step 6

Under IPS signatures and filters, click create new to create a set of IPS signatures or set of IPS filters. 

IPS sensors can be created for specific types of traffic. FortiGuard periodically adds predefined signatures to update and counter new threats. These are included automatically in IPS sensors which are configured to use filters when new signatures match with specifications of filters.

Introduction: Embracing Change with Purpose

Knowing how things are done and looking ahead is not enough. It demands emotional intelligence, adaptability, and proactiveness. Many companies worldwide are interested in finding leaders who can face uncertainty and promote extended growth.

To adapt for this challenge, professionals are joining top learning courses like IIM Calcutta Executive Program which give them thorough learning, a global approach and practical experience. Growth-driven leaders realize that before managing change, they should learn about themselves and give their teams focus and certainty.

What is Growth-Driven Leadership?

Leading in this way means emphasizing learning, being innovative, and acting before problems arise. Its purpose is to help create value, foster teamwork, and allow teams to perform well in complicated conditions. This kind of leadership is about thoughtful and fast responses to any kind of change.

Key Traits of Growth-Driven Leaders

1. These leaders set a firm, exciting vision for the future and help everyone understand it so that they can work well together.
2. Growth-driven leaders can still work effectively by changing rather than opposing changes that happen in the business world.
3. Building trust and a resilient team requires leaders who are empathetic, aware of themselves, and skilled at dealing with people.
4. Decision Making: They make good, timely choices by combining facts with their instincts.
5. If you have a Growth Mindset, you believe that challenges can help you learn and improve.

Related: Enterprise Architect vs Technical Architect

Navigating Change with Confidence

All experiences are going to change. Managing and making use of changes is what distinguishes successful organizations. Transforming healthcare is largely dependent on leaders. This is how growth-driven leaders deal with change with confidence:

• Keep Your Ear to the Ground: They monitor advancements, shifts worldwide and new technologies, so they can steer strategy before being affected.
• Include key players: Honesty and cooperation with internal and external stakeholders help ensure unity and support for each stage of the transition.
• Support Innovation: They guarantee safety during the experimentation process so that teams can find creative alternatives.
• Invest in Resilience: Growth-focused leaders put in time to support their teams and strengthen each person’s mental and emotional abilities to change.
• Regular reviews and exchanges of feedback help businesses adjust course and solve problems right away.

Real-World Examples

• Satya Nadella’s leadership changes at Microsoft were centered on growth, cloud computing, and a culture that values diversity, helping to drive the company into new possibilities.
• Kiran Mazumdar-Shaw at Biocon is admired for her skills in growing and diversifying the business while facing new rules and global rivals.

The Role of Continuous Learning

Today, leaders must prioritize learning throughout their lives. Because technologies and business strategies are constantly growing and changing, what we learn today might not matter tomorrow. Leaders need to look for new ideas, tools, and abilities.

Executive education, thoughts shared by respected people, and learning with others can present useful information and help you grow professionally. Advanced programs available at institutions tackle present leadership problems by using the newest and most practical tools.

Challenges to Growth-Driven Leadership

• Growth-driven leadership is not always free from difficulties.
• Old habits can prevent organizations from transforming easily.
• Too much pressure to grow can result in people becoming tired mentally and physically.
• It is usually very difficult to keep the company’s long-term ambitions aligned with the outcomes reported quarterly.

A good way for leaders to tackle these obstacles is to focus on self-care, have strong support, and stay focused on what matters emotionally rather than on instant outcomes.

Strategies for Cultivating Growth-Driven Leadership

1. Regularly think about your strong points, areas of weakness, and places you can improve.
2. Look for and follow the advice of those who have accomplished what you are aiming for.
3. Try out creative ideas, as they could force you to do things differently and challenge your usual approach.
4. Give control to teams and allow them to run their projects.
5. Ensure that every individual, team, and company goal is closely connected.

Conclusion: Leading with Courage and Commitment

Because disruptions in global markets occur more regularly, leadership is being examined more carefully. With growth-driven leadership, managers can handle these changes with ease, certainty, and significance. It’s not only about guiding the ship, you must also support and guide the group, change the direction at times, and stay steady through every bad weather condition.

An executive program or similar courses are designed to prepare leaders with the necessary mindset, tools, and strategies to perform well in changing and complex settings. Whether your organization is huge or just getting off the ground, making growth the main focus of leadership may be what helps you succeed in the future.

Web 3.0’s ultimate aim is to move the Internet from centralized model to decentralized model. Currently all big giants hold control over systems and applications and its data. This also makes software vulnerable to security threats and downtimes if the hosting server fails or gets compromised. Decentralized architecture puts an end to these kinds of issues.

It is just like a piece of open-source software which runs transactions on decentralized computing systems having no single authority. No single entity controls the application and data stored on distributed nodes thus making it more resilient to cyber threats. 

Today we look more in detail about decentralized applications (dApps), what are they?, how they are used, what are their limitations and benefits etc. 

What are Decentralized Applications

When you visit a website to reach or fetch its content at the backend it interacts with a centralized server to get you the desired content. Tech giants such as Meta (Facebook), Amazon, Google etc. feed on customer data and generate revenue. Decentralized applications on the other hand brings the freedom from this centralized control in a few hands. Instead of requests going to a centralized server , requests land up to blockchain for information. dApps are applications only without any central control. 

App = Frontend + Backend → Hosted on Centralized Network Servers

dApps = Frontend + Backend + Smart Contracts → Hosted on Blockchain

Decentralized applications are software programs which run on blockchain or peer-to-peer(P2P) networks of systems instead of on a single system. dApps are outside the control and preview of single authority. dApps are built majorly on Ethereum platform and used for a variety of purposes including gaming, social media, and finance. 

Ethereum network has several applications such as Smart Contracts. Smart contracts allow several parties to agree on conditions that will be coded in the self-executing program. This program automatically executes when coding conditions are met. A smart contract eliminates the dependency and trust on third parties, saves constant attention, time, and cost. Ethereum blockchain is an open-source development platform and environment to build Decentralized Applications (dApps) using Smart Contracts capability.

How dApps work

dApps interact with users on mobile or web browsers like a normal web or mobile application only. Users can connect or log in via wallet also to access the application. The dApp hosts are on the blockchain network and source code is available for verification in the network for each node. The front end of an application is coded in HTML, CSS, JS etc. and the backend is written using JS or Python. dApps can run on P2P networks or blockchain networks. Such as BitTorrent , Tor or Popcorn Time applications run on systems that are in a P2P network and allows multiple users to consume the content, feed and content seeding. 

Pros and Cons of dApps

PROS

• No single party is authorized to control the application actions to maintain decentralization and equality 
• Whole network is decentralized hence there is no single point of failure highly redundant in nature
• User privacy is better guarded as in decentralized application users not required to hand over any personal information 
• Enhance possibility to deploy DeFi, which is decentralized finance, a system to ensure anonymous peer-to-peer financial transactions without the need for a middle party or third parties
• Automation of cumbersome processes such as agreement verification etc. 
• Eliminate risks of data breach and hacking of personal data 

CONS

• Might impact user experience and maintenance as no single party is responsible for upkeep
• Once a smart card is deployed in blockchain it is not possible to alter it
• dApps cloud lead to network congestion due to heavy computation
• Skill gap is a major concern for organizations who wish to switch over to blockchain based applications
• Developers cant alter the code once it is live and available publicly to everyone, some coding flaws and loopholes can be exploited by hackers to gain systems access

Uses of dApps

• Facilitate peer-to-peer financial transactions such as exchange currencies and asset transfers
• Tracking movement of goods in supply chain to ensure transparency and accountability
• Used to securely store and verify identity related information such as voter polls, passport applications, driving license applications etc.
• Facilitate buying and selling real estate directly between buyer and sellers and tracking property ownership and related documentation
• Store and track medical health records and facilitate communication and collaboration between healthcare professionals
• Use to create decentralized platforms for learning , allow user interaction and share content without need for a centralized authority
• Create decentralized platforms for predictive market analytics, let them predict on variety of topics 

Constant threats and vulnerabilities are permanent companions in the IT landscape. Various security solutions have emerged to protect perimeter, digital assets. As the cyber threat landscape is very vast and complex and requires specialized tools and technologies to effectively handle cyber threats and which are constantly evolving to reduce the threat landscape. 

In today’s article we understand the difference between endpoint detection and response (EDR) and Network detection and response (NDR) tools and technologies, their key features, key differences and use cases. 

What is Endpoint Detection and Response (EDR)

Endpoint detection and response tools focus on endpoints as the name suggests. They work on endpoints such as workstations, servers, mobiles, laptops and other mobile assets. They provide real time monitoring, detection and blocking of threats with advanced threat detection capabilities. It can identify malware and other malicious activities on devices and provide rapid incident response. EDR solutions provide threat hunting, malicious activity discovery and its containment to prevent incidents and reduce the attack surface. 

Features of EDR

• Real time visibility into activities happening on endpoints 
• Wide range of threat detection techniques being used such as anomaly detection, heuristics and scans based on threat signatures
• Rapid incident response to isolate suspected endpoints , malicious content blocking and threat remediation with minimal or no impact on operations
• Proactive threat hunting is supported to identify hidden threats and potential vulnerabilities on endpoints 

What is Network Detection and Response (NDR)

Network detection and response or NDR as the name suggests focus is network perimeter / network traffic. Continuous monitoring of network traffic is performed to create a baseline for normal network behaviour patterns. When any pattern outside the baseline is detected then potential threat presence is recorded and notified. NDR tools collect and analyze network data using machine learning techniques to detect potential threats. It detects unusual traffic based on baseline derived by network analysts which might get missed out due to unknown or new signatures. 

Features of NDR

• Capturing network packets and analyzing them for their content for unusual behaviour detection, threat identification with deep packet inspections
• Behaviour analytics to establish normal network traffic baseline
• Continuous monitoring of network traffic for anomaly detection such as unusual high data transfers, multiple login attempts and suspected breach indicated with data flows
• It is integrated with threat intelligence feeds to detect unknown threats from dark web
• Network traffic analysis in real time using machine learning and AI algorithms
• On detection of suspicious activity real time threat alerts are generated 

Comparison: EDR vs NDR

Below table summarizes the differences between the two:

Download the comparison table: Endpoint Detection and Response vs Network Detection and Response

Artificial intelligence is reshaping organizations and redefining the work culture. With Artificial intelligence (AI) emerged two more terms Generative AI and responsible AI. These two terms are closely linked to Artificial intelligence and address different aspects of AI. AI based solutions are deployed in high stake domains such as healthcare, hiring, criminal justice, education etc. which makes it more challenging to address issues related to undue discrimination against minority groups, biases, data manipulation etc. 

In today’s topic we will learn about Responsible AI and Generative AI, key principles of both, key features of both, and key differences. 

What is Responsible AI

Responsible AI refers to ethical and responsible development and use of artificial intelligent systems which emphasize on ensuring use of AI technologies in a way that it aligns to human values, privacy respect, promoting fairness, non-biases and avoidance of negative consequences. 

Ethical considerations are essential while dealing with AI and businesses can promote responsible AI usage with: 

• Establish data governance to ensure data accuracy, preventing bias, and protection of sensitive information 
• Algorithm transparency to foster trust among stakeholders
• Identifying and mitigating ethical risks associated in AI usage such as discrimination and bias
• Human expertise to monitor and validate AI output, alignment to business objectives and meeting regulatory requirements

What is Generative AI

Generative AI systems create any type of new content basis of patterns and existing content. Generative AI can reveal valuable insight but businesses need to be vigilant about bias and misleading outcomes. Generative AI is a subset of AI technologies which are capable of generating new data instances such as text, images, music etc. having resemblance to training data. These technologies leverage patterns learned from larger data sets and create content which is indistinguishable from what is produced by humans. 

Key Technologies in Generative AI

• Generative Adversarial Networks (GANs) involve two neutral networks having the generator and discriminator which compete against each other for generation of new, synthetic data instances which are indistinguishable from what is produced by humans. 
• Variational Autoencoders (VAEs) are meant to compress data into a latent space and reconstruct to allow generation of new data instances by sampling 
• Transformers are meant for natural language processing, and can also be used for generative tasks such as creation of coherent and contextually relevant text or content.

Uses of Generative AI

• Generative AI is used in content creation such as art, music and text 
• Data augmentation and machine models training 
• Modelling and simulation in scientific research 

Comparison: Responsible AI vs Generative AI

Download the comparison table: Responsible AI vs Generative AI

Data Warehouse

The data warehouse is devised to perform the reporting and analysis functions. The warehouse gathers data from varied databases of an organization to carry out data analysis. It is a database where data is gathered, but, is additionally optimized to handle the analytics. The reports drawn from this analysis through a data warehouse helps to land on business decisions.

Data warehouse is an integrated view of all kinds of data drawn from a range of other databases to be scrutinized and examined. It helps to establish the relation between different data that is stored in an organization to further build new business strategies. Analysis or data processing in a warehouse is done by intricate interrogation and questions. It is an Online analytical processing (OLAP) that takes use of standard languages to handle relational data where the data is stored in a tabular form only including rows and columns, indexes, etc. The data stored in a warehouse is applicable to many functions and databases.

The data warehouse is well developed and optimized for amassing and collecting large quantities of data for analyzing it. Data in a warehouse is standardized for boosting the response time for analytical queries and making the data normalized to be used by businessmen. Data analysis and business reporting in a warehouse can be done in many different ways like diagnostic, predictive, descriptive or prescriptive. Since warehouse includes related data all in one place, it uses lesser disk space than databases for those related data. A data warehouse can also store historical data while also real time or current data for handing over most recent information.

Database

Database includes information or data in a tabular form arranged in rows and columns or chronologically indexed data to make access easy. All, whether small or large enterprises require databases to store their information and a database management system that handles and manages the large sets of data stored. For instance, customer information database or product information or inventory database are all different databases for storing information about the customers and products respectively.

The data in a database is stored only for access, storage and data retrieving purposes. There are different kinds of databases available like CSV files, XML files, Excel Spread sheets, etc. Databases are often used for online transaction processing which allows adding, updating or deleting the data from a database by the users. Database makes the task of accessing a specific data very easy and hassle free to carry out other tasks properly. They are like day to day transaction system of data for any organization.

Such transactional databases are not responsible for carrying out analytics or reporting tasks, but, are only optimized for transactional purposes. Database only have a single application of carrying one kind of data in an organized tabular format. Real-time transactions are also applicable in a database which is developed for speedy recording of a new data, e.g. name of a new product category in the product inventory database. Only read and write operation can be carried out in a database and response time is optimized for a few seconds. No analytical task can be initiated in a database as it blocks all other users out of it and slows down the entire performance of a database.

Related – Data Warehousing and Data Mining

Comparison Table: Database vs Data Warehouse

Below table summarizes the differences between Database and Data Warehouse:

BASIS	DATA WAREHOUSE	DATABASE
Definition	A kind of database optimized for gathering information from different sources for analysis and business reporting.	Data storage or collection in an organized manner for storage, updating, accessing and recovering a data.
Data Structure	Denormalized data structure is used for enhanced analytical response time.	Normalized data structure is there in a database in separate tables.
Data timeline	Historical data is stored for analytics while current data can also be used for real-time analysis.	Day to day processing and transaction of data is done in a database.
Optimization	Warehouse is optimized to perform analytical processing on large data through complex queries.	Optimized for speedy updating of data to maximize enhanced data access.
Analysis	Dynamic and quick analysis of data is done.	Transactional function is carried out, though analytic is possible but are difficult to perform due to complexity of normalized data.

Download the difference table: Database vs Datawarehouse

Continue Reading:

Business Intelligence vs Data Warehouse

Top 10 Data Mining Tools

In today’s digital world, Data is wealth, Data is Power and Data is everything. Thus a business should give large importance to Users and their data. 

The Database monitoring tools can help us to a wide number of variables and keep track of the performance metrics of our database or server. Today in this article you will get to know about the top 10 database monitoring tools that every business should have. 

Okay without further ado let’s get started. 

List of Top Database Monitoring Tools

1.SolarWinds Database Performance Analyzer 

It is a database monitor that identifies the problems pinpoint in real-time. 

They offer 14 days free trial and after that, it is available at the price of $1,995. It is suitable for Windows, Linux, Unix, etc… 

PROS:

• Dashboards are highly customizable 
• This database management system is tailored for medium and large-size databases.
• Graphs and alerts in a different color for critical warnings. 

2.DataDog Database Monitoring

It is a SaaS monitoring solution that monitors your cloud infrastructure, applications, and  serverless features. The major advantage of this platform is that it gives a full observability solution with metrics, logs, security, real user, etc…

It gives annual billing and demand billing options. You can also use it free for the first 14 days for an unlimited number of servers. It supports more than 400 Integrations. 

3.OpsView

These database monitoring tools are designed to provide a unified view, which includes both cloud and on premise systems. It operates famous databases like Oracle, IBM DB2, Amazon RDS, etc… 

It offers two types of plans as OpsView Cloud and Enterprise. The former one starts with 150 hosts to 50,000+ hosts and the latter starts with 300 hosts to 50,000+ hosts. 

4.Paessler PRTG Network Monitor

It is a network monitor tool that is compatible with many different databases and can monitor your complete IT Infrastructure. And the interface and dashboards are flexible and customizable. 

It tracks Applications, Cloud Services, Web Services, and other network metrics. You can build your custom configuration or else use the PRTG default ones. 

5.Site 24 x 7

Site 24×7 is a SaaS-based unified cloud monitoring for DevOps and IT operation in both small and large organizations. Site 24×7 is an all-in-one solution that works on Desktop, Linux, Windows and mobile devices.

It is not a specialized tool like the previous one but it is a cloud-based monitoring service, which can help database monitoring. It offers a 30 days free trial and there is also a free version that limits only to five servers. 

6.AppOptics APM

It is also a cloud-based service from SolarWinds, however, there is a lower edition called AppOptics Infrastructure which focuses on Performance and monitoring of databases. 

It has a specialized screen for different application databases and is easily scalable to build as a cloud service. It offers a 14-day free trial. 

7.SentryOne SQL Sentry

It is a database monitoring tool that takes a traditional approach, the user interface is not as attractive as the other products in this list however it gets the job done. 

It is dedicated to SQL, thus it will be a good choice if you have any other monitoring tools, and has more than 100 alerts. It is a little expensive and offers only a 14-day free trial. 

8.ManageEngine Applications Manager

It is an application managing system provided by the managing engine however it works well for database monitoring and server monitoring. It is available with 30-day free trial plans. 

It can map out interdependencies between applications and works both on premise and cloud Infrastructure. 

9.Spiceworks 

If you don’t have any advanced or complex use for your database monitoring tool then Spiceworks will do the job. It is a free tool compatible with SQL Server databases. It is customizable and has simple data visualization.

10.dbWatch

It is a simple and easy-to-install tool. It works well at multiple cross platforms and has a good reporting method. It operates in real-time and historical data. There is also a zoom-in option. 

Conclusion

There are many database monitoring tools out there in the market, and you can choose the one that suits the best as per your requirements. Please share your thoughts and doubts in the comment section below. 

Continue Reading:

Top 10 Serverless Compute Providers

Top 10 Cloud Monitoring Tools

Data storage has been an integral part of the IT ecosystem since the earliest emergence of computer systems in mid-20 century. Initial days data storage was simpler, a basic file storage system housed inside the physical data centers. As technologies evolved the need for more refined methods of managing and information access grew. As the need for flexibility and scalability grew cloud storage took the precedence to handle structured data for analytical purposes and non-structured data such as NoSQL databases to accommodate flexibility required around images and audio files type of data. 

In today’s article we understand and compare the difference between database and data storage, how databases and data storage work? Where to use a database or data storage and understand their key characteristics. 

What is Database

Database is a structured data repository to provide storage, management and retrieval. Databases support various functions such as querying, indexing and transactions handling and are meant for applications which require organized and structured data which is quickly and easily accessible. 

Some examples of databases are relational databases (MySQL, PostgreSQL) which use structured query language (SQL) for data management. Data is organized into tables and has a schema to ensure data integrity and relationships. 

NoSQL databases (MongoDB, Cassandra) handle unstructured data efficiently with flexibility and scalability. MongoDB uses JSON to store documents and Cassandra uses a wide column store model. 

Graph databases (Neo4J, Dgraph) store data as edges and nodes to represent entities and their relationships. Efficient queries with complex relationships and patterns are supported by them. 

Characteristics of a Database

• Efficient management of storage 
• Data integrity with enforced consistency and eliminating data duplication
• Handling large volumes of data 
• Strong security features to support data integrity and protection  

Related: Database and Data Warehouse

What is Data Storage

Data storage is meant for data retrieval and persistence. It is a repository to store, manage and retrieve data. There could be different types of data stores such as databases, file systems, key value stores and object stores. The choice of data storage type is determined by its performance, scalability and data structure. Data can be in structured format and organized into tables or an unstructured format such as NoSQL to handle large scale applications. 

Characteristics of a Data Storage

• A digital repository to store and manage information.
• Datastore can be a network connected storage, distributed cloud storage, or virtual storage
• Can store both structured and unstructured data types 
• Data distribution efficiently with high availability and fault tolerance 

Comparison: Database vs Data Storage

Below table summarizes the difference between the two:

Download the comparison table: Database vs Data Storage

Service providers play a major role in providing different kinds of IT services related to Internet, web, applications, email, advertising, cloud, Security etc. Different service providers provide a variety of services to its clients. For example, an ISP (Internet service provider) sells internet services or data connectivity to its users. OSPs (Online service providers) on the other hand sell online services via the Internet to users such as email, advertising etc. 

Today we look more in detail about the basics of OSP (Online service provider) and ISP (Internet service provider), commonalities and key differences between both of them. 

What is OSP

OSP or Online service provider as they called provide more than just basic Internet service when already connected to Internet. They provide extensive unique web services, email services, advertising services and so on. Services coming from OSPs require ISPs support. OSPs provide its customers with online services like email, websites, discussion forums, downloading files, news articles and chat rooms. The services either provide web access to specialized databases or added general purpose Internet access. 

In addition to providing Internet access, OSPs also provide software packages, email accounts and some personal web space. The OSPs make it easy to communicate with others at every corner of the world, work from home by online services using video telephones and shop sitting at home using online shopping websites. OSPs help organizations to save costs by building and maintaining their websites, building databases, collecting and storing large amounts of information, sharing and data exchange with partners and customers, speeding up operations etc. 

Related: ISP vs VPN

What is ISP

An ISP or Internet service provider as they call it provides Internet and other related services to various enterprises and users. It has a telecommunication network and its associated equipment to offer services across geographies and regions. ISPs provide Internet to its clients along with email, web hosting and domain registration services. ISPs provide Internet connections such as cable, fiber, high speed broadband services. ISPs are connected to a single or more than one high speed leased line which enables them to provide services to its customers. Servers are also maintained by ISPs in their data centers to control all customer traffic.

ISPs are grouped into three tiers namely – 

• Tier 1 ISP which manages most of the traffic on its own as they have physical network lines and have maximum geo reachability. They negotiate other Tier 1 networks to pass traffic through other Tier 1 providers and provide access to tier 2 ISPs.
• Tier 2 ISP connects tier 1 and tier 3 ISPs. They possess regional and national boundaries. Access is procured from tier 1 ISPs but teams up with tier 2 ISPs. Majority of commercial and consumer customers belong to them
• Tier 3 ISPs connect customers to the Internet via other ISP networks. They consume higher tier ISP services which are paid and work to provide Internet to local consumer markets and enterprises.

Comparison: OSP vs ISP

Download the comparison table: OSP vs ISP

So are you new Engineering graduates or experienced professionals who want to enter Facebook? Then here is some information you need to know about the Facebook Technical Hierarchy. 

Okay without further ado let’s start the article with a short introduction to Facebook. 

About Facebook 

As we all know Facebook is a Multinational American Internet and Social Media company. It was formed by Mark Zuckerberg to connect people around the globe. Now it is owned under Meta Platforms. As of 2025 more than 3.07 billion users are monthly active on Facebook. 

It is available in more than 112 languages worldwide. It has 67,317 employees around the globe (as of 2023). Like other, American multinational companies Facebook also uses the grade or band system to classify the Hierarchy of the Employees. 

However, when it comes to the technical field, in Facebook instead of grading them as a senior, fellow, Lead, etc… They are called by their levels. You should first understand this Leveling or banding system to understand how much you can earn as a Software Engineer in Facebook. 

So here are they for your information – 

Technical Hierarchy of the Software Engineers

The software engineers are covered under the E band on Facebook, and they are given a certain level when they enter the company. It shows their seniority and power in the organization. They are as follow –

Software Engineer Level I 

He comes under the band E3. It is the level of position you will be allotted when you’re hired as a fresh graduate. You’re expected to write basic codes and conduct some tests. 

The Average Annual Salary of an E 3 Software Engineer in Facebook is $193907 including cash bonus and Stock Options. 

Software Engineer Level II

These E4 band software engineers are expected to be experienced and considered as senior software engineers in other companies. If you have experience then there is a chance for you to get hired directly for this position. 

The Average Annual Salary of an E4 Software Engineer in Facebook is $307414 including cash bonus and Stock Options. 

Software Engineer Level III

You will be promoted to Level 3 or E5 Software Engineer if you work on Facebook for more than 5 years. This Software Engineer is equal to the team lead or Super Senior software Engineer in other companies. 

The average annual Salary of an E5 Software Engineer in Facebook is $ 471762 including cash bonus and Stock Options. 

Software Engineer Level IV 

The next band is the E6 which is equal to a project lead or Team Lead position in other organizations. This is most likely the position till which you can get hired directly. 

The average annual salary of an E6 Software Engineer in Facebook is $ 756711 including cash bonus and Stock Options. 

Software Engineer Level V 

As a Level 5 Software Engineer, you will be graded in the E7 band and you will be responsible to contact the clients or customers to make adjustments in the Codes and Software. 

The Average Annual Salary of the E7 Software Engineer in Facebook is equal to $1291730 including cash bonus and Stock Options. 

Software Engineer Level VI 

This is the final level, you can be promoted as a software engineer. Hereafter you will be moved to management or other technical parts of the company. It equals the technical director position of the other organizations. 

The Average Annual Salary of this E8 band software Engineer in Facebook is $2187487 including cash bonus and Stock Options. 

From the above points, you can see that all the Software Engineer level shows a promising compensation. So if you want to choose your career path then Facebook is the best choice for you. 

If you have any further questions please leave them in the comment section below. 

***The salary packages are only indicative and may vary as per the rise and low of the demand.***

You can also watch this video for better understanding:

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Technical Hierarchy in Honeywell

Technical Hierarchy: PayPal

Are you planning to get an Information Technology, Engineering, or any other tech-related job at Honeywell? Then you must learn about the different job positions and functionalities of the company. 

Here, in this article, you will get to know about the job level hierarchy and salary and other related information of Honeywell. Okay without further ado let’s start the article with a short introduction to Honeywell. 

Introduction to Honeywell International Incorporation

As a leading Tech multinational corporation, Honeywell focuses mainly on the following four areas: Aerospace, building technologies, performance materials, and safety & productivity solutions. As of 2024, they have more than 102, 000 employees. (ref: https://en.wikipedia.org/wiki/Honeywell)

Honeywell Technical hierarchy 

Here are the details of software engineers’ hierarchy on Honeywell International Incorporation. 

Associate Software Engineer

It is the entry-level position you will be first hired after your graduation. Here you will get exposure to the work environment and organizational methods. 

The Average Annual Salary of an Associate Software Engineer is approx. ₹ 5-7 L/yr and an additional bonus. 

Software Engineer 

After one or two years of working experience, you will be promoted or directly hired as a Software Engineer. From here you will start making a real contribution to the organization. 

The Average Annual Salary of a Software Engineer at Honeywell is ₹ 6 -12 L/year and an additional bonus. 

Senior Software Engineer 

As a senior software engineer, you should have good experience and great problem-solving skills. You will be assigned to difficult and important tasks that need more knowledge and skills. 

The Average Annual Salary of a Senior Software Engineer at Honeywell is ₹ 8 -14 L/year and an additional bonus. 

Tech Lead

He/She will be responsible for all the software engineering work. As a tech lead you should have to help the other engineers when they face problems and evaluate their work. 

The Average Annual Salary of Tech Lead on Honeywell is ₹ 12 -18 L/year and an additional bonus.

Project lead / Technology Specialist

The project lead is responsible for the particular project, he manages the project. His responsibility is the same as the tech lead but focused on a particular project. A technology specialist, instead of managing a group/over checks the outcome and makes adjustments to them. 

The Average Annual Salary of a Project Lead at Honeywell is ₹ 25 – 28 L/yr and an additional bonus. And the Average Annual Salary of a Technology Specialist is ₹ 24 – 28L/yr and an additional bonus.

Engineering Manager

An Engineering Manager is the normal business manager with engineering knowledge and skills. You can choose the business or managing side after the tech lead position. 

The Average Annual Salary of an Engineering Manager at Honeywell is ₹ 35 – 40 L/yr and an additional bonus.

Senior Engineering Manager

A Senior Engineering Manager manages a particular branch or department. The Average Annual Salary of a Senior Engineering Manager of Honeywell is ₹ 45 – 50 L/yr and an additional bonus. 

Staff Engineer 

It is the highest most technical position that involves technical skills. If you choose to stay on the technical side you will be promoted as Staff Engineer after being Senior Software Engineer for enough years. The Average Annual Salary of a Staff Engineer at Honeywell is ₹ 55,02,077 and an additional pay and bonus of ₹ 7,79,392. 

Director

A technical Director controls all the branches or offices in a particular area or country. The Average Annual Salary of a Director of Honeywell is ₹ 70 -75 L/yr and an Additional Pay bonus. 

Senior Director / Vice President / Chief Technology Officer (CTO)

Then based on your achievements you will be promoted to a Senior Director, Vice President, or Chief Technology Officer. They are the final decision-makers of the company. And their pay and benefits are unknown (not clear). 

If you have further questions about the Honeywell organizational Hierarchy please leave them in the comment section below. 

***The salary packages are only indicative and may vary as per the rise and low of the demand.***

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Technical Hierarchy in Juniper Networks

Technical Hierarchy in Arista Networks

Nowadays you often hear buzz words such as artificial intelligence, machine learning and deep learning all related to the assumption that one day machines will think and act like humans. Many people think these words are interchangeable but that does not hold true. One of the popular google search requests goes as follows “are artificial intelligence and machine learning the same thing?”

What is Deep Learning

Deep learning is a subset of machine learning which makes use of neural networks to analyse various factors. Deep learning algorithms use complex multi-layered neural networks where the abstraction level gradually increases by non-linear transformations of data input. To train such neural networks a vast number of parameters have to be considered to ensure the end solution is accurate. Some examples of Deep learning systems are speech recognition systems such as Google Assistant and Amazon Alexa. 

What is Machine Learning (ML)

ML is a subset of artificial intelligence (AI) that focuses on making computers learn without the need to be programmed for certain tasks. To educate machines three components are required – datasets, features, and algorithms.

• Datasets are used to train machines on a special collection of samples. The samples include numbers, images, text, or any other form of data. Creating a good dataset is critical and takes a lot of time and effort. 
• Features are important pieces of data that work as the key to the solution of the specific task. They determine when machines need to pay attention and on what. During the learning process the program learns to get the right solution during supervised learning. In the case of an unsupervised learning machine it will learn to notice patterns by itself.
• Algorithm is a mathematical model mapping method to learn the patterns in datasets. It could be as simple as a decision tree, linear regression. 

Artificial Intelligence (AI)

AI is like a discipline such as Maths or Biology. It is the study of ways to build intelligent programs and machines which can solve problems , think like humans, and make decisions on their own. Artificial intelligence is expected to be a $3 billion industry by year 2024. When artificial intelligence and human capabilities are combined, they provide reasoning capability which is always thought as human prerogative.  The AI term was coined in 1956 at a computer science conference in Dartmouth. AI was described as an attempt to model how the human brain works and based on this know-how creating more advanced computers. 

Comparison: Deep Learning vs Machine Learning vs AI

Download the comparison table: Deep Learning vs Machine Learning vs AI

In today’s topic we will learn about different types of DNS attacks and measures to mitigate them. 

What are DNS Attacks?

DNS attacks have been on the rise for quite some time. In 2024 DNS Filter report showing phishing attacks went up to 106% and as these attacks are getting worse, enterprises and individuals need to take these DNS attacks more seriously as they lead to data loss, ransom demand, and damaged reputation. In DNS attack hackers exploit DNS weaknesses such as 

• Traffic redirection to malicious websites changing DNS records 
• Overwhelm DNS servers with too many requests in short span of time to cause service disruptions
• Tick users in visiting fake websites to steal credentials, passwords etc.

Types of DNS Attacks

DNS Cache Poisoning (DNS Spoofing)

Users are redirected to malicious websites by manipulating the DNS cache of the DNS resolver by the attacker. Attackers exploit vulnerabilities in DNS software or intercept DNS queries and inject false DNS records into the DNS cache database. The legitimate domain names are mapped with malicious IP addresses to redirect users to fictitious websites.

DNS spoofing leads users to unknown websites which result in phishing attacks, malware distribution or sensitive information theft. Implementation of DNSSEC (Domain name system security extensions) help in authenticating DNS data to prevent tampering. Configuring secure DNS resolver settings, regular monitoring and updated DNS cache contents, deploy intrusion detection systems to detect and block malicious spoof traffic. 

DNS Amplification

DNS Amplification exploits open DNS servers which generate a large volume of traffic which is redirected to the target. Small DNS requests are sent by attackers to open DNS servers having spoofed source IP address which belong to the victim. DNS server responds with larger responses with amplified volume of traffic directed to the targeted network. Overwhelming of network bandwidth occurs in this case.

To mitigate these attacks ingress filtering is an effective option to mitigate IP address spoofing. Configuring DNS servers to put limits on query response, and traffic scrubbing solutions which filter malicious DNS traffic. Maintaining up to date DNS server configurations and monitoring of DNS traffic for anomalous patterns. 

DNS Tunnelling

This technique is used by attackers to bypass network security controls using encapsulation of unauthorized data in DNS query and response. Attackers launch exploits to establish covert communication channels between external servers and victim systems, enable data exfiltration, control /command, propagation of malware which remain undetected.

Anomalous patterns are analyzed by DNS traffic monitoring. Enforce query size/response limits, intrusion detection and prevention systems implementation to detect and block suspicious traffic, DNS firewall solutions and DNS traffic inspection for any signs of tunnelling activity. 

Distributed Denial of Service (DDoS) Attack

DDoS attacks overwhelm DNS servers with flooding malicious traffic making them inaccessible and disrupting DNS resolution services. Exploit vulnerabilities in DNS and abuse misconfigurations in DNS servers, botnets to generate DNS queries in high volumes which lead to service degradation leading to its unavailability.

Mitigation techniques involve deploying DDoS mitigation software to detect and mitigate volumetric attacks. Distribution of query loads using distributed DNS infrastructure absorbs DNS traffic attacks. Implement network traffic filtering in collaboration with internet service providers (ISPs) and rate limiting feature to maintain redundancy and failover for continued services availability during DNS attacks. 

NXdomain Attack

NXdomain attack focus is DNS servers. Fake requests for websites which do not exist are sent by hackers to flood servers. Server time is wasted and eventually resources are overwhelmed and stopped working as people can’t access actual websites. Implementing rate limiting technologies in collaboration with internet service providers, restricting number of requests to DNS resolvers for single IP address source reduces load on servers and prevents them from getting overwhelmed. 

Comparison Table

Below table summarizes the difference between the 5 types of DNS attacks:

Download the comparison table: DNS Attack Types Compared

In today’s topic we will learn about the zero trust architecture approach, its need, how zero trust security is achieved and its benefits. 

What is  Zero Trust Architecture (ZTA)

Zero trust architecture’s basic principle is ‘Never trust, always verify’ which focuses on stringent access controls and user authentication. It helps organizations to improve their cyber defenses and reduce network complexity. Pre-authorized user access concept no longer exists in zero trust architecture.

Due to cloud computing penetration and diminishing physical boundaries and network complexity of enterprises is increased. Implementing several layers of security is tough to manage and maintain. Traditional perimeter-based security is no longer adequate. Zero trust architecture helps organizations build policy-based access which are meant to prevent lateral movement across networks with more stringent access  controls. User policies can be defined based on location, device and role requirement. 

How Zero Trust works

Zero trust works by combination of encryption, access control, next generation endpoints security, identity protection and cloud workloads advantages. Below set principles are the basis for NIST zero trust architecture as under:

• Access to resources is managed at organization policies level considering several factors such as user, IP address of user, operating system and location.
• Corporate network or resource access is based on with secure authentication for every individual request 
• User or device authentication do not automatically provide resources access
• All communication is encrypted and authenticated 
• Servers, endpoints and mobile devices are secured with zero trust principals which together are considered corporate resources 

How to implement Zero Trust Architecture?

The very first step is to define the attack surface which means identify what you need to protect which areas? Based on this you need to deploy policies and tools across the network. The focus should be protection of your digital assets.

Define Attack Surface 

• Sensitive data – the organization collects and stores what kind of sensitive data such as employees and customers personal information 
• Critical applications – used by business to tun its operations or meant for customers 
• Physical assets – IoT devices, POS devices any other equipment
• Corporate services – all internal infrastructure meant to provide day to day operations  

Implement controls around network traffic 

The routing of requests within the network for example access to a corporate database which could be critical to business so as to ensure access is secure. Network architecture understanding will help to implement network controls relevant to its placement.

Create a Zero-Trust Policy 

Use the Kipling method here to define the zero-trust policy : who, what , when , where , why and how need to be well thought out for every device, user. 

• Architect a zero-trust network 
• Use a firewall to implement segmentation within the network. 
• Use multi-factor authentication to secure users 
• Eliminate implicit trust 
• Consider all components of organization infrastructure in zero-trust implementation scope such as workstations, servers, mobile devices, IoT devices, supply chain , cloud etc.

Monitor the Network 

Once a network is secured using zero trust architecture it is important to monitor it. 

Reports, analytics and logs are three major components of monitoring. Reports are used to analyze data related to system and users and could be an indication of anomalous behaviour. Data collected by systems can be used to gain insight into behaviour and performance of users. Logs produced by different devices in your network provide a record of all kinds of activities. These can be analyzed using the SIEM tool to detect anomalies and patterns. 

These tools are designed to streamline the compliance requirements, automate risk management and help businesses in managing their risk appetite more efficiently with desired operational efficiency. 

In today’s topic we will learn about top 10 risk and compliance tools available in the market in the year 2025. 

PDF Download – 10 Best Risk and Compliance Tools Cheat Sheet

List of Best Risk and Compliance Tools

1. ServiceNow GRC

It is widely recognized having robust automation capabilities meant for large organizations. 

Key features of ServiceNow GRC:

• Integration with risk analytics 
• One cohesive system for compliance tracking and operational 
• Predictive analytics and vendor risk management 
• Comprehensive policy management suite 
• Automation of audit processes to reduce manual efforts 

2. IBM Open Pages

IBM Open Pages leverages artificial intelligence power of IBM Watson and meant for large enterprises in regulatory space such as Finance, healthcare and manufacturing industries offering scalability and extensive customization to support various regulatory and compliance requirements. 

Key features of IBM Open Pages: 

• Centralized risk management with automated compliance workflows 
• Having audit management software inbuilt 

3. Hyper Proof

This a cloud based risk and compliance tool to simplify compliance management with automation and real time monitoring capabilities. Information technology and software development industries are key users of this to help organizations to manage various compliance frameworks in seamless manner. 

Key features of Hyper Proof: 

• Automatic evidence collections
• Streamlined audits 
• Adherence to regulatory standards , mapping of controls to reduce compliance risks and improve operational efficiency 
• Mapping control to multiple requirements hence Scale up information security compliance program
• Supports 60+ compliance frameworks out-of-the-box 

4. ZenGRC

ZenGRC is a user friendly cloud based GRC tool it is meant for mid-size organizations in technology and financial domain to maintain high compliance standards and get rid of manual oversight. 

Key features of ZenGRC:

• Centralized risk management activities
• Continuous monitoring capabilities 
• Simplified audits 
• Compliance tracking across regulatory frameworks 

5. Riskonnect

Riskonnect is a GRC solution ideal for healthcare, financial sector.  

Key features of Riskonnect:  

• Integrated risk management, compliance tracking and audit management in unified system
• Workflows based automation 
• Analytics dashboards to provide insight into risks and risk landscape in real-time 

6. Sprinto

This tool is ideal for IT companies and software firms which need a streamline approach towards compliance management. 

Key features of Sprinto: 

• Automation of compliance processes
• Continuous adherence to regulatory standards and frameworks 
• Centralized evidence management 

7. Workiva

Workiva is a cloud based GRC tool used by banking, insurance and education sector primarily. 

Key features of Workiva: 

• Cloud based reporting
• Automation of manual tasks related to Sarbanes Oxley (SOX) compliance 
• Audit management 

8. Ncontracts

Tailored for banks and credit institutions risk and compliance management requirements. 

Key features of Ncontracts:

• Integrated risk management 
• Robust tool for vendor risk management , compliance tracking and audit management 
• Custom risk management modules 
• Monitoring of regulatory changes 
• Business continuity with configurable analytics capabilities 

9. Diligent HighBond

It is a comprehensive GRC tool for risk and compliance management. 

Key features of Diligent HighBond: 

• Automation of decision making 
• Centralized platform for risk management, audit workflows and compliance tracking 
• Risk time risk reporting 
• Compliance tasks automation 

10. LogicManager

LogicManager is widely used in retail, healthcare and manufacturing sectors. 

Key features of LogicManager:

• Comprehensive reporting tools 
• Platforms incident management 
• Automated task tracking features 
• Simplification of policy management and audit processes 

In today’s topic we will learn and compare Storage area network (SAN) and Hyper Converged Infrastructures (HCI) technologies, their key differences and understand their working. 

What is SAN? 

SAN is designed to connect storage devices within enterprises, data centers, offices or any other environments. In SAN storage, all storage devices, routers, switches and other networking technologies are connected to a single network fabric. Protocols such as Ethernet and Fiber channel are used to enable network nodes or connection points such as switches for communication. 

Host bus adapter (HBA) connects host servers to the SAN to provide interfacing to storage devices or network devices within the network. The purpose of this is to pool storage resources concurrently. Regardless of the physical location one can access servers, flash arrays, and other storage devices in the network. SANs are useful for large organizations which need to connect to storage resources – especially critical applications in big enterprises. They are good candidates for customizing storage network as per organization needs. 

Advantages of SAN

• Provides automatic backup
• It provides high data storage capacities
• Reduction in cost of deploying multiple servers
• Improved performance
• Improved backup and recovery options 

What is HCI?

Hyper Converged infrastructure (HCI) is a combination of products – storage, compute and networking. The HCI platform includes both hardware and software components. These can be commodity hardware or vendor specific hardware. All HCI components are virtual – separated from the underlying physical layer of compute, storage and networking which are managed by a hypervisor or software which runs a virtual machine. Everything on the HCI platform comes from one vendor due to which traditional HCI systems are prone to vendor lock-in. 

Advantages of HCI

• Ease of scaling, in and out based on business needs extra nodes can be added
• Improves business agility in enterprises 
• Improved data protection and disaster recovery with each node in pool contributes to a reliable and redundant shared storage
• Supports hybrid cloud environment 
• Supports faster deployment 
• Reduction in footprint as all components of data center are combined into a single appliance 

SAN vs HCI

Download the comparison table: san vs hci

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Hyper Converged Infrastructure (HCI) vs Converged Infrastructure (CI)

Difference between File Level Storage and Block Level Storage

Every organization, be it small, medium or large are impacted by third party risks. This risk is exponentially increased as more and more providers are building and using AI technologies in their products which resulted in apart from security but privacy concerns also. 

In today’s topic we will learn about top 10 TPRM Tools (third party risk management tools) available in the market.

List of TPRM Tools

Upguard 

Upguard has seven key features to detect threats at multiple levels. It covers security risks associated with Internet facing third party assets. Auto detection happens using third- and fourth-party mapping techniques. 

Key features of Upguard 

• Evidence gathering involves combining risk information from multiple sources to get complete risk profile
• Monitoring third party attack surfaces via automated scan 
• Third parties trust and security pages to showcase information about their data privacy standards, certifications, cybersecurity programs 
• Elaborate security questionnaires to assess risk posture of third party
• Third party baseline security posture 
• Vulnerability model of third party 

SecurityScore card 

SecurityScore card detects security risks associated with third party vendors.

Key features of SecurityScore

• Detection of security risks associated with internal and third-party attack surface mapped to NIST 800-171 
• Projected impact of remediation tasks and board summary reports 
• Third parties risk management via Atlas to manage security questionnaires and calculate third-party risk profiles 
• Third-party monitoring via security score feature and track performance 

Bitsight

Bitsight multiple third-party risk identification techniques work together to present a comprehensive risk profile from third-party exposure. 

Key features of Bitsight 

• Automatic identification of risks associated with alignment gaps with regulations and cyber frameworks such as NIS 2 and SOC 2 
• Track third-party cybersecurity performance using security ratings
• Monitor emerging cyber threats across cloud, geographies, subsidiaries and remote workers
• Multiple threat sources are used to create a risk profile

OneTrust

OneTrust identifies risks across onboarding and offboarding phases of third-party vendors.

Key features of OneTrust 

• Predictive capabilities to gather insights about privacy and security , governance risks 
• Maintain updated vendor inventory but workflow automation across vendor onboarding / offboarding
• AI engine (Athena) to expedite internal and third-party vendor risk discovery 

Prevalent

Prevalent point in time risk assessments with automated workflows to monitor third-parties and track emerging risks in real time. 

Key features of Prevalent 

• Impact of third-party risks on organization and security ratings from 0-100
• Point in time risk assessments with continuous monitoring capabilities
• Identification of common data leak sources, dark web forums and threat intelligence feeds 

Panorays

Remain informed of third-party risks with built-in risk assessment workflow for risk assessment creation quickly. But it does not support threat and risk intelligence into supply chain data. 

Key features of Panorays

• Detection of common data breach vectors
• Library of questionnaire templates mapped to popular standards and frameworks
• Combining data from security ratings and questionnaires to support third-party risk attack surface
• Workflows customization with external applications using JSON based REST API 

RiskRecon

Third-party risk exposure assessments with deep reporting and security ratings. 

Key features of RiskRecon 

• Uses risk analysis methodology having 11 security domains and 41 security criteria to get contextualized insight into third-party security posture
• Security rating scoring system 0-100 
• Standard API to create extensive cybersecurity ratings  

CyberGRX

Expediting third-party risk discovery during vendor due diligence. More frequent risk assessments are supported coupling third-party risk data streams.

Key features of CyberGRX

• Security questionnaires to establish vendor security posture
• Continuous updates to library of point in time assessments to map current risks to threat landscape
• Monitor emerging risks related to phishing, email spoofing, domain hijacking, and DNS issues

Vanta

Focuses on detection of risks associated with misalignment to frameworks and standards. 

Key features of Vanta 

• Intuitive dashboard to monitor third-party risks related to compliance and track their progress
• Alignment tracking with security frameworks and standards such as SOC 2, ISO 27001, GDPR and HIPAA.

Drata

Full audit readiness assessment by security tools monitoring and compliance workflows to streamline operations 

Key features of Drata 

• Policy builder to map specific compliance requirement for third-party risk analysis
• Maintain compliance across 14 cybersecurity frameworks
• Continuous monitoring of compliance controls 

Today we look more in detail about two important terms, widely used data science and artificial intelligence and understand the difference between them, the purpose for which they are deployed and how they work etc.

What is Data Science?

Data science is the analysis and study of data. Data science is instrumental in bringing the 4th industrial revolution in the world today. This has resulted in data explosion and growing need for industries to rely on data to make informed decisions. Data science involves various fields like statistics, mathematics, and programming.

Data science involves various steps and procedures such as data extraction, manipulation, visualization and maintenance of data for forecasting future events occurrence. Industries require data scientists which help them to make informed decisions which are data driven. They help product development teams to tailor their products which appeal to customers by analysing their behaviours.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a broad field and quite modern. However, some ideas do exist in older times and the discipline was born a way back in 1956 in a workshop at Dartmouth College. It is presented in contact with intelligence displayed by humans, and other animals. Artificial intelligence is modelled after natural intelligence and talks about intelligent systems. It makes use of algorithms to perform autonomous decisions and actions.

Traditional AI systems are goal driven however contemporary AI algorithms like deep learning understand the patterns and locate the goal embedded in data. It also makes use of several software engineering principles to develop solutions to existing problems. Major technology giants like Google, Amazon and Facebook are leveraging AI to develop autonomous systems using neural networks which are modelled after human neurons which learn over time and execute actions.

Comparison Table: Data Science vs Artificial Intelligence

Below table summarizes the differences between the two terms:

Parameter	Data Science	Artificial Intelligence
Definition	Comprehensive process which comprises of pre-processing, analysis, visualization and prediction It is a discipline which performs analysis of data	Implementation of a predictive model used in forecasting future events It is a tool which helps in creating better products and impart them with autonomy
Techniques	Various statistical techniques are used here	This is based on computer algorithms
Tools size	The tools subset is quite large	AI used a limited tool set
Purpose	Finding hidden patterns in data Building models which use statistical insights	Imparting autonomy to data model Building models that emulate cognitive ability and human like understanding
Processing	Not so much processing requirement	High degree of scientific processing requirements
Applicability	Applicable to wide range of business problems and issues	Applicable to replace humans in specific tasks and workflows only
Tools used	Python and R	TensorFlow, Kaffee, Scikit-learn

Download the comparison table: Data Science vs Artificial Intelligence

Where to use Data Science?

Data science should be used when:

• Identification of patterns and trends required
• Requirement for statistical insight
• Need for exploratory data analysis
• Requirement of fast mathematical processing
• Use of predictive analytics required

Where to use Artificial Intelligence?

Artificial intelligence should be used when:

• Precision is the requirement
• Fast decision making is needed
• Logical decision making without emotional intelligence is needed
• Repetitive tasks are required
• Need to perform risk analysis

Continue Reading:

Artificial Intelligence vs Machine Learning

Top 10 Networking technology trends 

Automation has been among humans for a long time though Artificial Intelligence has been developed in recent years. In this article, we are going to see the difference between these two. Yes, though we consider both as the robots or machines that work on their own there is a pretty big difference between them.

So without ado let’s get started with the an introduction to automation and AI before discussing Automation vs Artificial Intelligence.

What is Automation?

Automation refers to a technique or process that makes a machine or system operate on its own or with minimum human inputs. Implementing automation in a process improves efficiency, reduces cost, and gives more reliability.

The history of automation starts from mechanization which is connected to the great industrial revolution. Now automation is everywhere in the modern economy.

Examples of Automation

The examples of automation are:

• Automatic payment system in your banks,
• automatic lights, and
• even automatic or self-driving cars.

To explain it technically, automation is software that acts according to the way it is pre-programmed to act in a given situation. For example, let’s take the example of copy-pasting or moving data from one place to another. Moving data from one place to another can be a tedious repetitive task for humans, but automation software makes it simple.

All you need to do is program the computer or machines how to transfer files from, and when to do it. After that, the machine itself will transfer or move files automatically from one place to another. In this way, automation saves both money and time spent on these monotonous, large tasks. The employees and human resources can be used in something more creative.

What is Artificial Intelligence?

Artificial Intelligence is the further advanced form of automation, where the machines or mostly systems mimic human thinking and make decisions of their own. AI is software that simulates human thinking and processing in machines.

Artificial Intelligence is achieved by combining various automation technologies like data analysis, data prediction, etc… In Artificial Intelligence you don’t need to write any program for a particular process…all you need to do is give the past data to the system, it will analyze the decisions made in the past and make decisions for the current problem like a human being.

As automation can only be applied for repetitive tasks, artificial intelligence has been invented to do more variable processes where there is a need for human decisions. It learns from experience and involves self-correction to give a proper solution to a problem.

Examples of Artificial Intelligence

Good examples of Artificial Intelligence are

• Chatbots,
• Digital assistants,
• Social media recommendations,
• Text or grammar editors,
• Facial detection,
• maps,
• navigation, etc…

Let’s explain it with maps and navigation, Google maps show you the quickest way to go to a place. As it is not a repetitive process the navigation software should adopt artificial intelligence and guide users in a way an ordinary human would do.

Comparison Table: Automation vs Artificial Intelligence

Now as you got the basic idea about what automation and artificial intelligence is let’s see the major difference between them i.e. Automation vs Artificial Intelligence:

Continue Reading:

RPA – Robotic Process Automation

What is AIML (Artificial Intelligence Markup Language)