Tag: cybersecurity

  • AI-powered Zero Trust Networks for Cybersecurity

    Let’s secure valuable data in a world where hackers try to break IT networks everyday. Artificial Intelligence or AI-powered Zero Trust  IT networks is today’s leading-edge technology for advanced cybersecurity. Combining zero trust network architecture with AI has multiplied its abilities to secure data. Let’s break it down, see how AI-driven zero trust networks fit in today’s cybersecurity landscape, and some practical aspects and tips about it. 

    In this Article

    What are Zero Trust IT Networks?

    First let’s look at what zero trust IT networks are. Zero-trust is a network architecture for cybersecurity. As the name suggests, it has zero trust in anyone whether inside or outside the network. Therefore, such networks enforce strict identity verifications and strong access controls on every user, device and app trying data access from them.

    For instance, imagine your office IT network as a castle. The castle has a gate keeper- firewall– to keep out bad guys like malware. But, if someone (even a gatecrasher) manages to pass through the firewall, traditional IT networks blindly trust them as legit.
    In contrast, zero trust network architecture flips that assumption. It works on the core principle: ‘Trust Nobody, whether they are inside or outside the network; Always Verify’. Every network user and device has to prove their legitimacy whenever asked. 
    Typically, zero trust networks check the following:

    1. Who’s trying to get in (user identity)?

    • Identity verification:
      A zero trust network verifies every request for network access via strong identity signals. E.g: Multi-Factor Authentication(MFA), Single Sign-On (SSO), Identity Providers (IdPs).
    • Role-Based Access Control (RBAC):
      RBAC grants network access based on user roles, not blanket permissions. Even admins may get only scoped access (E.g.- read-only access, permission to manage only specific parts of a system).
    • Continuous identity assurance:
      Zero trust network verifies identity not just once, but continuously. It conducts ongoing behavioral analysis, and session risk scoring. E.g.- User and Entity Behavior Analytics (UEBA) 

    2. Is their device safe (no malware or sketchy apps)?

    • Endpoint compliance checks:
      Devices must meet security baselines like OS version, antivirus status, disk encryption.
    • Mobile Device Management(MDM):
      Tools like Intune, Jamf enforce security policies, and isolate non-compliant devices.
    • Device identity & health attestation:
      Zero trust networks first register, and then monitor connecting devices. It can deny access, or quarantine unmanaged or jailbroken devices.

    3. Are they normal, or acting weird?

    • Anomaly detection:
      Artificial intelligence/machine learning (AI/ML) models detect strange behaviors like data access at odd hours, or from unusual locations.
    • Risk adaptive access:
      Zero trust network can adjust its decisions based on real-time risk signals. E.g.-If someone tries network access from a TOR (The Onion Router) exit node, it can block or challenge it with extra verification.
    • Session monitoring:
      After allowing entry, zero trust networks monitor and record users’ session activities. Suspicious behaviors trigger auto-logout, or privilege revocation.

    On a funny note, zero trust network architecture is like a bouncer at a club. Before letting people in, he thoroughly checks their IDs and bags. Then he watches everyone inside and outside the club continuously. If anyone acts weird, they have to reverify, or they are out! Zero trust. Period.

    AI-Powered Zero Trust Networks

    So far, we discussed zero trust IT networks. Now add Artificial Intelligence (AI) to them. This combo is a game changer in cybersecurity. It can process tons of data super fast, and spot patterns a human might miss.
    In the earlier story, an AI-integrated zero trust network is like this: The bouncer gets X-ray vision plus a smarter brain. He can now sense trouble even before it really starts.
    Let’s now explore the smarter ways of AI-integrated zero trust networks:

    • Spot weird behavior in real-time:
      AI does ongoing behavior analysis of people and devices in the network. E.g.- An employee usually logs in at 9AM from Brooklyn. Suddenly, there’s a login attempt at 3AM from Sydney. AI finds that weird. It either blocks it, or asks for extra identity proof(e.g.- text code via SMS).
    • Catch sneaky threats:
      Hackers keep trying new threats to break IT networks. E.g. Zero-day attacks(using bugs that nobody knew about yet). AI can spot such foul plays by observing patterns in data traffic, even if it has never seen a specific attack before.
    • Cut false alarms:
      At times, antivirus freaks out over something harmless. Here AI learns what’s normal in an IT network, prevents false alerts, and focuses on real threats.
    • Automatic troubleshooting:
      If AI spots trouble, it automatically locks out a suspicious device, blocks shady IP address, or quarantines the affected network part. 

    Why use AI-driven Zero Trust Networks?

    Today, people work from home, bring their own devices for work, and companies often use different cloud storage platforms to store business data. As our digital connections grow, chances of cyber attacks also go up. AI-integrated zero trust IT networks is an ideal solution here.

    Irrespective of where a user or device is logging in from, AI implements cybersecurity measures and non-stop monitoring with zero trust. In hybrid cloud setups (where some data is on local servers, say ERP systems, and the rest in cloud platforms like Google Cloud) also AI maintains strong data security without network slowdown.
    Plus, AI is getting smarter at things like:

    • Real-time alerting on weird network activities.
    • Predictive maintenance where AI predicts what might go wrong.
    • Integration with other security tools like SIEM, SASE.

    How AI-driven Zero Trust Networks Operate

    Let’s consider an example to understand this smarter version of zero trust networks. You own a business where employees work at the office, and from home. So you keep your business data on a local server, and on cloud platform Microsoft 365. Here are some ways an AI integrated zero trust IT network secures your critical business data:

    1. Spot strange login attempts:
      If someone steals an employee’s work laptop and tries to log in, AI notices that the login is from a strange location. Then it asks for credentials (e.g.- Asking for code sent to the employee’s phone). If the verification fails, AI automatically locks the account and alerts your IT team.
    2. Sneaky malware:
      A hacker sends a phishing email containing malware. AI spots unusual data transfer(e.g.- customer database download at 2 AM). It swiftly blocks the file transfer, isolates the infected device, and alerts your IT team.
    3. Cloud protection:
      About that data on Microsoft 365, AI monitors all your API calls (that’s how your apps ‘talk’ to each other). If someone tries to access files they shouldn’t, AI blocks the attempt and logs it for review by your IT team.

    Before setting up AI-powered Zero Trust Network 

    Here are some practical tips for you when planning to add AI-driven zero trust to your IT setup:

    • Assess your need:
      First analyze your existing IT network. Assess things like the number, and types of devices, and apps you use now. This helps in choosing best fitting tools for AI integration with the network.
    • Start small:
      In fact, you don’t need to secure all of your data at once. Start by protecting your critical data and sensitive data like financial records.
    • Check compatibility:
      Ensure the AI tool is compatible with your existing systems like firewall, cloud apps.
    • Train your team:
      Explain to your employees why go for AI driven zero-trust. Take them on board so that everyone is digitally safe.
    • Keep your eye on AI:
      AI is great, but you still need humans to cross-verify AI alerts. Also to scale up the network as your business grows.

    How to set up AI-Powered Zero Trust Network

    Here is a guide to integrate AI with zero trust IT networks:

    1. Establish zero trust foundations–

      Implement the principles of zero-trust architecture: never trust, always verify, least-privileged access, and assume breach. Segment the network and enforce identity-based access controls.

    2. Deploy AI-powered behavioral analytics–

      Use AI to monitor user and device behavior across endpoints, apps, and networks. Compare user activities against established baselines in real-time to detect anomalies.

    3. Automate threat detection and response–

      Integrate AI with incident response playbooks. That helps to isolate compromised devices, suspend access and trigger alerts automatically.

    4. Improve access controls–

      Apply adaptive network access using AI-driven risk analysis. Models like Just-In-Time (JIT), Just-Enough-Access (JEA) helps minimize exposure.

    5. Secure AI workloads and data–

      Protect training data and model outputs with zero trust policies. Ensure data integrity and prevent unauthorized use of the AI systems.

    6. Monitor and refine continuously–

      Using AI, audit access logs, refine policies, adapt to newer threats and give feedback to the system for ongoing improvement.

    Tools to integrate AI with Zero Trust Network

    • Palo Alto Networks (Prisma Access): For cloud, and hybrid setups.
    • CrowdStrike Falcon: For endpoints like laptops or phones.
    • Zscaler: For cloud-based setups.
    • Microsoft Defender for Identity: Ties into Microsoft 365.

    Each of these tools has its own strengths. Select one that best fits your business needs.

    Final Thoughts

    AI-powered zero trust IT networks is the cutting-edge tech for cybersecurity of your business. It not just prevents cyber attacks, but ensures only the right people enter your IT network. It doesn’t matter where they are, or what device or app they use to attack.
    With AI on your side, you can catch data threats super fast, avoiding false alarms. Keep your business safe and seamless.

    If you’re thinking of upgrading data security, an AI-driven zero trust network is a great next step. If you have questions, or are already using it, drop your comments below.

  • Data Loss Prevention (DLP): Securing Critical Business Data

    Data Loss Prevention (DLP): Securing Critical Business Data

    In today’s digital world, data is a company’s most valuable asset. All businesses have confidential data like project details, financial and client information etc. Losing critical data can lead to financial losses, reputational damage and legal penalties for businesses. Data Loss Prevention (DLP) is a concept of preventing data loss through external or internal sources. Let’s explore.

    In this article

    Importance of DLP measures in a business

    Companies use DLP (Data Loss Prevention) measures to safeguard their confidential and sensitive information. It ensures critical data is only accessible to authorized users. DLP is a proactive step towards building resilient and trustworthy businesses in the following ways:

    1. Prevents unintentional data leaks-

    One main reason for data loss is human errors. DLP reduces accidental data leaks by employees by employing context-aware controls and continuous data monitoring. With accurate detection, real-time user coaching and adaptive reaction, unintentional data losses can drop significantly.

    2. Reduces intentional data breaches–

    Intentional breaches by inside or outside players are another reason for business data loss. DLP can reduce malevolent data breaches by spotting irregularities and then stopping data exfiltration.
    Example- If a data movement occurs on a public holiday, a sensitive folder like .csv gets accessed screenshots will be taken. The system then classifies these changes and prevents them from happening.

    3. Unified, intelligent & proactive operations

    DLP isn’t just about preventing data loss. It actively contributes to smoother, smarter, and safer business operations. It unifies security efforts, manages data flow intelligently, and proactively addresses risks before data leaks happen.

    4. Safeguards private information in SaaS apps

    DLP ensures that private and corporate tenants, unapproved apps and AI powered platforms have data security across SaaS (Software as a Service)  applications. That prevents accidental disclosures and unwanted access via these apps. 
    Most of the SaaS applications in the market are compliant with General Data Protection Regulation (GDPR). Also ensure they comply with data protection laws in your operating country. 

    5. Verifies adherence to privacy laws-

    DLP’s unified and intelligent approach to data protection makes management easier. It also enables you to proactively support compliance to regulatory standards across all control points.

    6. Protects intellectual property

    Businesses have innovative ideas, product designs and trade secrets. These can also become prime targets of cyber attacks. DLP machine learning and trainable classifiers classify and protect intellectual property. And that helps businesses maintain their competitive edge.

    Before implementing DLP measures in your business,

    Here are some key things to keep in mind before deciding to use DLP measures in your business. 

    1. Analyze what level of data protection you need for your business. Assess whether you need a minimum, medium, or higher level of DLP measures. 
    2. Remember, DLP measures can affect your end-user operations. So, aim to have a balance in your data security measures so the end-users can transact without glitches. Have corrective measures if DLP prevents genuine business use cases. Example- Management authorizations to approve data access requests.
    3. Combining DLP with mobile device management or MDM solutions can increase data security in your organization. MDM can work in tandem with your DLP solution and enhance data security in your organization. 

    How to implement DLP measures in your business

    Now you can assess your company’s DLP needs and plan accordingly. Let’s look at the processes involved in DLP.

    1. Critical Data Identification

    Where exactly are your important files located? Nowadays, most data is in either approved applications like emails, or in unauthorized apps like file sharing or messaging apps. 
    Identify all your critical data sources. List where your business data lives: on-premise servers, cloud apps, databases, email systems, laptops, mobile and IoT devices etc. In short, don’t leave out any device that is connected to your local network or/and internet. 
    Also understand the flow of data — how it’s created, accessed, shared and stored.

    2. Data Classification 

    After identifying and locating your critical data, the next step is their classification. Data classification involves sorting the data into categories based on sensitivity. 
    Look at what type of data each department uses. Many departments may use only email platforms, and some document and MS Excel files.  Marketing or designing departments may use many other types of files. 
    While classifying data, ask yourself, Is there an active directory (AD) and document control in place now? What add-ons to consider? 

    3. DLP implementation

    After data classification, your DLP solution applies the right protections for each data category. It’s the foundation for smart, effective and compliant data security.
    Implement the right DLP solution that fits the specific needs of your business. Make approval policies and unified management of all data touch points in your company. This can be department wise, hierarchy wise or on a need-to-know basis. Update data security rules across all your channels.
    Have streamlined procedures to maintain smooth workflows and business operations. At the same time, make sure your business operations don’t get restricted because of the data security regulations.
    Sometimes, data breaches can occur via 3rd party SaaS apps like video conference platforms. To protect against this, use secured browsers, clouds and remote sites. Use SSO (single sign on) and MFA (multi factor authentication) in your business networks.
    Thus you have access controls, rules for your email platforms, cloud storage, endpoint security. Plus real-time monitoring to detect anomalies.

    4. Optimization and Monitoring

    After DLP implementation, what next? Now you can optimize, monitor and improve the data securing processes. For that, conduct periodic audits to mimic data loss possibilities. Test periodically, to know whether your DLP system works effectively, or needs further fine-tuning.
    Look at your DLP alerts. If false positive alerts emerge, then fine-tune it. Ask yourself, is sensitive data still accessible? Are user roles and risk levels configured properly?
    Train your employees on cybersecurity best practices, simulations and data breach exercises. For extra safety, integrate your DLP solution with SIEM (Security Incident and Event Management) and CASB (Cloud Access Security Broker) tools.
    Additionally, you can stay informed with changes in privacy and security related laws such as:

    DLP solutions

    What are DLP solutions?

    DLP (Data Loss Prevention) solutions are a set of software tools and strategies designed to detect, monitor, and protect sensitive business data from accidental loss, intentional leak or misuse. They help businesses control how information is accessed, shared and stored, so critical and confidential data stays secure.

    Key Features of DLP Solutions:

    • Data identification and classification-
      Locate and label sensitive data based on content, context, and business value.
    • Policy enforcement-
      Apply rules to control how data is accessed, transferred, or shared, both inside and outside the organization.
    • Real-time monitoring-
      Track data in use (on endpoints), in motion (across networks), and at rest (in storage or cloud).
    • Alerts and reporting-
      Notify security teams about unusual activities or policy violations.
    • Incident response-
      Automatically block or quarantine suspicious activities and provide insights for investigation.
    • Compliance support-
      Help businesses meet legal and industry-specific data protection requirements

    Examples of DLP solutions:

    • Microsoft Purview
    • Symantec DLP by Broadcom
    • Forcepoint DLP (a cloud native platform by Zero Trust)
    • McAfee DLP (strong for endpoint security)
    • Cyberhaven (focused on insider threats),
    • Safetica (user friendly with strong compliance tools),
    • SecureTrust (for data security)
    • TrenMicro Micro IDLP (strong in endpoint security),
    • Trellix DLP (for advanced threat protection)
    • Endpoint Protector (by CoSoSys for data security and device control)

    AI powered DLP

    As business grows, policies change and data interconnections become complex. Then it gets harder to track all data moves, interactions, and the factors affecting them. Unified, proactive AI-driven data management can streamline DLP processes then. 

    AI driven DLP solutions efficiently track movement of data shared by various software, apps and other programs. These applications benefit from AI access security. This lowers security risks, streamlines complexity and administration. Your life gets easier.

    While there are many benefits to AI powered DLP, make sure you don’t endanger your private and sensitive information.

    Examples of AI powered DLP solutions:

    • Palo Alto Enterprise DLP
    • Forcepoint DLP
    • Symantec DLP
    • Trellix DLP
    • Nightfall AI
    • Safetica

    Final Thoughts

    Data loss prevention (DLP) measures help protect organizations’ valuable assets. It secures critical business data from unintentional leaks and intentional breaches. It also helps businesses stay compliant with data protection laws, safeguard customer trust, and maintain operational integrity. 

    When deciding to use DLP measures in your company, consider your business environment. Assess the level of data safety you need and decide accordingly. Also keep a balance between data security and smooth end-user operations. 

    First identify your critically important data, classify them, and then implement a DLP solution. Afterwards, test and monitor regularly for effective DLP in your business.  

    Here are some useful links for you:

    Check if your email or phone number was compromised.

    To know whether other personal details are exposed.

    Find out if your personal data has been exposed on dark web.

    Share your thoughts on the topic. Drop a comment below.