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Apr 15, 2024
7 min read

Enhancing Zero Trust Security with AI

Long-form story about how AI helps fight hackers, fraudsters & data thieves.

Photo by Miłosz Klinowski on Unsplash

First of all, before we start enhancing and optimizing the Zero Trust model, let’s briefly recall what it actually is.

What is Zero Trust?

Zero Trust is a security model that operates on the principle:

Never trust, always verify.

The Zero Trust security model was first created by John Kindervag in 2010 while he was a principal analyst at Forrester Research.

Photo of John Kindervag

  • Mobile access
  • IoT devices
  • Cloud computing

These three core fields are the main catalysts that pushed and blurred the boundaries of traditional networks, extending it to a whole new level.

Core problem: Traditional security models before 2010 relied heavily on securing the perimeter of the network. However, the threats increasingly originated not only from outside but also from within the network or bypassed these network perimeters entirely. That’s how Zero Trust was born.

Zero Trust operates on the following core assumptions:

  • Assume a Breach: Zero Trust operates under the assumption that threats exist both outside and inside the network.
  • Verify Explicitly: Every attempt to access a system or data must be authenticated, authorized, and continuously validated for security configuration. Only after all those steps can the access be granted!
  • Least Privilege Access: Users and devices are given the minimum access necessary to perform their tasks.
  • Microsegmentation: The network is segmented into smaller, secure zones to maintain separate access for different parts of the network.
  • Layered Defense: Multiple layers of security are employed to protect each access point. This redundancy helps ensure that the failure of one security measure does not compromise the overall security of the system.

How AI is reshaping Cybersecurity?

In general, the role of AI & Predictive Analytics in cybersecurity can be split into three major areas:

  1. Endpoint Protection
  2. Network Monitoring
  3. Efficiency, Adaptability & Scalability

Obviously, there are more use cases and categories in which AI is strengthening cybersecurity tactics, but for the sake of readability we’ll focus on these major ones.

1. Endpoint Protection

Data grows. The world becomes more dynamic. Technologies advance. As a result, the volume of data that needs securing has grown immensely. Data moves with endpoints in today’s highly mobile world. Think about all the IoT devices, e.g., smartwatches, home assistants, smartphones, etc. It’s the rich variety of endpoints that makes them so attractive for cyberattacks.

AI enhances endpoint protection by enabling more dynamic and adaptive security measures.

Photo by Tyler Nix on Unsplash


Marketing Specialist John Doe, an employee at a multinational corporation, often works from public Wi-Fi networks in coffee shops ☕.

This practice obviously exposes his device to various security risks.

While John is working, a tool called CrowdStrike Falcon (real tool, not sponsored) continuously monitors his laptop for any unusual activity.

One sunny day, AI behind CrowdStrike Falcon recognizes that a piece of malware is attempting to encrypt files - a common ransomware tactic - which is unusual for the normal operation of John’s device.

Immediately, CrowdStrike Falcon isolates John’s device from the network, preventing the ransomware from spreading to the corporate network. It also alerts the security team and provides them with a detailed analysis of the threat. With that being said, the cleaning process of the infected device is initiated, and the whole multinational corporation can sleep safely!

2. Network Monitoring

There’s a whole new level of complexity in terms of effective cybersecurity when we’re talking about a company with 10 employees vs. 10,000 employees.

AI can analyze network traffic in real time to detect anomalies (different access location, unusual access hours, etc.) ensuring that any suspicious activity is identified and addressed immediately.

Photo by Robin Pierre on Unsplash


The same dude, John Doe, now tries to access the corporate network remotely to update a marketing online generation lead report.

However, he got drunk last night and lost his wallet with all his passwords. (Probably he should have been fired long time ago for keeping passwords in his wallet, but that’s another story.) Anyway.

His credentials have been compromised. The attacker attempts to use his credentials to gain unauthorized access to sensitive data.

A tool called Cisco Stealthwatch (again, not sponsored) monitors the network, analyzes the data flow and notices that the access pattern deviates from John’s typical coffee-shop 08:00–17:00 behaviour.

Based on past incidents involving similar indicators the systems predicts suspicious activity ⚠️. It immediately sends an alert to the cybersecurity team and applies additional authentication requirements for continued access, thereby preventing a data breach.

3. Efficiency, Adaptability & Scalability

AI systems can handle vast amounts of data. Much faster than human counterparts.

When the company grows the operational costs associated with manual security increase. AI can assist in the efficient management of different identities, authentication, dynamic access control allocation, etc. AI can be used to continuously learn and establish constantly evolving “new” baselines of “new normal” activities specific to each “new” user and “new” device added.

Let’s have a look at final example!

Photo by Robs on Unsplash


John Doe works in a growing marketing enterprise. A new colleague, Jane Müller, joins the company. Compared to John, she works in UTC+4 time zone, works primarily from home and usually in the evenings, which means:

  • Different location access
  • Different working hours
  • Different network access patterns

By using an AI-enhanced Security tools for security information and log event management, e.g., IBM QRadar (again, not sponsored), this marketing enterprise can now way more effectively detect, prioritize and respond to potential security threats.

AI behind IBM QRadar automatically classifies the device, assesses its security posture, and applies appropriate security policies without needing manual intervention.

In general, the third area of AI-enhanced Zero Trust Security model is a bit vague in definition, I get it.

But to make it clear, let’s cover some of the biggest pros:

  1. Fastest behaviour analysis (close to real-time)
  2. Proactive security adjustments (no more manual setups & human errors)
  3. No human errors (Minimization of the human errors in setting up security protocols for new users like Jane)
  4. Get rid of extensive manual oversight → lower operational costs 💲💲💲
  5. Seamlessly scale your Zero Trust Security model as your organization grows

Effective cybersecurity is less about defending yourself and more about disrupting the enemy’s ability to attack.

- James Scott, Senior Fellow, Institute for Critical Infrastructure Technology

That’s a wrap! Thanks for reading!

I hope you enjoyed it! Stay tuned, stay safe & healthy!