Artificial Intelligence in a Zero Trust World

March 24, 2025

There are so many new advancements in computing it is hard to keep up.  As a student of “Best Practices”, I have spent my career using frameworks and approaches to cyber security that best fit the business or mission. Zero Trust (ZT) is the approach of choice for Federal systems with mandates coming from the highest office for Federal systems to adhere to a Zero Trust approach and for the last 5 or so years, I have been helping federal and private sector clients with their ZT implementations. One thing that is clear is that the Automation and Orchestration pillar is critical to a ZT approach and is one of the most valuable pillars to keeping ZT principles congruent.

If you were wondering where AI/ML reside, this toolset is addressed mainly within the Automation & Orchestration pillar (see Figure 1).  Keep in mind that AI/ML are working throughout the pillars but for the sake of definition and alignment Automation & Orchestration is where it is found. The definition of AI is it is a broad field that involves machines replicating human intelligence to complete complex tasks, while ML is a specific branch of AI that concentrates on developing algorithms that enable computers to analyze data, recognize patterns, and enhance performance over time without direct programming. In essence, ML allows machines to "learn" from experience, making it a subset of AI. A good example is, AI/ML can “auto-magically” ensure authentication occurs or does not at the speed of compute once ZT principles are applied through thoughtfully designed access policies.

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Figure 1 Optiv + ClearShark Zero Trust Framework

Automation and orchestration are critical components of a Zero Trust Architecture (ZTA) because they enable organizations to enforce security policies dynamically, respond to threats in real time, and maintain continuous protection across complex IT environments. The Zero Trust model is based on continuous verification and least privilege access, which require rapid decision-making and enforcement that cannot be achieved manually at scale.

Examples of How AI/ML Supports Zero Trust Principles

In every pillar you can find ways to leverage AI/ML to enhance, speed-up, and enforce access policies, response processes, endpoint/workload/application compliance, data movement requirements, etc. Keep in mind that many modern ZT enabling capabilities have AI/ML embedded or available as a feature depending on the use-case. One thing to keep in mind as you formulate your strategy is that technology integration is your friend. "Many threat disrupting features are not offered by a single technology and therefore integration will help you achieve your goals". A 3^rd party solution integrator will be able to help you develop a “better together” ZTA. A few vendor agnostic and Zero Trust aligned examples of the benefits of AI/ML below:

AI and ML enhance identity verification, authentication, and access management by continuously analyzing user behavior and adapting security policies in real-time. Traditional identity and access management (IAM) systems rely on static rules, but AI-driven authentication evaluates multiple contextual factors—such as login location, device health, access patterns, and past behavior—to dynamically adjust access permissions. For example, ML algorithms can detect anomalies, such as a user logging in from an unusual location or accessing resources outside their normal workflow, and trigger step-up authentication or automatically restrict access. AI also strengthens privileged access management (PAM) by monitoring privileged user activities for signs of compromise and automating risk-based access decisions. By integrating AI-powered user and entity behavior analytics (UEBA), organizations can continuously verify identities, detect credential misuse, and enforce least privilege access, ensuring Zero Trust policies adapt to evolving threats without disrupting legitimate workflows.

AI and ML play a crucial role in continuously assessing device security posture and enforcing adaptive access controls. Traditional security models rely on predefined rules for device compliance, but AI-driven security solutions analyze real-time telemetry from endpoints, IoT devices, and unmanaged assets to detect anomalies and predict potential threats. ML models assess device behavior, identifying deviations from normal patterns—such as unusual network connections, unauthorized software installations, or unexpected configuration changes—that may indicate compromise. AI also enhances risk-based authentication by dynamically adjusting access privileges based on device health, location, and past usage patterns. Additionally, automated threat detection and response mechanisms enable Zero Trust architectures to isolate compromised devices in real time, preventing lateral movement within the network. By leveraging AI-powered analytics, organizations can enforce continuous verification of device integrity, ensuring only trusted, compliant devices gain access to sensitive resources.

AI and ML enhance security by continuously monitoring application behavior, enforcing dynamic access policies, and detecting anomalous activity. Traditional access controls rely on static permissions, but ML-driven solutions analyze real-time data to determine whether an application or workload request aligns with expected usage patterns. AI helps prevent unauthorized access by assessing factors such as user identity, device posture, geolocation, and previous interactions before granting access. ML also strengthens workload segmentation by mapping normal inter-application communications and flagging unexpected connections that could indicate an attempted breach. By leveraging AI-powered analytics, organizations can enforce least privilege access, prevent lateral movement, and ensure real-time protection of applications and workloads, strengthening their Zero Trust security posture.

AI and ML enhance data security by enabling real-time classification, access control, and anomaly detection. Traditional data protection methods rely on static policies, but ML-driven solutions can automate data discovery and classification, identifying sensitive information across structured and unstructured datasets. AI-powered behavior analytics monitor how users and applications interact with data, detecting unusual access patterns that may indicate insider threats, unauthorized data exfiltration, or ransomware activity. ML algorithms can also enforce dynamic data access policies, adjusting permissions based on risk factors such as user role, device security posture, and access location. Additionally, AI enhances Data Loss Prevention (DLP) by identifying and blocking unauthorized attempts to copy, transfer, or modify critical data. By integrating AI and ML into data security, organizations can enforce continuous verification, prevent unauthorized access, and reduce the risk of data breaches, strengthening Zero Trust principles across the enterprise.

AI and ML strengthen security by providing real-time network visibility, adaptive threat detection, and automated response mechanisms. Unlike traditional perimeter-based defenses, which rely on static rules, AI-driven solutions continuously analyze network traffic patterns, device communications, and connection requests to identify anomalies that may indicate malicious activity. ML models can detect lateral movement, unauthorized access attempts, or unusual traffic spikes that signal a potential breach, enabling automated containment before an attacker gains deeper access. AI also enhances micro-segmentation, dynamically adjusting network policies based on evolving risk conditions, ensuring that only verified users and devices can access specific resources. Additionally, AI-powered threat intelligence helps organizations anticipate attacks by correlating external data with internal network activity, enabling proactive defenses. By leveraging AI and ML for continuous monitoring and automated enforcement, Zero Trust architectures can prevent unauthorized access, limit attack propagation, and rapidly respond to emerging threats.

AI and ML provide continuous monitoring, anomaly detection, and predictive threat intelligence to enhance situational awareness. Traditional security monitoring relies on predefined rules and manual log analysis, but AI-driven solutions can process vast amounts of network, user, and device activity data in real time to detect emerging threats. ML algorithms identify behavioral deviations, such as unusual login patterns, unauthorized data access, or abnormal network traffic, helping security teams distinguish between legitimate activity and potential attacks. AI-powered User and Entity Behavior Analytics (UEBA) strengthens Zero Trust by correlating user behavior across multiple systems, flagging insider threats or compromised accounts before they escalate. Additionally, ML-driven analytics help prioritize security alerts, reducing false positives and allowing teams to focus on real risks. By integrating AI and ML into Zero Trust analytics, organizations can gain deep visibility into security events, detect threats faster, and automate intelligent responses to protect critical assets.

AI and ML play a crucial role in streamlining security operations, accelerating threat response, and ensuring continuous policy enforcement. Traditional security workflows rely on manual processes that can be slow and prone to human error, but AI-driven automation enables real-time decision-making and adaptive enforcement across an organization's security infrastructure. ML models analyze vast amounts of security telemetry to detect patterns, prioritize threats, and trigger automated workflows, reducing the burden on security teams. AI-powered Security Orchestration, Automation, and Response (SOAR) platforms integrate with existing security tools, allowing for automated containment of compromised accounts, isolation of suspicious devices, and enforcement of dynamic access policies. Additionally, AI enhances policy automation by continuously refining Zero Trust rules based on evolving risks, ensuring security controls remain effective without manual intervention. By leveraging AI and ML for orchestration, organizations can reduce response times, enhance threat mitigation, and maintain a resilient Zero Trust framework at scale.

AI and ML are indispensable for implementing a scalable, efficient, and proactive Zero Trust framework. They reduce manual workload, improve accuracy, and enable real-time security enforcement, ensuring that Zero Trust policies dynamically adapt to evolving threats. Organizations that integrate AI-driven automation into their Zero Trust strategy gain a faster, more resilient, and intelligence-driven cybersecurity posture.

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