By 2025, Zero Trust has transitioned from an emerging concept to a mandatory security framework essential for modern organizations. No longer just a theoretical approach, it’s becoming a prerequisite that organizations must integrate into their operational fabric. A well-architected Zero Trust strategy doesn’t merely help in meeting basic compliance demands; it actively fosters cyber resilience, safeguards partnerships, and guarantees seamless business continuity. According to a recent study, over 80% of organizations aim to adopt Zero Trust principles by 2026, signaling a significant shift towards robust security frameworks.
In the evolving landscape of Zero Trust, artificial intelligence (AI) stands out as a crucial tool, enhancing automation in adaptive trust and ongoing risk assessments. Zero Trust architectures require continuous user and device evaluations based on various parameters—device posture, user behavior, location, and workload sensitivity. This constant monitoring generates vast amounts of data, far exceeding the processing capabilities of human teams alone.
AI plays a pivotal role in managing this complexity by enhancing the effectiveness of all five CISA Zero Trust pillars: identity, devices, networks, applications, and data. It aids in distinguishing genuine threats from irrelevant data, identifying malware, and applying behavioral analytics to signal anomalies that human teams would likely overlook. For example, if a user suddenly initiates a download of sensitive files at an odd hour from an unfamiliar location, AI algorithms trained to recognize typical behavior can swiftly flag this anomaly, evaluate the associated risks, and trigger appropriate responses, such as session termination or requiring reauthentication. This mechanism reflects the principle of adaptive trust, whereby access decisions are continually adjusted based on real-time risk assessments, driven by automation to enable prompt responses without necessitating human intervention.
Predictive vs. Generative AI: Different Tools, Different Purposes
Within the realm of Zero Trust, two primary categories of AI come into play: predictive and generative models. Predictive AI, which encompasses machine learning and deep learning techniques, uses historical data to detect patterns, behaviors, and early warning signs of potential compromises. These models are integral to detection and prevention mechanisms—such as Endpoint Detection and Response (EDR) systems and intrusion detection platforms—aimed at identifying threats early in the attack lifecycle. In the context of Zero Trust, predictive AI strengthens the control plane by delivering real-time data that informs dynamic policy enforcement. It facilitates the ongoing evaluation of access requests by scoring the situational context: Is the device compliant? Is the login location suspicious? Does the user behavior align with expected patterns?
On the other hand, generative AI includes advanced systems like ChatGPT and Gemini, designed for entirely different functionalities. Unlike predictive AI, generative AI does not focus on enforcement; instead, it aids human operators by synthesizing information, crafting queries, and streamlining scripting processes. In fast-paced security frameworks, this capability is invaluable, enabling analysts to work more efficiently and effectively triage investigations.
Beyond these two categories lies agentic AI, which empowers advanced large language models to actively participate in security operations. By integrating an LLM within a lightweight “agent” that can interact with APIs, perform scripts, and adapt based on real-time feedback, organizations can establish a self-directed layer of automation. For instance, an agentic AI could autonomously gather user identity contexts, modify network micro-segmentation policies, initiate temporary access workflows, and retract privileges once risk thresholds are satisfied—all without human interaction. This level of automation not only accelerates response times but also guarantees consistency and scalability, freeing security teams to concentrate on strategic threat-hunting activities while routine procedures are executed reliably in the background.
All AI applications hold value within a Zero Trust framework. Predictive AI fortifies automated enforcement through real-time risk scoring. Generative AI expedites processes for defenders, enabling swifter, better-informed decision-making, particularly in high-pressure or data-rich situations. Meanwhile, agentic AI enhances orchestration and complete automation, allowing for the seamless adjustment of policies, risk remediation, and privilege revocation without human oversight. The strength of a Zero Trust model arises from its effective integration of AI where it yields the greatest advantages.
Human-Machine Teaming: Working in Tandem
While AI models are becoming increasingly important, they cannot become the sole administrators within a Zero Trust architecture. Predictive, generative, and agentic AI serve more as specialized co-pilot analysts—highlighting patterns, summarizing context, or managing workflows based on real-time insights. True Zero Trust still mandates careful human oversight concerning policy logic, rigorous system design, and continuous monitoring to ensure that automated actions fit in with broader security goals.
This perspective grows vital as AI is not shielded from exploitation. The SANS Critical AI Security Guidelines lay out potential risks including model poisoning, inference tampering, and vector database manipulation—all of which can undermine Zero Trust enforcement if AI systems are treated as infallible. In this light, the SANS SEC530 Defensible Security Architecture & Engineering: Implementing Zero Trust for the Hybrid Enterprise course highlights the importance of human-machine cooperation. In this model, AI works to automate data review and provide response suggestions, yet humans retain the responsibility of establishing constraints and validating outputs within the broader security architecture. This might involve writing stricter enforcement criteria or carefully managing access to outputs generated by the AI models, ensuring that the control remains firmly with operators.
This collaborative approach aligns as a sustainable pathway moving forward. Computers can exceed human capabilities in data processing, yet they often lack nuances like business context, creativity, and ethical considerations that only humans can provide. Security practitioners—those “all-around defenders”—remain indispensable, not just for responding to incidents, but for crafting resilient enforcement strategies, interpreting complex scenarios, and executing judgment calls that automated systems cannot handle. The future of Zero Trust is not about AI displacing humans; it’s about AI enhancing human capabilities, surfacing actionable insights, speeding up investigations, and scaling decision-making while preserving human oversight.
Ready for More Insight?
For a deeper dive on AI’s role within Zero Trust, SANS Certified Instructor Josh Johnson will lead a session as part of the SANS DC Metro Fall 2025 live training event (Sept. 29-Oct. 4, 2025) in Rockville, MD. This event fosters a hands-on learning atmosphere featuring immersive labs, simulations, and practical exercises aimed at real-world application.
Register for SANS DC Metro Fall 2025 here.
Note: This article was written and contributed by Ismael Valenzuela, SANS Senior Instructor and Vice President of Threat Research and Intelligence at Arctic Wolf.
