By 2025, Zero Trust had evolved from a conceptual framework to an essential pillar of modern security. It is no longer theoretical, and is a requirement that organizations must now adopt. A robust, defensible architecture built on the principles of zero trust is more than meeting baseline regulatory obligations. Supports cyber resilience, secures third-party partnerships, and ensures uninterrupted business operations. Second, according to a recent Zscaler report, over 80% of organizations plan to implement a Zero Trust strategy by 2026.
In the context of zero trust, artificial intelligence (AI) can greatly assist as a tool to implement automation around adaptive trust and ongoing risk assessment. In Zero Trust Architecture, access decisions must continually adapt to changing factors such as device posture, user behavior, location, and workload sensitivity. This constant assessment generates a large amount of data, far beyond what a human team can handle on its own.
AI is the key to managing that scale and plays a key role across all five Zero Trust Pillars (Identity, Devices, Networks, Applications, and Data). By filtering the signal from noise, AI can help detect intrusions, identify malware, apply behavioral analysis, and flag abnormalities that are nearly impossible to catch manually. For example, if a user suddenly downloads a sensitive file from an unusual location at 2am, an AI model trained at an action baseline can flag events, assess risks, and trigger actions such as reauthorization or end of session. This allows for adaptive trust. It is tailored in real time based on risk and supported by automation, allowing the system to respond quickly without waiting for human intervention.
Prediction and Generation AI: Various tools, different purposes
There are two main categories of AI related to zero trust: predictive models and generative models. Predictive AI, including machine learning and deep learning, trains historical data to identify early indicators of patterns, behaviors, and compromise. These models now help capture threats early in the attack chain, including EDR, intrusion detection platforms, and behavioral analysis engines. With regard to zero trust, predictive AI supports the control plane by supplying real-time signals to dynamic policy enforcement. Allows continuous evaluation of access requests in a scoring context. Is the device compliant? Is the location you log in rare? Does the behavior match the baseline activity?
Generation AI, such as large-scale language models such as ChatGpt and Gemini, serves a different purpose. These systems are not predictive and do not force control. Instead, it supports human operators by summarizing information, generating queries, accelerating scripts, and providing faster access to related contexts. In a high-tempo security environment, this feature reduces friction and allows analysts to triage and investigate more efficiently.
Agent AI incorporates a large language model beyond the role of support into active participants in security workflows. By invoking APIs, running scripts, and wrapping LLMs in lightweight “agents” that can adapt their behavior based on real-time feedback, you get an autonomous automation layer that coordinates complex zero trust tasks all the way to the end. For example, Agent AI can automatically collect identity contexts, adjust network microsegmentation policies, spin up temporary access workflows, and revoke privileges without manual intervention once the risk threshold is cleared. This evolution not only accelerates response times, but also ensures consistency and scalability, allowing teams to focus on strategic threat hunting, and daily enforcement and repair occur in the background.
All of these approaches have a location for the zero trust model. Predictive AI enhances automation by facilitating real-time risk scoring. Generated AI allows defenders to travel faster and get more detailed information, especially in time sensitive or in large numbers of scenarios. Agent AI can bring orchestration and end-to-end automation into the mix, automatically adjusting policies, modifying risks, and revoking privileges without manual intervention. The strength of the Zero Trust architecture is that it applies where it is most appropriate.
Human-Machine Team: Working in a Tandem
Despite the growing role, AI models alone cannot function as the only “brain” of zero trust architecture. Predictive AI, Generator AI, and Agent AI each act like a specialized co-pilot analyst. This adjusts the range of patterns, context summary, or workflow based on real-time signals. True Zero Trust still relies on human-defined policy logic, strict system-level design, and continuous monitoring to ensure automated actions align with security goals.
This is especially important because AI is not immune to manipulation. SANS Critical AI Security Guidelines outline risks such as model addiction, inference tampering, and vector database manipulation. All of these can be used to overturn zero trust enforcement if the AI system is blindly trusted. This is why the implementation of Zero Trust in the SANS SEC530 Defenseable Security Architecture and Engineering: Hybrid Enterprise Course highlights the concept of human machine teams. AI automates data analysis and response recommendations, but humans need to set boundaries and validate these outputs within a broader security architecture. Whether that means writing stricter enforcement rules or segmenting access to model output, the control remains with the operator.
This collaboration model is increasingly recognized as the most sustainable method. Machines can overtake humans when it comes to processing volumes, but they may lack the specific business context, creativity, and ethical reasoning that only humans can bring. Practitioners – “All-round defenders” I want to call them. Not only does it respond to incidents, it remains essential for designing resilient enforcement strategies, interpreting ambiguous scenarios, and making calls to decisions that the machine cannot. The future of Zero Trust is not an AI that will replace humans. It amplifies humans, surfaces actionable insights, accelerates investigations, and scales enforcement decisions without removing human control.
Ready for more insights?
To dig deeper into AI’s role in Zero Trust, SANS Certified Instructor Josh Johnson will teach SEC530 at the SANS DC Metro Live Training event (September 29 to April 4, 2025) in Rockville, Maryland. The event will develop a dynamic learning environment featuring industry-leading practical labs, simulations and exercises.
Please sign up for Sans dc metro in fall 2025.
Note: This article was written and contributed by Ismael Valenzuela, Senior Instructor and Vice President of Threat Research and Intelligence at Arctic Wolf.
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