AI Identities Are Coming for Your Zero Trust Framework — And Most CISOs Aren’t Ready

AI has become the wildcard in modern cybersecurity—simultaneously a productivity accelerator and a threat multiplier. For CISOs, the question isn’t whether AI will reshape security. It already has. The real question:

Can your zero trust framework tell the difference between a trusted user and an AI pretending to be one?

In our CISO Perspectives for 2026 survey of security leaders, 78% said AI will significantly increase their team’s workload, and the exact same percentage admitted they don’t yet have a formal strategy for securing AI-generated assets or identities.

This isn’t just a governance problem. It’s a zero trust problem, and that gap between human and machine trust is widening fast.

 

AI is flooding enterprises with unverified identities

Today’s zero trust frameworks were designed around known, verified, and managed identities—users, devices, applications, and services that follow predictable onboarding and access rules. But AI breaks those assumptions.

Tools like generative AI, autonomous agents, and self-learning scripts are now:

• Spinning up ephemeral service accounts faster than IAM policies can react
• Creating machine identities that mimic real users or applications
• Introducing new data access patterns that defy human logic

Without clear attribution, these entities challenge the fundamentals of zero trust:

• Who—or what—is this identity?
• What is it allowed to access?
• Is it behaving within normal bounds?

Right now, most organizations can’t answer those questions in real time.

 

Zero trust requires smarter verification—not just segmentation

AI’s impact isn’t just about identity sprawl—it’s about trust validation.

For years, many organizations equated zero trust with microsegmentation—dividing networks into smaller zones to limit lateral movement. But this only restricts where an identity can go, not whether it should be trusted in the first place. In a world of synthetic users, machine identities, and autonomous agents, knowing who or what you’re granting access to matters more than how your network is carved up.

As the number of synthetic and autonomous entities grows, static controls simply can’t keep up. For example:

MFA and SSO are blind to machine identity abuse

Multi-factor authentication (MFA) and single sign-on (SSO) are designed to verify humans, not automation. Service accounts, bots, and AI agents typically log in non-interactively using tokens or certificates, so traditional MFA challenges never trigger. Once those credentials are compromised, most identity systems still treat them as trusted. That’s why continuous, policy-based controls—not static logins—are essential.

Traditional NAC can’t track workloads that shift between cloud regions

Legacy, on-prem NAC was built for traditional office spaces. It monitors devices physically connected to switches or controllers, but can’t easily track workloads that live in the cloud or move between hybrid environments. Cloud-based NAC, on the other hand, extends visibility and enforcement everywhere—pulling telemetry from identity providers, EDRs, MDMs, and cloud platforms to keep policies accurate even as AI workflows spin up new devices and identities.

Legacy remote access tools assume too much trust

VPNs and early ZTNA/SDP implementations often granted overly broad access. VPNs place users on the network, allowing overexposure and risk of lateral movement. Early ZTNA solutions would often make an initial decision and then trust the session. Modern, cloud ZTNA enforces per-app access with continuous, context-aware evaluation. This includes factoring in user role, access level permitted, device posture, behavior, and the sensitivity of the resource being accessed. These modern ZTNA solutions can trigger step-up authentication, restrict actions, or terminate sessions when human or machine behavior deviates.

The solution to ensuring zero trust isn’t more segmentation—it’s smarter, constant verification.

This is where behavioral analytics and adaptive policy become the new foundation of zero trust. CISOs are beginning to prioritize tools that can:

• Baseline normal behavior for both users and AI-driven services
• Detect anomalies in access, velocity, or data use
• Quarantine or restrict access dynamically—without human intervention

Zero trust is evolving, extending access control to include autonomous trust verification. That evolution brings new responsibilities for security leaders.

 

The CISO dilemma: AI is necessary—and risky

Our research reveals a widening gap:

• 59% of CISOs are actively developing AI security strategies
• 78% admit they lack a framework to enforce zero trust for AI-generated assets

This disconnect is dangerous. Security teams are being asked to enable AI across development, analytics, automation, and operations — but most lack the visibility and guardrails to verify what these systems are doing, or who (or what) they’re acting as.

As AI becomes an actor in enterprise workflows—not just a tool—CISOs must protect both human and synthetic identities under a single policy model.

The fix starts with a mindset shift:

AI must be treated as a first-class identity.

Every agent, bot, and automation should have its own unique, verifiable credentials — onboarded and offboarded just like a human user, with certificate-based authentication and clear attribution. No shared service accounts. No anonymous endpoints.

Extend least privilege policies

Apply least privilege to machine identities just as you do to people. Grant only the minimum rights necessary for each AI task and continuously re-evaluate permissions as context changes. “God Mode” access for AI agents or automation scripts isn’t just risky — it’s 100% incompatible with zero trust.

Accountability is everything

Log every action an AI entity takes, including prompts, outputs, API calls, and data interactions, and pair that telemetry with real-time anomaly detection. An AI system gone rogue may not look like a traditional breach; it might be thousands of “harmless” queries quietly exfiltrating data.

Policy enforcement must also evolve

Build engines that enforce at the point of action. Block or contain AI-driven activity automatically when it deviates from normal patterns. Modern cloud NAC and ZTNA platforms already support these adaptive responses, enabling policy decisions in real time without human bottlenecks.

In short: while AI can accelerate your operations, it must always operate under supervision, verification, and enforceable policy boundaries.

Practical first steps for AI-aware zero trust

If your zero trust program was built around human users and SaaS apps, it’s time to expand your model. Start with these steps:

• Inventory machine and service accounts, including AI-generated entities and ephemeral workloads.
• Set minimum trust requirements: no identity—human or synthetic—gets access without certificate-based authentication and posture verification.
• Monitor baseline behaviors with NAC and ZTNA for anomalous activity.
• Automate containment: ensure AI-driven processes have built-in kill switches.
• Engage compliance early: regulators are already moving on AI accountability standards.

 

Bottom line: secure your AI identities now

Zero trust was designed to protect against the unknown—but AI is multiplying the unknowns faster than most organizations can adapt.

Securing human users is no longer enough. In 2026, zero trust must extend to machine identities, synthetic agents, and AI-driven behavior. That means real-time verification, continuous posture assessment, and context-aware policy enforcement at scale.

CISOs who adapt quickly won’t just reduce risk—they’ll build the operational foundation for AI to thrive securely.