Why Human-Centric Access Reviews Break Down for Machines and AI Agents

Let’s be direct about something most identity and security teams already know but rarely say out loud. Access reviews, as they were designed, are not working. Not for humans, and certainly not for the non-human identities and AI agents that now represent a growing share of access activity across modern enterprise environments.

This isn’t a criticism of the practitioners running these reviews. The problem is structural. The model was built for a world of users, accounts, and roles. A world where access was relatively static and human-driven. That world no longer exists.

The Honest Problem with Access Reviews

Traditional user access reviews operate on a simple premise to periodically present a reviewer with a list of entitlements and ask them to certify whether the access is appropriate. They are usually quarterly, monthly, or sometimes more frequently. While the cadence varies, the mechanics don’t.

The reviewer sees a snapshot consisting of a list of roles, a set of entitlements, a picture of what could be done. What they don’t see is what’s actually happening. Which permissions are being exercised? What actions are being taken? Is the access idle, or is it actively being used in ways that create exposure? That context is simply not surfaced. Without it, most reviews devolve into approval exercises or checks-in-a-box rather than meaningful risk decisions.

The result is access that accumulates over time and is unnecessary, persistent, and effectively invisible to the governance process meant to control it.

Non-Human Identities AI Agents Don’t Fit the Model

The gaps in traditional access reviews are significant when applied to human users. Applied to service accounts, API keys, OAuth tokens, and AI agents, those gaps become chasms.

Non-human identities and even more so AI agents don’t behave like human users. They operate continuously, not periodically. They’re represented by secrets and credentials, not profiles. Ownership is often unclear and assigned to a team, a system, or no one at all. When a reviewer is presented with a service account and its entitlement list, the relevant questions aren’t “should this exist?” They are: 

• What does this access actually allow? 
• What is it doing right now? 
• Does that behavior align with the purpose this identity was provisioned for?

None of those questions can be answered from an entitlement list. And, yet, that’s what most identity and access governance tooling offers today.

Entitlements Are Not a Proxy for Risk

Identity systems are good at telling you what access is assigned, where it’s assigned, and to whom. They’re not built to tell you how that access is being used. This distinction matters enormously.

When a review is based on entitlements alone, it’s a review of potential, not of actual behavior. And potential is a poor proxy for risk. The risks that matter in regulated environments are the ones that surface in SOX audits, GDPR assessments, and AI governance frameworks like ISO 42001 and are behavioral in nature, such as long-lived credentials that haven’t been rotated, permanent high-privilege access that’s exercised without business justification, service accounts operating outside their intended scope, AI agents interacting with systems unrelated to their function. None of these are meaningfully caught through periodic access reviews.

The Timing Problem

There’s a fundamental mismatch between how access works and how reviews work. Access is dynamic. Systems interact, agents execute workflows, permissions are exercised in real time across multiple environments. Reviews are periodic. By the time a quarterly access review occurs, the access landscape it’s meant to validate has changed substantially. The snapshot is already stale and not representative of the current state.

For human identities, this lag is a governance gap. For AI agents and automated workloads that operate continuously, it’s essentially a non-control.

What Actually Needs to Change

The question that governance needs to answer for non-human identities and AI agents isn’t “what access does this identity have?” It’s “what is the agent and its identities actually doing?”

That requires a different model entirely. Identity needs to be understood not just as a set of assigned entitlements, but as something that operates through credentials, acts within specific systems, and executes real functions. An identity’s risk profile is defined not by its provisioned access, but by its scoped intent and observed behavior.

Once you can see behavior, you can evaluate alignment. An AI agent provisioned to analyze sales data should interact with sales systems and not administrative interfaces in Snowflake, not financial systems outside its defined scope. When that boundary is crossed, it’s not an entitlement problem to solve in the next review cycle. It’s an intent-based misalignment that needs to be surfaced and addressed continuously.

Static Access Reviews Can Still Have a Role

This isn’t an argument for eliminating access reviews. In many environments, they’re a compliance requirement, and they serve a function as a periodic validation checkpoint and an audit artifact. That’s legitimate.

But they shouldn’t be confused with real-time controls, behavioral understanding, or a reliable mechanism for managing risk at scale, especially across the non-human identity and agentic AI infrastructure. Treating them as the primary governance mechanism for machine identities and AI agents creates a false sense of control.

The Bottom Line: The Need to Rethink Access Reviews

Human-centric governance models aren’t wrong. They are just insufficient for the environments we’re operating in now. Machines and agents need to be reviewed differently and dynamically. They need to be understood based on how they actually operate: what they access, what they execute, and whether that behavior aligns with their intended purpose.

Because in modern enterprise environments, risk doesn’t live in assigned access. It lives in access-in-use. And that’s not something a quarterly access review can see. It’s time for a new approach. Let us show you how Token Security helps you understand AI agent intent to enable you to continuously enforce least privilege access policies. Request a demo today.

‍