The “When” - Dynamic AI Access Control for a Changing Timeline

Generative AI has reshaped how we think about identity security and access control. One of the most pressing challenges is determining when access should be granted, revoked, or adjusted.

Unlike traditional systems, where access timelines are defined by static sessions or token expiration, AI-driven environments demand a more dynamic approach.

This article is the fourth installment in the series: “The Challenges of Generative AI in Identity and Access Management (IAM),” where we attempt to answer some major questions about AI identity security:

• The “Who” - Understanding AI Identity in IAM
• The “What” - Adopting Proactive Identity Security
• The “Where” - Managing AI Permissions
• The “When” - Dynamic AI Access Control for a Changing Timeline

In my previous articles in the series, I’ve covered who is accessing our systems, what they’re attempting to do, and where they are trying to go. The final piece is when access should be granted.

Now, it’s time to address the fourth question: When should access be granted, adjusted, or revoked?

The question of when is very important because it’s often overlooked when talking about authorization. A misconception exists that it’s a solved problem, as the timeline of a user’s actions is fully addressed. Yet AI access control introduces complexities that require us to revisit the access control timeline.

In this article, we’ll explore strategies like Continuous Access Evaluation Profile (CAEP) and event-driven tools like OPToggles and OpenFeature that help manage AI access control dynamically. These approaches ensure that access decisions are no longer bound to static timelines but are instead guided by intelligent, real-time assessments.

Let’s get into it -

Session-Based & Token-Based Auth

Authentication and authorization used to be session-based. We’d start a session through some form of verification, assign a time limit to the session, and then disconnect the session once it has expired. Throughout the session, we’d authorize actions based on session data.

Without diving too deeply into the reasons for this, session-based systems quickly showed their limitations, particularly in terms of security vulnerabilities and inefficiency, and were replaced with token-based authentication, ****where tokens validated user access without maintaining an active session on the server.

Token-based authentication was a significant improvement, but even tokens are not foolproof in our context. In short, generative AI can fake tokens, create false validations, or rapidly exploit vulnerabilities, making traditional token security increasingly unreliable.

Tokens that live too long are risky, while tokens with short lifespans force systems to constantly re-validate against a centralized server, undermining scalability and efficiency.

A New Approach: Continuous Access Monitoring

To address the limitations of sessions and tokens, we need to adopt a more dynamic and event-driven approach to access control. We should think of access timelines as resembling peaks and valleys, like a heart rate monitor, rather than a straight line. This requires us to constantly monitor and reevaluate access rather than relying on static methods.

One promising methodology in this space is the Continuous Access Evaluation Profile (CAEP). CAEP introduces event-driven mechanisms to identity security, enabling real-time monitoring and response to changes in user behavior or system conditions. If a user’s risk score increases or their IP address suddenly changes, for instance, CAEP ensures the system can adapt, revoke, or adjust permissions dynamically.

By leveraging event-driven systems, we can mitigate vulnerabilities stemming from unmonitored changes, which are increasingly relevant in the age of GenAI.

Authentication and Authorization Feedback Loop

One key aspect of creating dynamic access control for AI identity is establishing a feedback loop between authentication and authorization providers. This can be achieved by incorporating authentication standards like OIDC, OAuth, and CAEP with dynamic authorization APIs exposed through OPAL and Permit.io.

The feedback loop created by this incorporation allows applications to enforce AI operations with undetermined access dynamically. For example, strong authentication policies might specify checks for when a user last performed authentication or sent a one-time password (OTP). A feedback loop can be created to prompt the authentication provider to send an OTP by condition-based resource grouping to determine AI permissions.

This loop enables policies such as dynamically controlling access by allowing operations only for strongly authenticated users who submit an OTP. If an AI agent’s operation is denied due to insufficient authentication, the system can notify the agent to await manual verification by a human user. These feedback loops allow for proactive and adaptive enforcement of access rules for AI agents.

Dynamic Access Request Flows

Secure collaboration features are increasingly crucial for modern applications. Historically, authorization systems have been static, focusing on predefined rules about user access. With cloud platforms like Google Drive and Notion, secure collaboration is now integrated into authorization, incorporating access requests and approval flows.

Permit provides APIs, such as Permit Share-if, to manage these processes. These APIs allow seamless implementation of approval flows and access requests, even for granular resource instances. AI agents facing access denials can leverage these APIs to request approval from human users dynamically. For instance, the Permit Approval Flow API enables agents to invoke calls for operation permissions on specific resources, allowing fine-grained, one-time, or recurring access.

These tools create dynamic policy rules and enhance collaboration. An AI agent denied an operation can trigger a chain call to request approval, awaiting a human user’s response. Once approved, the agent can proceed efficiently while managing its own lifecycle of permissions dynamically and proactively.

Practical Implementation for Event-Driven Access

To build such a system, we can leverage open-source tools like OpenFeature and OPToggles, which integrate seamlessly with policy engines to create event-driven mechanisms. Here’s how that could work:

1. A user authenticates and is granted access to an application.
2. The system connects to OPToggles, which monitors for changes in relevant data (e.g., user behavior, risk score, or IP address).
3. OPToggles works with policy engines to trigger real-time updates, ensuring that the user’s permissions reflect their current risk level and context.
4. If changes occur—such as a suspicious IP address or a flagged behavior—the system can revoke access, restrict operations, or present a new user experience immediately.

Using tools like OPToggles and OpenFeature (or other feature-toggling solutions), you can create a continuous and responsive application experience.

A New Focus on AI Identity Access Lifespans

To fully answer the question of when, we must shift from viewing access as a static, time-bound concept to understanding it as part of a dynamic, event-driven timeline. This means continuously challenging assumptions, reevaluating sessions, and incorporating real-time data into every access decision.

Rather than relying solely on token expiration or session timeouts, we should design systems that can intelligently assess when access should end based on changing conditions.

In my next article, I will review all four questions and see what we learned from addressing each of them in the context of AI access control.

Until then, if you have questions or want to learn more about IAM, Join our Slack community, where hundreds of developers are building and implementing authorization.