How Artificial Intelligence Assistants Were Designed with Humans in Mind

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Access Management Fails in a World of AI Agents

Traditional Identity and Access Management (IAM) systems were built for a time when the biggest challenge was ensuring that humans had the necessary permissions to access company resources.

However, with the rise of artificial intelligence (AI) agents, this approach is no longer sufficient. Non-human identities now account for over 90% of all authentications, and AI agents act autonomously across systems, triggering complex chains of API calls and making access decisions in mere milliseconds.

The Flawed Core Assumption

The core assumption underlying most IAM platforms – that access is a gate to be passed through once – is fundamentally flawed when it comes to AI agents.

These systems are designed to treat authorization as a static concept, rather than a dynamic process that must be continuously evaluated at every step.

According to Gartner, “most IAM platforms lack the ability to understand how a request is made or whether it is expected in a given context.”

The Challenges of AI-Driven Interactions

When an AI agent acts on behalf of a user, there are effectively two identities at play: the agent itself and the user it represents.

Most IAM systems struggle to keep pace with the rapidly evolving landscape of AI-driven interactions, leaving organizations vulnerable to misconfigured permissions, orphaned identities, and inadequate access controls.

A New Approach: Application-Centric Access Management

Experts recommend adopting an application-centric approach to access management.

By decoupling what an application is permitted to do from what the user is permitted to do, organizations can define precise rules governing how one application may act on a user’s behalf versus another.

This enables just-in-time, least-privilege access, scoped to the specific action, data, and moment.

APIs and Access Tokens

APIs can then rely on access tokens carrying contextual information to make informed access decisions.

When an agent authenticates and receives a token, it should be scoped exactly to the action it needs to perform, disappearing once the task is complete.

This eliminates the risk of standing access to compromise and minimizes the impact of misconfigured permissions.

Existing Standards and Implementations

Fortunately, the necessary standards – such as OAuth 2.0, token exchange, and dynamic client registration – are already widely implemented by enterprise APIs.

Organizations that already use OAuth can apply these existing primitives more precisely, at the layer where AI agents operate, to get access management right.

The window for doing so is narrowing, as enterprises deploy AI agents on IAM infrastructure that was not designed for non-human identities.

The Risks of Not Acting

Without proper agent governance, organizations risk exposing themselves to increased security risks and compliance issues.

The question is no longer whether IAM platforms support AI agents, but whether they govern what agents do at runtime or merely authenticate them at the door.

Mitigating the Risks

By understanding the limitations of traditional IAM approaches and embracing an application-centric mindset, organizations can mitigate the risks associated with AI-driven access management and ensure that their access controls keep pace with the evolving digital landscape.