The AI identity crisis: Why traditional IAM is no match for agentic AI

By Joseph Dhanapal, Board Strategic Advisor, SecureAuth 

The enterprise AI revolution has moved beyond ChatGPT and document summarization.

We’re now entering the era of agentic AI — autonomous systems that don’t just generate responses but take real actions within business workflows. These AI agents are triaging support tickets, provisioning user accounts, and even writing access policies. They’ve evolved from tools to actors, and that shift is creating a massive security blind spot.

The problem hidden in plain sight

Traditional identity and access management was built for a predictable world: human users with stable roles, static policies, and clear organizational hierarchies. Your CFO has one set of permissions, your intern has another, and those boundaries rarely change overnight.

Agentic AI shatters those assumptions. These systems operate autonomously, making decisions and accessing data without human oversight. They don’t fit neatly into org charts or role-based access controls. Yet most enterprises are deploying them using the same IAM frameworks designed for human users — a recipe for unintended data exposure.

Consider this scenario: A CFO asks an AI agent, “Do we have enough cash for payroll?” They should receive real-time financial data. But what happens when an intern asks the same question through the same AI system? Without proper identity-aware controls, both users might get identical responses, either potentially exposing sensitive financial information to unauthorized personnel (intern) or not providing the meaningful information to the authorized personnel (CFO).

The scale of the challenge

The shift from passive to autonomous AI isn’t theoretical — it’s happening now across every industry. Healthcare systems use AI agents to process patient data. Financial institutions deploy them for transaction monitoring. Manufacturing companies rely on them for supply chain optimization.

These agents don’t just access one system; they often orchestrate complex workflows across multiple platforms, calling APIs, retrieving data, and triggering actions across an organization’s entire digital ecosystem. Each interaction represents a potential access control failure point.

What makes this particularly challenging is that agentic AI operates at machine speed and scale. A single agent might make thousands of data requests per minute — far beyond what traditional IAM systems were designed to handle. Manual oversight becomes impossible, and policy violations can cascade rapidly across interconnected systems.

Beyond traditional IAM

The fundamental issue is that traditional IAM makes assumptions that no longer hold in an agentic AI world:

•       Human users: Agentic AI introduces non-human identities that need governance
•       Predictable roles: AI agents evolve and adapt, requiring dynamic access controls
•       Static policies: Autonomous systems need real-time, context-aware authorization

Organizations need a new paradigm — one that treats AI agents as first-class identities while maintaining the principle of least privilege. This means every request from an AI agent should be validated against the original user’s permissions, context, and current risk profile.

Technology that works today

Rather than trying to retrofit human-centric IAM for AI agents, organizations need a system that validates every request in real-time — regardless of whether it comes from a human or an autonomous agent.  Businesses can enforce Zero Trust principles at the transaction layer with technologies like SecureAuth’s Microperimeter solution.

With these kinds of systems, when a user authenticates, they receive an access token that encodes their permissions. When an AI agent acts on their behalf, every data request is validated against those original permissions before any information is released. The system checks not just who is making the request, but what they’re authorized to access and whether the request aligns with current security policies.

This approach provides several critical capabilities:

•       Identity inheritance: AI agents inherit and respect the access permissions of the users they represent
•       Real-time validation: Every request is checked against current policies, not cached permissions
•       Granular control: Organizations can define precisely what each agent can access for each user
•       Audit transparency: Complete visibility into what agents are doing — and on whose behalf
•       Asynchronous human approval: Set and execute on policies where an agent activity will still require a human approval before transacting

The competitive imperative

Organizations that solve the agentic AI identity challenge will gain a significant competitive advantage. They’ll be able to deploy AI agents more confidently, scale their automation initiatives faster, and maintain security and compliance even as their systems become more autonomous.

Those that don’t will face a difficult choice: limit AI capabilities to maintain security or accept increasing risk as they scale automation. Neither option is sustainable in a competitive marketplace where AI capabilities directly impact business outcomes.

The path forward

The agentic AI revolution isn’t coming — it’s here. The question isn’t whether organizations will need to address AI identity management, but how quickly they can implement solutions that enable secure automation at scale.

Smart enterprises are already rethinking their identity strategies to accommodate non-human actors. They’re implementing systems that can handle the speed and complexity of autonomous AI while maintaining the security and governance standards their businesses require.

The organizations that master AI identity management today will be the ones that lead their industries tomorrow. The window for proactive preparation is closing rapidly — and the cost of reactive solutions will only increase as agentic AI becomes more deeply embedded in business operations.

The future belongs to organizations that can harness the power of autonomous AI while maintaining complete control over their data and systems. The technology to make that possible exists today — the question is which organizations will be first to implement it.

About the author

Joseph Dhanapal is a Board Strategic Advisor at SecureAuth, a provider of workforce and customer identity and access management solutions. With over 20 years of experience in identity security, he’s led transformation initiatives for Fortune 500 companies and helped organizations navigate the evolving cybersecurity landscape.

Article Topics

AI agents  |  cybersecurity  |  identity access management (IAM)  |  identity management  |  non-human identities  |  SecureAuth  |  zero trust