The identity crisis at the heart of the AI agent revolution – Source: securityboulevard.com

AI agents are becoming the new interface for enterprise work, helping teams write code, automate operations, and execute transactions. But as organizations lean into Agentic AI, a foundational blind spot is coming into view:

Today’s identity systems were built for humans, not autonomous agents.

While agents now act with independence and intent, their identity infrastructure is stuck in the past. Most security and IAM tools assume static users, predictable sessions, and cloud-connected environments. None of that applies when you’re dealing with autonomous agents that:

• Operate independently
• Make real-time decisions
• Act on behalf of others
• Scale to thousands of instances per application

This mismatch is creating a fast-growing identity crisis in AI adoption.

Problem #1: Human identity patterns don’t translate to AI agents

Legacy IAM assumes:

• Long-lived user accounts
• Manual provisioning (JML)
• Passwords or MFA for authentication
• Role-based access grants

But agentic systems require:

• Ephemeral identities
• JIT credential issuance tied to CI/CD
• SPIFFE/SVID, PKCE, or cert-based auth
• Granular, scoped permissions at runtime
  

Without support for these modern requirements, organizations resort to insecure workarounds like shared credentials, over-permissioned roles, and hardcoded API keys.

Problem #2: OAuth and API keys are insufficient for autonomy

OAuth was designed for users. It assumes that the identity making the request can:

• Log in
• Consent to access
• Stay logged in for a while

Agents don’t do that. They:

• Act on behalf of users
• Spin up and down in seconds
• Chain requests across APIs and services

Traditional tokens and scopes can’t reflect delegation, context, or task-specific risk — making policy enforcement brittle and audit trails meaningless.

Problem #3: Access control doesn’t evolve with agentic workflows

Agents operate in dynamic workflows that change as business logic shifts. Yet traditional access control models:

• Are static
• Are assigned at deployment
• Don’t evaluate context at runtime

This leads to:

• Over-permissioned agents
• Toxic combinations of access
• No real-time policy enforcement

Enterprises lose visibility and control over what agents can do — and why.

Problem #4: No runtime delegation or provenance tracking

When agents act on a user’s behalf, trust boundaries break down without:

• On-Behalf-Of delegation
• Signed assertions
• Execution graphs for traceability

This creates:

• Compliance gaps (e.g., GDPR, SOX)
• Unattributable actions in logs
• Inability to answer “Who triggered this?”

Problem #5: Non-human identity sprawl

Most organizations already struggle with dormant service accounts and zombie credentials. Now, with Agentic AI:

• Each app may create 100s–1000s of agents
• Agents live only for minutes or hours
• Permissions often outlive the agent

Without automated lifecycle governance, we’re repeating the mistakes of human IAM — at machine speed and scale.

Problem #6: Identity tools aren’t composable across domains

Agents often interact with:

• APIs
• MCPs
• SaaS apps
• On-prem services

But IAM is still siloed by domain, and policy logic isn’t portable. Agents need cross-system identity orchestration, not just logins per service.