AI Agents Need IAM Too: Rethinking Identity and Access for Autonomous Systems

For years, Identity and Access Management has followed a familiar pattern. A person logs in. An application authenticates. A service account connects to another service. We know who — or what — is making the request, and we decide whether that identity should be allowed to proceed.

AI agents are starting to challenge that model. Unlike a traditional application, an AI agent can interpret a goal, choose actions, call multiple tools, access data, and continue working with limited human involvement.

That raises a simple but important question:

When an AI agent performs an action inside your environment, whose identity is it using? And more importantly — who is accountable for what it does?

These questions are becoming increasingly relevant as autonomous systems move from experimentation into real enterprise workflows.

From AI Assistant to Digital Operator

Most organizations first used generative AI for answering questions, summarizing documents, or generating code. Agentic AI changes that. An agent may now be able to:

• read enterprise files
• query databases
• create or update tickets
• send emails
• trigger APIs
• deploy applications
• modify infrastructure
• execute business workflows

At that point, the AI is no longer simply generating information. It is operating inside the environment. And anything operating inside an enterprise environment needs an identity.

The Shortcut That Creates Risk

The easiest way to give an AI agent access is often to reuse something that already exists:

• a developer token
• a shared service account
• a privileged system role
• a long-lived application credential

It works, but it creates a serious accountability problem. If an agent and a human share the same identity, audit logs may show who authenticated without clearly showing who actually performed the action.

Imagine seeing:

admin@company.com deleted production-resource-01

What if the user did not perform that action directly? Perhaps an AI agent was operating with that user's credentials and interpreted an instruction incorrectly. Now the organization has an identity problem, not just an AI problem.

AI Agents Should Be First-Class Identities

A useful model is simple: treat an AI agent like an employee, workload, or service identity — but with tighter controls.

An enterprise should be able to answer: Who owns it? What is its purpose? What systems can it access? What data can it read? What actions can it perform? Who authorized those actions? How do we disable it?

Soon, AI agents may become another managed identity category alongside users, applications, workloads, and service accounts.

Least Privilege Matters Even More

Least privilege is not a new concept. But autonomous agents make it more important. Consider an infrastructure agent designed to investigate application failures. It may need permission to:

AI Operations Agent
 |
 +-- Read Application Logs
 +-- View Container Status
 +-- View Orchestrated Workloads
 +-- Read Monitoring Metrics
 +-- Create Incident Ticket
 |
 X-- Delete Infrastructure
 X-- Modify Access Policies
 X-- Change Network Security Rules

The agent remains useful without receiving unnecessary authority. Giving an agent broad administrative access simply because it is easier to integrate creates avoidable risk.

Authentication Is Only Half the Problem

Proving an agent's identity is one challenge. The harder question is: what is the agent allowed to do right now?

An agent may perform tasks for multiple users with different levels of access. The system therefore needs to understand both who requested the action and which agent performed it. A better model looks like this:

Human User
   ↓
Delegates a Task
   ↓
AI Agent Identity
   ↓
Scoped / Temporary Authorization
   ↓
Approved Tools and Systems
   ↓
Action
   ↓
Audit Trail

The user should not simply hand over their identity to the agent. Delegation should be explicit and traceable.

Short-Lived Credentials Should Be the Default

Long-lived credentials have always been risky. They are even less suitable for autonomous agents. Where possible, agents should use:

• temporary credentials
• workload identities
• managed service identities
• short-lived tokens
• role-based access
• just-in-time authorization

Give the agent the minimum authority it needs, for the minimum amount of time it needs it.

Human Approval Still Matters

Autonomy does not need to mean unlimited autonomy. Some actions can happen automatically:

Read logs              → Automatic
Generate report        → Automatic
Create support ticket  → Automatic
Restart test service   → Policy dependent

Others should require approval:

Change firewall rules       → Approval required
Modify access permissions   → Approval required
Delete production database  → Approval required
Transfer financial funds    → Approval required

This creates a practical balance between automation and control.

Logging Has to Change Too

Traditional monitoring often asks: did the application succeed or fail? Agent observability needs more context. A useful audit trail should capture information such as:

User:              employee@company.com
Agent:             operations-agent-07
Task:              Investigate API latency
Systems Accessed:  Centralized Logging Platform
                   Container Platform
                   Monitoring Dashboard
Actions:           Read application logs
                   Checked workload health
                   Queried performance metrics
Privileged Action: Restart production service
Approval:          Approved by Operations Lead

This provides far more value during an investigation than a simple message such as API call successful. As AI agents become operational actors, identity and observability will become increasingly connected.

Agent Sprawl Could Be the Next Identity Sprawl

Most organizations already struggle with forgotten service accounts. An application is retired, but the identity remains. The permissions remain too. Now imagine the same problem with hundreds or thousands of AI agents.

Organizations will need lifecycle controls similar to those already used for employees and applications:

Create → Approve → Monitor → Review → Revoke → Retire

Without this discipline, AI agents could quickly become another source of unmanaged identities and excessive permissions.

Zero Trust Applies to Agents Too

The Zero Trust principle is simple: never trust, always verify. That principle fits AI agents well. An agent should not automatically be trusted because:

• it was developed internally
• it runs inside the corporate network
• it uses an approved AI model
• a legitimate employee initiated the task

Every access request still needs context. Who is the agent? Who delegated the task? What resource is being accessed? What action is being requested? Is that action allowed? Does it require approval?

An intelligent system should not receive unlimited authority simply because it is intelligent.

What IT Teams Can Start Doing Now

Organizations can take practical steps today:

• inventory AI agents
• give each agent a unique identity
• avoid shared human credentials
• apply least privilege
• prefer temporary credentials
• require approval for sensitive actions
• log both the human and agent identity
• review and retire unused agents

The goal is to treat agents as managed enterprise identities, not experimental scripts with API access.

The Bigger Shift

Much of the AI security conversation has focused on models. Can the model hallucinate? Can it leak data? Can someone manipulate its prompt? Those questions matter. But agentic AI adds another question: what happens when the AI has credentials?

Once an AI system can authenticate to applications, query production databases, call APIs, modify infrastructure, or execute business workflows, the discussion changes. It is no longer only about protecting the model. It is about controlling the authority given to it.

For decades, identity and access systems have answered three basic questions: Who are you? What are you allowed to do? Can we prove what you did?

Autonomous AI does not make those questions obsolete. It makes them more important. And that is why one thing is becoming increasingly clear:

AI agents need identity and access controls too.