How to Secure Autonomous AI Agents with Verifiable Identity

AI agents are starting to do more than generate text or answer questions.

They can retrieve information, call APIs, interact with databases, and carry out tasks across different systems. Some can even coordinate with other agents to complete workflows without constant human involvement.

That's useful. But it also introduces a security problem that deserves more attention.

When an AI agent takes an action, how do we know which agent it is, who authorized it, and whether it should still have permission?

For developers building agentic applications, these questions are becoming increasingly important.

The identity problem in agentic AI

Consider a customer service AI agent that can retrieve account information, update customer records, and initiate refund requests.

In a traditional application, a user signs in and receives permissions based on their role.

With an autonomous agent, the situation becomes more complicated.

The agent may act on behalf of a user, communicate with other services, and execute several actions as part of a single workflow.

If the agent operates using a shared API key or a broadly privileged service account, it can become difficult to distinguish its actions from those of other agents.

This creates several challenges:

• Identifying which agent initiated a request.
• Determining who authorized the agent.
• Restricting access to specific tasks.
• Revoking permissions when circumstances change.
• Maintaining an audit trail of agent activity.

The problem isn't simply authentication. It's establishing identity, authority, and accountability throughout an agent's lifecycle.

Why traditional IAM alone may not be enough

Identity and Access Management (IAM) systems are essential for enterprise security.

They help organizations authenticate users and workloads, manage permissions, and control access to resources.

These capabilities remain important for AI agents.

However, autonomous agents introduce additional requirements.

An agent might receive a task from a user, delegate part of that task to another agent, access several APIs, and make decisions based on information received during execution.

The security system needs to understand more than whether the agent possesses valid credentials.

It also needs to determine whether the requested action falls within the authority granted to that agent.

Existing IAM infrastructure can support these requirements, but developers may need additional agent-specific identity and authorization controls.

*1. Give every AI agent a verifiable identity
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The first step is making agents identifiable.

Instead of allowing multiple agents to operate under the same credentials, each agent should have a distinct identity that can be authenticated.

This helps organizations distinguish between agents, associate actions with specific identities, and apply appropriate permissions.

A verifiable agent identity also provides a foundation for secure agent-to-agent communication.

For example, when Agent A requests information from Agent B, the receiving system should be able to establish which agent initiated the request before deciding whether to authorize it.

Identity establishes who is requesting access. Authorization determines what that identity is allowed to do.

Both are necessary.

2. Delegate authority instead of granting unrestricted access

An AI agent rarely needs unlimited permissions.

Imagine an agent responsible for preparing a monthly financial report.

It may need permission to read selected financial records and generate a summary.

It probably doesn't need permission to modify bank account information or approve payments.

A better approach is to grant scoped authority based on the task.

For example:

• Agent: Financial Reporting Agent
• Authorized action: Read approved financial records
• Resource: Reporting database
• Permission duration: Until the reporting task expires
• Restricted actions: Payments, account modifications, and administrative changes

This is an illustrative policy, not a MudraID API configuration.

Scoped delegation helps reduce the potential impact of compromised agents, unexpected behavior, or overly broad tool access.

3. Verify authorization when actions happen

Authentication should not automatically provide unlimited authority for the rest of an agent's execution.

An agent's permissions may change while a workflow is running.

A user might withdraw authorization. A security administrator might revoke access. A particular action might require additional approval.

This makes request-time authorization especially important.

Before a sensitive operation is executed, the relevant system should evaluate the agent's identity, the requested action, the resource, and the authority currently granted.

Consider an agent that was previously authorized to retrieve customer records.

If its permission has been revoked, a later request should not succeed simply because the agent authenticated earlier.

Authorization must reflect the current security policy.

4. Make revocation part of the design

Revocation is often overlooked when developers focus on getting an AI-agent workflow running.

But what happens if an agent starts behaving unexpectedly?

What if a user cancels a task?

What if an agent's credentials are compromised?

Security teams need a reliable way to withdraw access.

Effective revocation also requires enforcement. Removing a permission from a database is not enough if downstream systems continue accepting previously issued authority.

Developers should consider credential lifetimes, cached authorization decisions, active sessions, and how quickly revoked permissions take effect.

5. Maintain accountability across agent workflows

When multiple agents participate in a workflow, investigating an unexpected action can become difficult.

Suppose one agent receives a request, another retrieves data, and a third performs an operation.

If something goes wrong, the organization needs to understand the sequence of events.

Useful audit information may include:

• Which agent initiated the action.
• Which identity or user delegated authority.
• What operation was requested.
• Which resource was accessed.
• Whether the request was authorized.
• When the activity occurred.

Auditability helps teams investigate incidents, understand agent behavior, and improve governance.

It should be designed into agent workflows rather than added only after a security incident.

Where MudraID fits

These are the kinds of challenges we're addressing with MudraID, an AI-agent identity and trust platform developed by Decryptogen.

MudraID focuses on providing identity and authorization infrastructure for autonomous agents, including:

• Verifiable agent identities.
• Secure authentication.
• Delegated authority and access control.
• Real-time verification.
• Access revocation.
• Agent activity monitoring and auditability.'

The objective is to help organizations establish clearer boundaries around what their AI agents can access and perform.

As enterprises adopt increasingly autonomous workflows, agent identity should become part of the security architecture from the beginning.

*Final thoughts
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Building capable AI agents is only part of the challenge.

Developers also need to consider how those agents are identified, what authority they receive, how their permissions are enforced, and what happens when access must be withdrawn.

These questions become especially important when agents interact with financial systems, customer information, administrative tools, or other sensitive resources.

The next generation of AI applications will need more than intelligent agents.

It will need agents that can be identified, authorized, controlled, and held accountable.

How is your team currently handling identity and delegated permissions for autonomous AI agents?

I'd be interested to hear what approaches developers are using.

Learn more about MudraID: https://mudraid.ai