What are AI agents and machine identities, and how should they be managed?

What are machine identities?

Machine identities are digital identities used by systems, applications, services, and automated processes to authenticate and access resources.

They are often called non-human identities because they do not belong to a person. Instead, they are assigned to something that needs to communicate, integrate, or perform work on behalf of a system, process, or organization.

Common examples include:

• Service accounts
• API keys
• OAuth tokens
• Certificates
• Cloud workload identities
• CI/CD pipeline identities
• Bots, scripts, and automation tools
• Application integrations
• AI agents that authenticate or act across systems

In practice, machine identities are what allow modern digital environments to function. They help applications talk to each other, enable automation, and support cloud-based operations.

What are AI agents?

AI agents are AI-driven identities that can perform tasks, use tools, retrieve information, and take action within a defined scope.

They can interact with applications, APIs, data sources, and workflows to support business or technical processes. For example, an AI agent may search internal documentation, summarize customer data, update a ticket, generate code, call an API, or trigger a workflow.

To do this, the agent needs access rights.

These rights can be provided through APIs, service accounts, delegated user permissions, tokens, or integrations with internal systems. From an identity security perspective, this means an AI agent is not just an AI feature; it also functions as an identity that needs to be governed and managed.

Why AI agents and machine identities create security risks

Machine identities are not new. But with cloud services, SaaS platforms, DevOps, automation, and AI, the number of non-human identities has grown significantly.

The main risk is that many are created for practical reasons, but not always governed properly afterward. AI agents add to this challenge because they may be able to choose tools, retrieve information, or initiate actions across systems.

This can lead to several risks:

Too much access

Machine identities are often given broad permissions to make integrations, scripts, or automations work. The same can happen with AI agents if they are connected to too many tools, systems, or datasets from the start.

Unclear ownership

Human users typically have a clear manager, department, and employment status. Machine identities often do not. For AI agents, ownership may also sit somewhere between IT, security, data, and the business function that is using the agent.

Long-lived credentials

API keys, secrets, certificates, and tokens can remain valid for months or even years. If they are exposed or forgotten, they can be exploited without being subjected to the same controls and monitoring as human logins.

Limited visibility

Many organizations lack a complete overview of where machine identities are used, what they can access, and whether they are still needed. For AI agents, visibility should also cover which tools they can use, what data they can access, and which actions they are allowed to perform.

How to manage AI agents and machine identities

Managing AI agents and machine identities is both a technical task and an identity governance challenge.

The goal is to make sure every non-human identity has a clear purpose, limited access, accountable ownership, secure credentials, and a defined lifecycle.

1. Create visibility

Map which machine identities exist across your environment, including service accounts, API keys, cloud identities, automation tools, integrations, and AI agents. Identify where they are used, which systems and data they can access, who owns them, and whether they are still needed.

2. Assign ownership

Every machine identity should have a clearly defined owner. This could be an application owner, system owner, product team, platform team, or business function. For AI agents, ownership should cover both the business process the agent supports and the technical systems it connects to.

3. Apply least privilege

Machine identities should only have the access they need to perform their specific task. If an AI agent only needs to retrieve or summarize information, it should not be able to change records, trigger workflows, or access unrelated systems.

4. Secure credentials and secrets

API keys, secrets, certificates, and tokens should be stored securely, rotated regularly, and removed when no longer needed. Your organizations should avoid hardcoded secrets, shared credentials, and unmanaged keys.

5. Include them in governance

Access governance should also cover machine identities. They need to be part of access reviews, recertifications, policy checks, and lifecycle processes, especially when they have privileged access or access to sensitive data.

6. Monitor behavior

Monitor machine identities based on what they normally do, such as API activity, access patterns, failed authentication attempts, and unusual permission use. For AI agents, monitoring should also cover which tools they use, what data they access, and which actions they perform.

Where to start

Start with the identities that create the highest risk, such as privileged service accounts, API keys for sensitive systems, cloud workload identities, CI/CD credentials, and AI agents with access to critical data or production systems.

A practical starting point is to ask:

• Which machine identities do we have?
• What systems do they access?
• Who owns them?
• What permissions do they have?
• Are their credentials secure?
• Are they included in access reviews?
• Can we monitor what they do?

You do not need to solve everything at once. Start with visibility, ownership, and high-risk identities, then extend governance across applications, cloud environments, automation tools, and AI agents.