Machine Identity Management: Securing AI Agents Like You’d Secure Employees

AI agents are a new class of non-human identity. They differ from conventional workloads because they may dynamically select tools and make decisions about how to accomplish a goal.

Every production AI agent needs a unique identity, bounded authority, and an accountable owner. But governing an autonomous digital workforce requires more than traditional identity and access management. Imagine a new worker joining your organization. On the first morning, the worker receives access to customer records, internal documents, and several business applications. No one creates an employee profile. No manager is named. The worker borrows a colleague’s login credentials and begins completing tasks under that person’s identity.

Months later, the worker is still active. Its permissions have quietly expanded. It has accumulated API keys, scheduled jobs, and connections to additional systems. When it changes a customer record unexpectedly, the audit log shows only the name of the employee whose credentials it borrowed.

No security team would approve that arrangement for a human employee. Yet it bears an uncomfortable resemblance to how some organizations deploy AI agents.

As AI agents move beyond answering questions and begin interacting with enterprise systems, identity becomes one of the most important parts of the architecture. An agent that can retrieve confidential data, invoke tools, or modify business records is no longer merely a model generating text. It is an actor inside the enterprise.

If an agent can act, it must also be identifiable, governable, and accountable.

Traditional generative AI applications largely operated within a narrow boundary. A user submitted a request, the model produced a response, and the interaction ended.

AI agents cross that boundary. They can gather information from several systems, decide which tools to use, and take a sequence of actions to complete a goal. Depending on how they are designed, agents may update records, deploy code, approve routine transactions, or communicate with customers.

SPR has described this broader shift as AI moving from an assistant that generates information to an operator working inside enterprise tools. That shift matters because the more an AI system can do, the more precisely an organization must govern who or what is doing it.

It is also important to distinguish an agent from conventional automation. An automated workflow generally follows a predetermined path. An agent may choose its path according to the goal, available tools, and information it discovers along the way. The distinction between an AI agent and AI automation is therefore not merely semantic. It changes the security and governance model.

That added agency creates a familiar but newly urgent set of questions:

• What is this agent?
• Who owns it?
• What is it allowed to access and perform?
• Is it acting on its own authority or for a particular employee?
• Who is responsible for the outcome?
• Can its access be revoked immediately?
• Are its actions logged, monitored, and auditable?
• Are its permissions reviewed and recertified periodically?
• How is it authenticating?

These are identity and access management questions.

The National Cybersecurity Center of Excellence, part of NIST, is exploring how established identity principles such as identification, authentication, and authorization should apply to software and AI agents. Its Software and AI Agent Identity and Authorization initiative highlights issues including agent identification, authorization, delegated access, logging, data provenance and the ability to connect agent activity to human authorization.

Enterprise identity platforms are also beginning to introduce constructs designed specifically for agents. For example, Microsoft Entra Agent ID provides specialized identities intended to authenticate, authorize, and govern AI agents rather than forcing them into identity models built solely for people or conventional applications.

The technology will continue to evolve, but the architectural direction is clear: production agents cannot remain invisible behind shared credentials and loosely governed integrations.

The employee analogy offers a useful starting point. Before a person joins an organization, someone determines the role that person will fill. The employee receives an identity, a manager, and access suited to the job. Permissions change when responsibilities change. When the employee leaves, access is removed.

AI agents need a comparable lifecycle:

1. Discover: Identify agents already operating or being introduced into the environment.

1. Register and classify: Classify its risk and document its purpose and capabilities.
2. Assign ownership: Document business and technical ownership.
3. Provision: Give the agent a distinct governable identity.
4. Authorize: Grant limited access based on the intended task and operating context.
5. Monitor: Monitor activity and periodically recertify its access.
6. Retire: Revoke its credentials, permissions, delegated authority, and connections when it is retired.

Discovery deserves particular attention. Agents may be introduced by central technology teams, configured by business units inside SaaS platforms, embedded in third-party products, or created experimentally by developers. An organization cannot govern an agent it does not know exists.

But an AI agent is not simply a digital employee. A human worker generally performs tasks at human speed. An agent can make hundreds of tool calls, traverse several systems, and initiate downstream processes in minutes. It can also be duplicated, embedded in other workflows, or allowed to create and delegate tasks to additional agents.

Agents may require tighter controls than employees because their speed and reach can magnify both useful work and damaging mistakes. Security therefore needs to be part of designing and scaling production-grade AI agents, not a separate layer added after an agent has already entered the business.

The first principle is simple: an agent should not operate as an anonymous process, a shared service account, or an indistinguishable extension of an employee.

It needs an identity that lets the enterprise determine which agent is making a request and whether that agent is trusted to make it.

The identity record should contain more than a display name. Beneficial attributes may include:

• Business and technical owners
• Approved purpose and operating environment
• Agent or application version
• Permitted tools and data classifications
• Expected degree of autonomy
• Review and expiration dates
• Parent, child, or collaborating agent relationships
• Identity provider and credential or token type

The precise identity model will depend on the architecture. Some organizations may distinguish between an approved agent design and each runtime instance created from it. Others may tie identity to a deployed workload or a specific business capability.

The goal is not to force every agent into the same schema. It is to make the agent discoverable and accountable throughout its life.

Without that distinction, one compromised or poorly configured credential can expose every workflow that shares it. It also becomes difficult to suspend one agent without interrupting other systems that rely on the same account.

A useful identity model should let the organization answer four questions without searching through code repositories or interviewing the agent’s original developer:

• What is this agent?
• Why does it exist?
• Who is responsible for it?
• What is it currently permitted to do?

Many agents do not act entirely on their own. They perform work for an employee, customer, another application, or even another agent. That does not mean the agent should impersonate the user.

Suppose an employee asks an agent to update a customer’s mailing address. The activity record should preserve two facts:

• The employee who initiated and authorized the task.
• The agent that performed the action.

If the agent simply uses the employee’s credentials, the business loses that distinction. The log may show that the employee accessed a record, called an API, or made a change, even though the agent selected and executed the action.

A better authorization model considers both identities. The agent must be allowed to use the tool; the user must be allowed to request the business action; and the particular task must fall within the boundaries of the delegation.

Conceptually, effective authority should be no broader than the intersection of user authority, agent authority, delegated task scope, and contextual policy. The employee’s full access should not pass automatically to the agent. Someone who may view an entire customer database might reasonably ask an agent to review one account, but that does not mean the agent should receive permission to export every record.

Delegated authority should be narrower than the user’s total authority.

The audit trail should preserve the full chain: which user initiated the work, which agent carried it out, what authority was delegated, and where the delegation ended.

Multi-agent systems extend the same problem. A request may move from a user to one agent, from that agent to a specialist agent, and then to a business tool or API. Each handoff should preserve the original principal and constrain the authority passed downstream.

An agent should not be able to delegate more authority than it possesses or silently expand the scope of the original request.

The objective is to preserve a verifiable chain such as: user → coordinating agent → specialist agent → tool or resource. Without that chain, organizations may know which API was called while losing sight of whose authority ultimately permitted it.

Traditional least privilege grants a person or application only the access required for its role.

That remains necessary for agents, but it may not be sufficient.

An agent’s authority may need to change according to the user, the task, the workflow stage, and the consequence of the requested action. Its permissions should be:

• Role-bound: Limited to the agent’s approved purpose.
• Task-bound: Restricted to the work currently being performed.
• Time-bound: Available only for the necessary duration.
• Context-bound: Adjusted according to the user, data, environment, and risk involved.

Consider an accounts-payable agent. It may need to read an invoice, retrieve a purchase order and compare the two. That does not mean it needs permanent access to issue payments, alter supplier banking information, or browse unrelated financial records.

Even when the agent can technically complete the entire process, its authority can be divided. It might collect and validate the information independently, then require stronger authorization before initiating a payment.

This extends the layered approach organizations already need for securing LLM applications. Model guardrails, content filters, and input validation remain valuable, but the permissions surrounding the model determine how far an error or manipulation can travel.

Least privilege for an AI agent should not be a broad role assigned once and forgotten. It should be a continually enforced boundary around the agent’s current task. The agent’s ability to reason about an action does not automatically give it the authority to perform that action.

Long-lived API keys are convenient during a proof of concept. They are also easy to forget. A key created by one developer may remain active after the pilot ends. It may be copied into configuration files, shared across environments, or reused by agents with different purposes. Over time, no one may be certain which systems still depend on it.

Production agents should use centrally governed workload credentials wherever possible. Credentials should be short-lived, rotated automatically, and limited to the intended resource. Security teams should be able to suspend an agent without hunting through application code or manually disabling a collection of keys.

Mature cloud environments already use centralized security controls, logging, IAM analysis, and permission boundaries to reduce the risk of fragmented account management. Agent architecture should build on those practices rather than creating a new collection of unmanaged secrets beside them.

Workload-identity standards can provide part of this foundation. SPIFFE, the Secure Production Identity Framework for Everyone, defines open standards for securely identifying software systems across dynamic and heterogeneous environments. Established protocols such as OAuth and OpenID Connect can also support authentication and delegated authorization, depending on the architecture.

The objective is not to adopt every available protocol. It is to avoid building agent security around permanent secrets that cannot be traced, narrowed, or quickly revoked.

Not every agent action needs a person standing over it. Requiring approval for each minor step would erase much of the efficiency agents are meant to provide.

At the same time, granting broad autonomy simply because an agent usually behaves correctly creates unnecessary risk.

Approval requirements should follow the potential consequence of an action. An agent might safely categorize a support request, retrieve an approved document, or draft a response without human intervention. A different threshold should apply before the agent transfers money, deletes infrastructure, changes account permissions, or discloses sensitive information.

Organizations should consider several factors:

• Is the action reversible?
• Could it expose regulated or confidential data?
• Could it interrupt a production system?
• Does it create a financial or legal obligation?
• How many people, records, or systems could be affected?

The greater the consequence, reach, or irreversibility, the stronger the authorization should become.

This is also where identity controls support broader defenses against prompt injection and goal manipulation. Input filtering and model guardrails remain important, but no prompt-defense technique should be the only barrier between a manipulated agent and a high-impact tool.

SPR’s examination of the OWASP Agentic Top 10 describes how excessive agency, insecure tool use, and manipulated goals can create risks across an agentic system. Machine identity management addresses the next question: even if an agent is misled, compromised, or simply wrong, what is it actually permitted to do?

Strong authorization reduces the blast radius when another defense fails.

Agent observability is often discussed as a way to understand reasoning, model behavior, and application performance. Identity introduces another requirement: reconstructing the authority behind an action. A useful audit record should show:

• Which agent acted
• Which user or system initiated the task
• What goal or request the agent received
• Which identity and authorization policies were applied
• What authority was delegated and by whom
• What data and tools the agent accessed
• Whether a person approved a sensitive step
• What action occurred and what outcome followed

Logging the final API request is not enough. It may reveal what changed without explaining why the agent was able to change it.

The organization should be able to follow the chain from the original request through delegation, policy evaluation, tool invocation, and business outcome. That chain becomes essential during a security investigation, a compliance review, or an examination of why an agent made an unexpected decision.

Auditability is not merely recording what the agent touched. It is preserving the chain of authority that allowed it to act.

Logs should also distinguish between an action the agent considered, one it attempted, one that was blocked, and one that was successfully completed. Without that separation, organizations may mistake a blocked request for a completed change or overlook repeated attempts to cross an authorization boundary.

Most organizations already maintain inventories of employees, applications, and cloud assets as part of their cloud governance and security programs. They will need similar visibility into AI agents.

An enterprise agent registry can document:

• Where the agent runs
• Who owns and supports it
• Its approved purpose and risk classification
• What systems it can reach
• What permissions it currently has or can activate
• What data classifications it can access
• Which identity provider or credential type it uses
• Whether it acts independently or for which users it acts for
• What other agents or services it depends on
• When its access was last reviewed
• Whether it is active, suspended or retired

This becomes especially important as agents emerge from several directions. Central technology teams may build some. Business units may configure others inside software platforms. Third-party products may introduce embedded agents with their own connections and permissions.

Without a registry, organizations may discover agents only when one appears in an incident report, an access review or an unexpected cloud bill.

Ownership should not fall entirely to the security team. A production agent needs a business owner accountable for its purpose and outcomes, along with a technical owner responsible for deployment, access, monitoring and maintenance.

Security establishes the guardrails. It should not become the unnamed manager of every digital worker in the company.

An agent’s permissions should be reviewed whenever its capabilities or responsibilities materially change. Adding a new tool, connecting a new data source, increasing its autonomy, or allowing it to perform more sensitive actions may introduce risks that were not considered when the agent was originally approved.

These changes should trigger a review of the agent’s access. The organization should determine whether existing permissions are still appropriate, whether new permissions are necessary, and whether additional controls or approvals are required.

Access should also be reviewed periodically to confirm that the agent still has a valid business purpose, its owners remain responsible for it, and its permissions remain appropriate. Access that is no longer required should be removed.

Normal offboarding is not enough when an agent is suspected of compromise, misuse, or uncontrolled behavior. Organizations need a rapid containment path, a practical kill switch for the agent’s authority.

Depending on the architecture, containment may require disabling the agent identity, revoking active sessions or tokens where supported, denying authorization, removing tool access, stopping scheduled executions, and preventing downstream agents from continuing delegated work.

The goal is not merely to stop the user interface or application process. It is to remove the agent’s ability to act across every connected system as quickly as the risk requires.

Turning off an agent’s primary application does not necessarily retire it. The agent may still have API credentials, scheduled processes, stored tokens or access through connected tools. Copies may exist in test environments. Child agents may continue operating after the parent workflow disappears.

A complete retirement process should revoke credentials, remove permissions, terminate scheduled work, and update the agent registry. It should also identify downstream dependencies before access disappears.

The organization should determine whether the agent created additional resources, delegated tasks, or stored information that must also be removed or reassigned. Retirement may require more than deleting one application registration.

The central question is not whether the agent has stopped responding. It is whether its authority has been removed from every place it once operated.

Organizations do not need to wait for every agent-identity standard to mature before improving their controls. Begin with each production agent that can access enterprise data or invoke a business tool:

1. Does the agent have a unique and discoverable identity?
2. Are both its business owner and technical owner documented?
3. Can we distinguish the agent’s actions from those of the user it serves?
4. Are its permissions limited by task, time and operating context?
5. Can we reconstruct why it was authorized to take each sensitive action?
6. Can we revoke its credentials and connected access immediately?

Any unanswered question represents a governance gap. It may also reveal that an experimental agent has quietly become production infrastructure without receiving production-grade identity controls.

The conversation around AI security often begins with the model: whether it can be manipulated, whether its answer is accurate, and whether its guardrails will hold.

Those concerns matter, but agents introduce a more fundamental architectural question: Who is acting inside your systems?

A secure enterprise should never have to answer with a shared username, a forgotten API key, or the name of an employee who did not personally perform the work. AI agents need identities of their own. They need intentionally bounded authority, oversight that reflects the consequences of their actions, and owners who remain accountable for what they do.

The organizations that establish those controls now will be better prepared to expand agent autonomy later. Those that do not may find themselves surrounded by capable digital workers whose access no one can fully explain and whose departure no one knows how to complete.