Agent Identity: 5 Types, 7 Security Risks, and 6 Best Practices

What Is Agent Identity?

Agent identity is the set of attributes and credentials that uniquely identify an AI agent when it interacts with systems, services, or other agents. It establishes which agent is making a request and provides the information needed to determine what that agent is allowed to do.

An agent identity can include a unique identifier, cryptographic credentials, assigned roles, permissions, and information about the agent’s owner or workload. Unlike a human identity, it often represents software that operates autonomously and may create many short-lived sessions or processes.

This is part of a series of articles about agentic AI security

Why Is Agent Identity Important? 

Attribute Actions to the Correct Agent

Each agent needs a distinct identity so systems can determine which agent performed an action. This makes logs and audit trails useful for tracing API calls, data changes, tool usage, and other operations back to a specific agent. Without this attribution, troubleshooting incidents and investigating unauthorized actions becomes difficult.

Clear attribution is especially important when several agents use the same tools or operate on the same data. Teams can identify which agent accessed a resource, what operation it requested, and when the request occurred. This information supports security investigations, compliance audits, debugging, and monitoring of unexpected agent behavior.

Control What Each Agent Can Access

Agent identity provides the basis for authorization. Systems can assign permissions according to an agent’s role and required tasks, such as allowing one agent to read customer records while another can update deployment settings. Separate identities support least-privilege access and make it possible to change or revoke one agent’s permissions without affecting others.

This separation also limits the impact of a compromised or malfunctioning agent. An agent that only needs read access should not receive credentials that allow it to modify or delete data. Access policies can also consider additional identity attributes, such as the agent’s workload, environment, owner, or requested resource.

Prevent Credential Sharing and Identity Ambiguity

When multiple agents share the same API key, service account, or other credential, the receiving system cannot reliably distinguish between them. Giving each agent its own identity removes this ambiguity and reduces the impact of credential compromise. Credentials can be rotated or revoked for one agent without interrupting every agent that uses the same service.

Shared credentials also make access policies harder to enforce because every agent using the credential effectively receives the same permissions. Individual agent identities allow systems to authenticate and authorize each agent separately. This becomes increasingly important as organizations deploy larger numbers of agents across applications, environments, and infrastructure.

Agent Identity vs. Human Identity 

Human identity represents a person interacting with a system. It is typically tied to a long-lived account and verified through methods such as passwords, passkeys, security keys, or multi-factor authentication. Permissions are then assigned based on the person’s role, group membership, and organizational policies.

Agent identity represents software acting autonomously or on behalf of a user, application, or organization. Unlike human users, agents may run continuously, operate at machine speed, and interact with many APIs and tools without direct human involvement. Their identities therefore need authentication methods designed for machine-to-machine communication, such as workload identities, certificates, signed tokens, or short-lived credentials.

The lifecycle is also different:

• A human identity may remain active for years, while agent instances can be created and destroyed frequently or run only for a particular task. 
• Agent identity systems need to handle this dynamic lifecycle without relying on manually issued, long-lived secrets.

Agent identity also needs to distinguish the agent from the person or service that authorized it. For example, a system may need to record both that an agent changed a database entry and that the agent was acting on behalf of a particular user. Keeping these identities separate improves authorization, auditing, and accountability.

Related content: Read our guide to non-human identity

Types of Agent Identity

1. Platform-Native AI Agent Identities

Platform-native ai agents operate inside a cloud, AI platform, or software ecosystem. Their identities are usually created and managed through the platform’s existing identity and access management system. The agent may receive:

• A service identity
• A workload identity
• A managed identity
• A similar machine credential

This approach lets organizations apply existing authentication and authorization controls to agents. For example, an agent can be granted permission to invoke selected models, access a data store, or call approved services without receiving broad access to the entire platform.

2. Enterprise Application Agents

Enterprise application agents work with business systems such as CRM, ERP, document management, and collaboration platforms. They may:

• Retrieve customer information
• Update records
• Generate reports
• Perform actions on behalf of employees

These agents often need identities that preserve both the agent’s identity and the identity of the user or application authorizing the task. This distinction helps systems enforce user-specific permissions and record whether an action was performed directly by a person or through an agent acting on that person’s behalf.

3. Autonomous Workflow Agents

Autonomous workflow agents coordinate multi-step processes with limited human involvement. An agent might retrieve data from one service, analyze it, make a decision, and trigger an action in another system. A single workflow can involve several APIs, databases, and other agents.

Their identities need permissions that remain valid across these interactions without granting unnecessary access. Short-lived credentials and scoped authorization can limit what the agent can do at each stage. Systems should also retain the agent’s identity throughout the workflow so its actions remain traceable.

4. Coding and Development Agents

Coding and development agents can read source code, modify files, run tests, create branches, or interact with repositories and development infrastructure. Depending on their permissions, they may also access:

• Package registries
• CI/CD systems
• Cloud environments
• Deployment tools

A dedicated identity makes it possible to restrict these capabilities and distinguish agent-generated changes from developer actions. For example, an agent might receive permission to create a pull request but not merge it into a protected branch. This separation also makes agent activity easier to review in repository and deployment logs.

5. Security AI Agents

Security AI agents analyze alerts, investigate suspicious activity, query security data, and sometimes perform response actions such as isolating endpoints or disabling access. These tasks can require access to sensitive telemetry and privileged security controls.

Because their actions can affect production systems and user access, security agents require tightly scoped identities. Permissions should reflect the exact response actions an agent is authorized to perform. Separate identities also allow security teams to audit automated decisions, revoke an agent’s access, and distinguish automated responses from actions taken by human analysts.

Common Agent Identity Security Risks 

Shared or Inherited Credentials

Agents sometimes reuse API keys, service accounts, or credentials originally issued to users or applications. This makes it difficult to determine which agent performed an action and often gives the agent more access than it needs.

Shared credentials also increase the impact of a compromise. If one agent exposes a credential used by several workloads, an attacker may gain access to every system that trusts it. Assigning separate identities and credentials to agents limits this risk and improves auditability.

Excessive Agent Privileges

Agents may receive broad permissions because they need to interact with several tools, APIs, or data sources. These permissions can include capabilities that are unnecessary for the agent’s actual tasks, such as write access when it only needs to read data.

Excessive privileges increase the potential impact of errors, malicious instructions, or credential theft. Agent permissions should follow least-privilege principles and be scoped to specified resources, actions, and environments whenever possible.

Orphaned Agent Identities

Agent identities can remain active after an agent, workflow, experiment, or application is no longer used. These orphaned identities may retain valid credentials and permissions even though nobody actively manages them.

Attackers can exploit forgotten identities because their activity may be less likely to attract attention. Organizations should track identity ownership and lifecycle state, then disable or remove identities when their associated agents are retired.

Compromised Agent Credentials

Agent credentials can be exposed through source code, configuration files, logs, insecure storage, or compromised runtime environments. Once obtained, an attacker may use the credential to authenticate as the agent and access the same resources.

Short-lived credentials reduce the period in which stolen credentials remain useful. Secure secret storage, credential rotation, runtime protection, and monitoring for unusual authentication activity provide additional layers of protection.

Related content: Read our article about compromised credentials

Unauthorized Tool or API Access

Agents frequently use external tools and APIs to complete tasks. Weak authorization controls can allow an agent to call tools it was not intended to use or perform sensitive operations beyond its assigned role.

Tool access should therefore be tied to the agent’s identity rather than controlled only by application logic or prompts. Policies can restrict which APIs, operations, resources, and data an agent can access, with stronger controls for high-impact actions.

Agent Impersonation

An attacker or malicious workload may attempt to impersonate a trusted agent to gain its permissions. This can occur through stolen credentials, forged tokens, weak authentication, or identity configuration errors.

Strong authentication should allow receiving systems to verify that requests genuinely come from the expected agent or workload. Cryptographic credentials, signed tokens, workload attestation, and short credential lifetimes can make impersonation more difficult.

Agent-to-Agent Trust Abuse

Multi-agent systems often allow agents to exchange information, delegate tasks, or request actions from one another. If agents automatically trust these requests, a compromised or malicious agent may use another agent’s permissions to reach resources it cannot access directly.

Each agent-to-agent request should therefore be authenticated and authorized independently. Systems should validate the requesting identity, requested action, and delegation context rather than assuming that another agent is trustworthy simply because it operates within the same environment.

Agent Identity Security Best Practices 

Here are some of the ways that organizations can improve their security when using AI agents.

1. Assign Every Agent a Unique Identity

Each agent should have its own identity rather than sharing a user account, API key, or service account with other agents. Unique identities make authentication and authorization decisions specific to the agent performing the action. They also prevent one agent from automatically inheriting another agent’s permissions.

This separation improves accountability. Security teams can trace activity to a particular agent, revoke its access independently, and investigate abnormal behavior without disrupting unrelated workloads. Identity records should also include ownership and purpose so administrators know which team is responsible for each agent.

Where possible, agents should use short-lived, automatically issued credentials instead of static secrets. This reduces the risk associated with leaked credentials and makes credential rotation easier to manage at scale.

2. Maintain an Inventory of AI Agents

Organizations need an up-to-date inventory of agents operating across their environments. The inventory should record information such as the agent’s owner, purpose, identity, permissions, credentials, connected tools, data sources, and deployment environment.

Agent discovery should cover both centrally managed deployments and agents created by individual teams. This is important because agents can appear in development environments, automation platforms, cloud services, and third-party applications without going through a central deployment process.

The inventory should be connected to identity and asset management processes where possible. Regular reconciliation can identify unmanaged agents, duplicate identities, excessive permissions, missing owners, and agents that no longer have a valid business purpose.

3. Apply Least Privilege to Agent Permissions

Agents should receive only the permissions required to complete their assigned tasks. Access can be limited by resource, operation, environment, data type, and duration rather than granting broad access to an entire service.

Permissions should also account for what an agent can do through connected tools. For example, an agent that needs to inspect repository code may require read access but not permission to merge changes, modify CI/CD settings, or deploy applications. High-impact operations can require additional authorization or human approval.

Organizations should review permissions when an agent’s role or connected tools change. Just-in-time and temporary access can further reduce persistent privileges by granting sensitive permissions only when the agent needs them.

4. Monitor Agent Behavior Continuously

Authentication confirms an agent’s identity, but it does not guarantee that every subsequent action is safe. Organizations should monitor agent activity for unusual API calls, unexpected data access, privilege changes, abnormal request volumes, and attempts to use unauthorized tools.

Monitoring should establish expected behavior for each agent and generate alerts when activity deviates significantly from that baseline. Useful signals can include authentication events, tool calls, resource access, permission failures, data transfers, and changes to security-sensitive resources.

Detailed identity-aware logs help security teams determine whether unusual behavior resulted from an error, compromise, malicious input, or an authorized change. Monitoring can also trigger automated controls, such as restricting an agent’s access or requiring approval before allowing further sensitive actions.

Related content: Read our guide to behavior anomaly detection

5. Correlate Agent Activity with Human Identity

When an agent acts on behalf of a person, audit records should preserve both identities. Recording only the user’s identity hides the fact that an agent executed the action, while recording only the agent makes it harder to determine who initiated or authorized the task.

Maintaining this relationship provides a clearer chain of accountability. Systems can evaluate the agent’s permissions together with the user’s permissions and record who requested, approved, and executed sensitive operations.

Delegation should also have clear boundaries. An agent acting for a user should not automatically gain every permission available to that user. Systems can restrict delegated access to the resources and actions required for the requested task.

6. Review and Revoke Unused Agent Identities

Agent identities should be reviewed throughout their lifecycle rather than remaining active indefinitely. Reviews can identify agents that are no longer running, have changed purpose, belong to discontinued projects, or retain permissions they no longer require.

Unused identities should be disabled or removed, and associated credentials, tokens, certificates, and access grants should be revoked. Related permissions in external tools and third-party services should also be removed so that retiring the primary identity does not leave another access path active.

Organizations can use expiration dates and automated lifecycle policies so temporary agent identities do not remain active after their intended use ends. Periodic access reviews should also verify that active agents still have a valid owner, purpose, and appropriate permission set.

Securing AI Agent Identities With Exabeam Agent Behavior Analytics

Exabeam Agent Behavior Analytics (ABA) gives security teams visibility into AI agent activity across the digital workforce. ABA establishes behavioral baselines for AI agents and autonomous workflows, then detects misuse, drift, abnormal tool usage, risky access, and actions outside an agent’s intended role. It monitors major AI platforms and open telemetry sources, correlates agent activity with users, entities, and applications, and helps teams identify emerging agent risk before it impacts operations.

Key capabilities of Exabeam Agent Behavior Analytics:

• Behavioral detections for AI agents: Builds baselines for agents and autonomous workflows to detect autonomous installation and execution, track tool usage and MCP activity, and identify activity outside expected roles.
• Coverage for any agent: Analyzes activity from Claude Enterprise, ChatGPT, Google Gemini, Microsoft Copilot, GitHub Copilot, and custom agents. Observra standardizes agent-native telemetry using a Common Information Model (CIM), while Exabeam Agent Sensor collects and normalizes activity across models, agents, and runtime environments.
• Automated analysis of agent activity: Machine-built timelines correlate agent actions with related user, entity, and application activity. Exabeam Nova summarizes threats, explains behavioral context, and recommends next steps while analysts remain in control.
• Customizable detection rules: ABA detections are available in Threat Detection Management, where teams can review, clone, tune, and customize them. Exabeam Nova Rule Creator generates behavioral models and correlation rules from natural language and converts Sigma and YARA rules into Exabeam detections.
• Measurable AI security posture: Outcomes Navigator maps ABA coverage to the OWASP Agentic Top 10, identifies gaps in data and detections, and uses the Exabeam Nova Advisor Agent to provide prioritized recommendations that improve readiness.
• Flexible deployment at enterprise scale: Add ABA to an existing SIEM with New-Scale Analytics, or deploy New-Scale Fusion for integrated detection, investigation, and response, with behavioral detections across human and non-human identities.
• Pioneering open-source AI agent security: Exabeam sponsors and contributes to Praxen for pre-deployment Agent Behavior Verification (ABV) and Observra for normalized, framework-agnostic runtime telemetry. Together, these projects establish a full-lifecycle framework to verify, observe, and secure AI agents.

Learn more about Exabeam Agent Behavior Analytics