The Problem: Why You Need to Act Now
Your agency's identity governance framework wasn't built for software that makes independent decisions. Traditional Identity, Credential, and Access Management systems handle human users, service accounts, and Non-Person Entities with static permissions. Agentic AI disrupts this model.
An AI agent can retrieve data, call application programming interfaces, deploy code, and initiate transactions without waiting for human approval at each step. It doesn't follow a fixed script. It chooses tools based on context, which means its access patterns shift dynamically. If you can't identify the agent, determine who owns it, understand its delegated authority, or reconstruct what it accessed, you're managing a privileged identity without governance controls.
The Cybersecurity and Infrastructure Security Agency highlighted these governance risks, warning that agentic AI creates expanded attack surfaces, privilege creep, behavioral risks, and gaps in activity records. The National Cybersecurity Center of Excellence published a draft concept paper exploring standards-based methods to identify agents, manage their permissions, and connect their actions to an accountable person or organization.
This isn't a future problem. If you're piloting generative AI tools, robotic process automation with decision logic, or autonomous workflow engines, you're already managing agentic identities. Here's how to bring them under governance.
What You Need Before Starting
Before you can govern AI agents, you need baseline identity hygiene:
An authoritative identity source. Maintain a single record for every user, device, workload, Non-Person Entity, and agent. This source defines the owner, purpose, privilege level, and review or expiration point for each identity. If you're still reconciling identities across Active Directory, cloud directories, and application-specific accounts, fix that first.
Automated provisioning and deprovisioning. Manual access requests and email-based approvals don't scale to agents that may be created, modified, or retired weekly. You need provisioning workflows that can assign, modify, and revoke access based on role changes, project completion, or security events.
Consistent policy enforcement across environments. Your access policies must apply uniformly whether the requester is a human in GovCloud, a Non-Person Entity in an on-premises enclave, or an agent calling a SaaS API. Fragmented policy engines create gaps agents will exploit.
Audit logging with correlation capability. You need centralized logs that capture authentication, authorization decisions, resource access, and actions taken. Those logs must include enough context to reconstruct an agent's decision path, not just its API calls.
A defined ownership model. Every agent must have an assigned owner who is accountable for its configuration, permissions, and behavior. This can't be the AI team as a group. It must be a named individual with authority to approve access changes and responsibility for incident response.
Step-by-Step Implementation
Step 1: Inventory Existing Autonomous Identities
Identify every software component that makes decisions or takes actions without per-transaction human approval. This includes:
- Robotic process automation bots
- Workflow orchestration engines
- API integrations with conditional logic
- Generative AI agents with tool access
- Machine learning models that trigger actions
For each, document what it does, which systems it accesses, who owns it, and what permissions it holds. If you can't answer those questions, the agent is already ungoverned.
Step 2: Define Agent Identity Attributes
Extend your identity schema to capture agent-specific attributes:
- Agent type (retrieval, action, generative, orchestration)
- Delegated authority source (which human or role authorized this agent)
- Approved tool set (which APIs, databases, or services it may call)
- Scope boundaries (which data classifications, projects, or environments it may access)
- Approval requirements (which actions require human confirmation)
- Review schedule (when permissions must be reassessed)
These attributes must be machine-readable. They'll drive your access policies.
Step 3: Implement Least-Privilege Scoping
An agent should never inherit a human user's full permission set. Instead:
- Create a dedicated Role-Based Access Control profile for each agent category
- Grant only the minimum permissions needed for the agent's defined mission
- Use attribute-based policies to further restrict access based on data sensitivity, time of day, or request context
- Require explicit approval before an agent can access CUI Basic, personally identifiable information, or systems processing Impact Level 2 or higher data
If your agent needs to act "on behalf of" a user, implement delegation tokens with narrow scope and short expiration. The agent's identity should remain distinct in all logs.
Step 4: Establish Monitoring and Behavioral Baselines
Configure your Security Information and Event Management or audit aggregation platform to:
- Capture every authentication and authorization decision involving an agent identity
- Log the specific resources accessed, actions taken, and data retrieved
- Record which human identity delegated authority and when
- Flag access attempts outside the agent's approved scope
Build behavioral baselines for each agent type. An agent that suddenly accesses new systems, retrieves unusual data volumes, or operates outside normal hours should trigger alerts.
Step 5: Define Lifecycle Management Procedures
Document procedures for:
- Provisioning: Who approves new agents, what security review is required, how initial permissions are scoped
- Modification: When permissions can be expanded, who authorizes tool additions, how scope changes are logged
- Review: How often agent permissions are reassessed, who conducts the review, what triggers an unscheduled review
- Deprovisioning: When agents are retired, how access is revoked, how long logs are retained
Automate these procedures where possible. Manual processes create delays that encourage teams to request overly broad initial permissions.
Step 6: Implement Incident Response Procedures
Prepare your incident response team to handle agent-related events:
- How to immediately suspend an agent's access
- How to reconstruct an agent's actions from logs
- How to determine whether an agent operated within its approved boundaries
- How to identify the responsible human when an agent causes harm
Test these procedures before you need them.
Validation: How to Verify It Works
Run these checks to confirm your agent governance is functional:
Identity Completeness Check: Can you produce a list of every agent identity in your environment, with owner, purpose, and permission scope? If your inventory is missing agents, your governance is incomplete.
Reconstruction Test: Select an agent action from your logs. Can you determine which human authorized the agent, what permissions it held at the time, whether the action was within scope, and which data it accessed? If not, your logging is insufficient.
Privilege Escalation Test: Attempt to expand an agent's permissions without following your documented approval process. If the change succeeds, your controls are bypassable.
Deprovisioning Test: Retire a test agent and verify its access is revoked across all systems within your defined timeframe. Orphaned agent credentials are high-value targets.
Cross-Environment Policy Test: Verify that an agent's access policies apply consistently whether it authenticates to a cloud service, an on-premises application, or a partner system.
Maintenance: Ongoing Tasks
Agent governance isn't a one-time implementation. Schedule these recurring tasks:
Quarterly Access Reviews: Review every agent's permissions against its current mission. Revoke access that's no longer needed. Reassess scope when an agent's model, tools, or integrations change.
Monthly Behavioral Analysis: Review anomaly alerts for agent identities. Investigate access patterns that deviate from baseline.
Continuous Log Monitoring: Monitor authentication failures, authorization denials, and out-of-scope access attempts in real time.
Policy Updates After Changes: When you deploy a new AI capability, add a data classification, or modify a system boundary, update your agent access policies before granting access.
Annual Framework Review: Reassess your agent identity schema, lifecycle procedures, and monitoring baselines as your use of autonomous software evolves.
Agencies that successfully adopt agentic AI will treat agent identities as privileged accounts requiring continuous governance, not invisible extensions of human users. Start building that discipline now.
