Make Identity the Control Plane for Agentic AI
AI agents are identities. Govern them with discovery, least privilege, monitoring, short-lived credentials, and clear ownership-or risk outages, breaches, and audit gaps.
AI agents are identities. Govern them like you mean it.
An AI agent spins up in a CI/CD job with broad access and no expiration date. It was meant for a quick experiment; weeks later it's still active. It fails in production or its token leaks. Security starts digging and finds no audit trail, no owner, and no rollback plan. A shortcut becomes a security incident.
Contrast that with a mature approach to human access: every identity is discovered, scoped, time-bound, monitored, and easy to revoke. Governance isn't bolted on. It's built in. AI agents deserve the same discipline.
Why non-human identities matter
Modern environments run on containers, multi-cloud, CI/CD, and now autonomous AI systems. Non-human identities (NHIs)-service accounts, API tokens, and AI agents-make up the majority of access. Yet most programs are people-centric. Machine and agent identities sprawl, because they're easy to create and hard to track.
The result is identity debt at machine scale: excessive permissions, long-lived secrets, unrotated tokens, and no clear owner. That's an outage or breach waiting to happen-and a drag on audit readiness.
A practical maturity model for AI agents
Adopt AI agents, but do it with a maturity curve that balances speed with control. Use four stages:
- Visibility: Inventory every agent, token, and service account. Map where it runs, what it can access, and what it touches.
- Structured enablement: Standardize onboarding, scopes, and time-bound access. Enforce least privilege from day one.
- Operational governance: Apply policies, telemetry, and monitoring at scale. Alert on anomalies and drift. Prove it with reports.
- Autonomous action with control: Let agents act in real time, but only inside a trusted, auditable framework with safe rollback.
What happens if you delay
A supply chain agent reroutes shipments. Its OAuth token gets stolen. Attackers manipulate logistics at scale. A CI/CD agent has admin access. Its credentials leak. Your delivery pipeline is compromised. Then the key questions hit: Who created it? Who owns it? What breaks if we kill it? Without answers, response slows and accountability disappears.
Treat identity as data
Move from static access control to data-driven assurance. Build a living map of every non-human identity and how it behaves.
- Discovery and mapping: Enumerate agents, service accounts, and tokens across clouds, Kubernetes, and AI systems. Link identities to workloads, repos, and pipelines.
- Ownership: Tie each identity to a team and business service. No owner, no access.
- Behavioral monitoring: Detect unusual calls, privilege creep, or off-hours use. Compare to peer baselines.
- Lifecycle automation: Enforce provisioning from templates, short token TTLs, automatic rotation, and clean decommissioning.
90-day executive plan
- Days 0-30: Mandate an inventory of all NHIs and AI agents. Require owner, purpose, scopes, and expiry for each. Freeze creation outside approved workflows.
- Days 31-60: Standardize: least-privilege templates, default expirations, and owner-of-record fields. Route all new agents through CI/CD with audit logs.
- Days 61-90: Turn on monitoring and auto-revocation for stale or over-privileged identities. Pilot break-glass rollback for agents tied to critical systems.
Metrics that matter
- % of NHIs with a named owner, purpose, and expiry
- Median token TTL and rotation success rate
- % of agent permissions aligned to least-privilege templates
- Mean time to revoke (MTTR) for compromised identities
- Number of orphaned identities discovered per month
Operating model and accountability
- CISO: Policy, metrics, and audit evidence. Approves maturity goals.
- Platform/DevOps: Enforce templates, short-lived credentials, and workload identity. Owns runtime logging.
- App teams: Own each agent's purpose, scopes, and lifecycle. Fix drift within SLA.
- Procurement/Legal: Vendor reviews for third-party agents and tokens that touch core systems.
Tooling patterns that work
- Short-lived, federated credentials (e.g., OIDC between CI and cloud) instead of static keys
- Secrets managers and KMS; no secrets in code, images, or chat logs
- Workload identity for Kubernetes and serverless to avoid long-lived service accounts
- Policy-as-code for scopes and time limits; approvals via pull requests
- Centralized logs tied to identity, workload, and change requests
- Graph-based inventory to map identities to systems and owners
Compliance and standards
Regulators are asking for traceability, risk controls, and accountable ownership of AI systems. Align controls with established guidance and be ready to show evidence.
Upskill your leaders
If your team is rolling out agents across core workflows, make sure managers understand identity-first operations, monitoring, and safe rollout. Consider focused training and certification paths that connect AI adoption with security outcomes. Explore AI automation certification.
Bottom line
AI agents are non-human identities. Treat them that way. Build visibility, ownership, behavioral monitoring, and automated lifecycle controls into their design. Make identity the control plane so you can scale automation without inviting exploitation.
