How can identity teams govern legacy accounts and AI agent accounts under the same model?

Use one inventory, one ownership model and one evidence record for both. The difference is that AI agent credentials may be short-lived and harder to observe, so the governance process must rely on authoritative system events rather than periodic snapshots alone.

Why a Shared Governance Model Works

Identity teams do not need separate operating models for legacy accounts and AI agent accounts, because both are governed by the same fundamentals, ownership, approved purpose, entitlement review, and auditable evidence. The practical difference is that AI agent access can change faster and may exist for shorter windows, so governance has to be event-driven and tied to authoritative system signals. That makes the model stricter, not different, when it comes to accountability and control.

This matters because the failure mode is usually governance drift, one set of controls for humans and another, weaker set for machine-run activity. A single model reduces exceptions, makes reviews comparable, and gives security, compliance and operations one source of truth. Current guidance suggests the biggest improvement comes from treating account type as a lifecycle attribute, not as a separate policy universe. In practice, teams usually discover the gap only after an automated account has already exercised access that no one can clearly explain.

One useful reference point is that only 52% of companies can track and audit the data their AI agents access, leaving many teams without reliable evidence for review and investigation. That is exactly why the governance record has to be shared across account classes. AI Agents: The New Attack Surface report

How to Operationalise One Model Without Losing Control

The model should start with one inventory that records account owner, business purpose, system scope, approval basis, privilege set, review cadence and revocation path. Legacy accounts often have stale ownership or long-lived privileges, while AI agent accounts may be provisioned for a task, workflow or tool chain and then retired quickly. The governance rule is to treat both as governed identities, but to adapt the evidence source to the account’s behaviour. For human-managed legacy accounts, periodic recertification and change records are often sufficient. For AI agent accounts, authoritative events from the identity platform, workflow engine, token service, vault, or target system are more important than point-in-time screenshots.

A practical operating pattern is:

Use one control plane for naming, ownership, approval and exception tracking.
Tag each account with type, environment, delegate authority and expiry conditions.
Require evidence of creation, privilege grant, use, rotation and revocation.
Prefer event logs over manual attestations when an AI agent can self-initiate actions.
Escalate any account that cannot be tied to a named owner or a current business purpose.

That approach aligns governance with what can actually be observed, rather than with how the account was originally classified. It also helps when one AI agent spawns multiple runtime credentials, because the inventory can still map them back to one accountable service or workflow. NIST Cybersecurity Framework 2.0 and OWASP Top 10 for Agentic Applications 2026 both reinforce the need for visibility, governance and misuse-resistant controls around autonomous access. These controls tend to break down when teams rely on monthly review cycles for credentials that may exist for minutes, not months.

Where the Model Breaks Down and What to Watch

Tighter governance often increases operational overhead, so organisations have to balance stronger evidence requirements against the need for fast provisioning and short-lived access. The edge cases are usually not about the account label, but about authority, duration and observability. A legacy account with broad standing privilege can be riskier than a well-scoped AI agent account, while a short-lived AI agent account can still be high risk if it can reach sensitive systems or expose credentials.

Best practice is evolving toward unified policy with differentiated enforcement. That means the same ownership and review standard, but different thresholds for credential lifetime, token refresh, approval depth and monitoring. Teams should also expect exceptions where a vendor-managed or embedded agent cannot emit the same quality of telemetry as an internal workflow. In those cases, the governance model should require compensating controls, not a separate policy lane. Shared governance is most effective when it is flexible on evidence format but strict on accountability and revocation. If an account can act but cannot be attributed quickly, the model is already failing.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.

Framework	Control / Reference	Relevance
NIST CSF 2.0	GV.OC-01 — Organisational Context	Shared account governance must align to business purpose and ownership.
	GV.RM-01 — Risk Management Strategy	Unified governance needs one risk model for standing and short-lived access.
	PR.AA-01 — Identity Management, Authentication and Access Control	The question is about governing identities and access under one model.
Recommendation — Define account purpose and ownership so both legacy and AI agent accounts stay within approved business context. Apply one risk strategy for account lifecycle, review cadence and revocation across all account types. Enforce one identity and access control model for legacy and AI agent accounts.
OWASP Agentic AI Top 10	A2 — Identity and Access Control	AI agent accounts need bounded delegated access and runtime control.
	A3 — Tool/Action Authorization	Agent accounts require permission checks for tool and system actions.
	A8 — Monitoring and Auditability	Event-driven governance depends on traceable agent activity.
Recommendation — Bind AI agent actions to explicit identity, scope and revocation controls. Authorize each agent action against approved tools, systems and scopes before execution. Capture authoritative events for agent creation, use, privilege change and shutdown.

Practitioner Guidance

What to prioritise: Put ownership, purpose and revocation first. If an account cannot be tied to a current owner and a current business function, it should not stay in the governed population, whether it is a legacy account or an AI agent account.

What to verify: Verify that the evidence trail comes from authoritative events, not from a periodic spreadsheet or manual screenshot. For AI agent accounts, confirm you can prove creation, scope change, token issuance, use and shutdown from system records.

Decision rule: If the account can execute actions autonomously or on delegated authority, apply the same approval and review model as any other governed identity, but shorten the evidence window to match the credential lifetime and usage pattern.

Practitioner takeaway: The goal is one governance model with two evidence rhythms, slower and review-based for legacy accounts, faster and event-based for AI agent accounts.