Identity governance must shift for machine identities and AI agents

At a glance

What this is: This is an identity governance analysis arguing that traditional IGA no longer fits an estate dominated by machine identities and AI agents with fragmented data and weak lifecycle control.

Why it matters: IAM, IGA, PAM, NHI, and emerging agentic AI programmes all depend on the same governance foundation: reliable identity context, explicit ownership, and continuous least privilege.

By the numbers:

• Machine identities now outnumber human identities 109:1, according to Palo Alto Networks' 2026 Identity Security Landscape cited by Linx Security.

Context

Identity governance now has to cover a much larger and more dynamic identity estate than the workforce-centric model it was designed for. In practice, that means service accounts, workloads, API keys, machine identities, and AI agents are appearing alongside human users across SaaS, cloud, on-premises, and hybrid environments.

The governance gap is not simply one of volume. When identity data is fragmented across HR, IdP, SaaS, and cloud systems, IGA cannot reliably answer basic questions about ownership, lifecycle state, or whether access still matches current business purpose.

For NHI and AI agent programmes, the implication is clear: governance has to start with correlation and accountability, not with automation or certification redesign.

Key questions

Q: How should security teams govern machine identities in industrial environments?

A: Security teams should govern machine identities the same way they govern privileged access: assign an owner, define a specific purpose, limit scope, and review it continuously. In practice, that means tracking service accounts, certificates, APIs, and connectors as non-human identities with their own lifecycle, not as background infrastructure. A machine identity should never have broader access than its workflow requires.

Q: Why do periodic access reviews miss excessive access in modern IGA programmes?

A: Because access often drifts between review cycles. Roles change, temporary permissions linger, and non-human identities can keep standing privilege long after the original need has ended. Reviews can confirm that access existed, but they cannot by themselves maintain least privilege as a continuous operating state.

Q: What breaks when identity data is fragmented across HR, directory, and application systems?

A: Fragmented identity data creates blind spots in access governance. Teams lose a reliable source of truth, which makes it harder to detect shadow IT, validate permissions, and trigger timely offboarding or entitlement changes. The result is inconsistent controls across the identity perimeter and a higher chance of access drift going unnoticed.

Q: What is the difference between access administration and ownership in IGA?

A: Administration is the ability to change permissions. Ownership is accountability for whether those permissions are appropriate for the business purpose. In modern IGA, those are not the same role, and treating them as the same is one of the fastest ways to lose control of non-human and delegated access.

Technical breakdown

Why fragmented identity data breaks governance context

Identity governance depends on correlating identities, accounts, entitlements, ownership, usage, and lifecycle state. When those signals live in separate systems, an access review may confirm that an entitlement exists without showing who owns it, why it exists, whether it is still used, or whether it creates other risky permissions. That is a data problem before it is a process problem. In NHI and AI agent environments, the fragmentation gets worse because many identities do not originate in HR and may never have a human manager attached. Practical governance has to treat correlation as the control foundation, not a reporting convenience.

Practical implication: build a unified identity graph before expanding certification or automation.

Why least privilege has to be continuous, not periodic

Least privilege is not a one-time state. As roles change, projects end, exceptions are granted, and temporary access is never removed, yesterday's appropriate access becomes today's excess. Periodic certification can find some of that drift, but it cannot keep pace with a living identity estate. The article's core point is that governance must continuously evaluate stale, unused, or over-scoped access, especially for identities whose permissions change outside workforce processes. For NHIs and agents, the risk is stronger because standing credentials often persist longer than any single task or business event.

Practical implication: shift from review-only governance to continuous entitlement evaluation and removal.

How ownership and lifecycle models differ for NHIs and AI agents

Traditional IGA assumes a person, a manager, and a predictable joiner-mover-leaver path. Machine identities and AI agents break that assumption because their lifecycle may be application-bound, ephemeral, event-driven, or process-specific. A service account may live as long as the application, a workload identity may exist for a short execution window, and an AI agent may gain or lose integrations as its process changes. Governance therefore needs explicit ownership that is separate from technical administration, plus lifecycle rules tied to purpose and scope rather than employment status alone.

Practical implication: define accountable owners and lifecycle rules for every non-human identity class.

Threat narrative

Attacker objective: The objective is to retain access through identities that governance cannot reliably map, review, or revoke.

1. Entry occurs through identities that are created outside workforce onboarding, such as service accounts, API keys, or AI agent credentials that enter the environment without the same controls as human accounts.
2. Credential access then persists because those identities often carry standing privilege or long-lived secrets, making revocation and review less reliable than in human IAM.
3. Escalation happens when excessive access accumulates across roles, projects, or systems, allowing the identity to reach resources beyond its original purpose.
4. Impact is governance failure at scale: unauthorized persistence, audit gaps, and access that remains live even after the business context has changed.

Breaches seen in the wild

• Dropbox Sign breach 2024: A compromised back-end service account gave attackers Dropbox Sign customer data, including API keys, OAuth tokens and MFA information.
• OneLogin API flaw (CVE-2025-59363): A OneLogin API flaw exposed OIDC client secrets to anyone with an API key, including vendors (CVE-2025-59363); fixed with no customer impact.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Identity governance is now a graph problem before it is a workflow problem. When identities, accounts, entitlements, ownership, and usage are scattered across HR, IdP, SaaS, and cloud systems, governance decisions lose the context they need to be meaningful. The programme failure is not merely incomplete visibility. It is the inability to connect access to accountable ownership and current business purpose. Practitioners should treat correlation as the first governance control.

Least privilege has become a continuous operating state, not a review-cycle outcome. Periodic certification can surface drift, but it cannot keep pace with role changes, temporary access, and standing credentials that survive long after their original use case ends. That is especially true for NHIs and AI agents, where access may be created and forgotten outside normal workforce processes. Practitioners should reframe governance around continuous entitlement validity, not calendar-driven attestation.

Ownership must be explicit because technical administration and accountability are different functions. The person who can change permissions is not necessarily the person accountable for whether those permissions are appropriate. That distinction becomes essential for service accounts, workloads, API keys, and agents that may have no natural manager. Practitioners should assign named ownership for every non-human identity and make that ownership a governance attribute, not an informal convention.

Lifecycle policy has to follow purpose and scope, not just employment status. A workload identity, API credential, or AI agent does not fit a workforce JML model unless the underlying business process does. The article points to a more durable governance pattern: define why the identity exists, how long it should exist, what can change its scope, and when it must be retired. Practitioners should extend JML logic across all identity types without assuming the same lifecycle mechanics apply to each one.

Governance should be measured by revoked risk, not completed activity. A finished review that removes nothing, or a fast ticket closure that leaves access live elsewhere, does not prove the programme is working. That is a success-metric problem, not a tooling problem. Practitioners should measure reduction in excessive access, removal of orphaned entitlements, and remediation speed as the real indicators of governance maturity.

From our research library:

• NHIs outnumber human identities by 25x to 50x in modern enterprises, according to the Ultimate Guide to NHIs.
• Read next: Ultimate Guide to NHIs

What this signals

Identity graph first: Modern IGA programmes will keep failing if they try to automate before correlating identities, accounts, entitlements, ownership, and usage into one governance view. The practical shift is from reviewing access in silos to governing it as a connected identity graph, which is the only way to make remediation trustworthy.

Machine identities and AI agents expand the governance surface beyond workforce JML, so teams need lifecycle policy that defines purpose, scope, and retirement for each identity class. That means building controls around issuance, ownership, and revocation at the point of creation rather than relying on later certification cycles to correct drift.

Governance maturity will increasingly be judged by outcomes such as reduced excessive access and faster remediation, not by how many reviews closed on time. A programme that completes checks but leaves live access in place has activity, not control.

For practitioners

• Build a unified identity graph Correlate identities, accounts, entitlements, ownership, usage, and lifecycle state across HR, IdP, SaaS, cloud, and engineering sources before expanding governance automation.
• Assign explicit owners to non-human identities Create accountable ownership for service accounts, workloads, API keys, and AI agents, and separate that ownership from technical administration rights.
• Operationalize continuous least privilege Review stale, unused, excessive, or orphaned access continuously, with special attention to standing privilege and permissions that outlast the business need.
• Automate only deterministic governance decisions first Start with well-understood events such as terminations, expired temporary access, and clear policy violations, then expand automation only after the identity data foundation is reliable.
• Extend lifecycle policy to NHIs and AI agents Define purpose, expiration, scope changes, and retirement triggers for each non-human identity class so lifecycle governance matches how the identity actually behaves.

Key takeaways

• Machine identities and AI agents now force IGA to govern a far larger estate than workforce identity alone, and the old review-led model no longer provides enough context.
• The main failure mode is fragmented identity data, which prevents teams from tying access to ownership, purpose, and lifecycle state.
• Modernisation should focus on unified identity context, explicit ownership, continuous least privilege, and outcome-based governance measures.

Key terms

• Identity Graph: An identity graph is a relationship map that connects identities, assets, data, and permissions so teams can see how access actually flows. In NHI programmes, it helps explain which agent is related to which owner, which system, and which policy boundary.
• Lifecycle Governance: Lifecycle governance is the set of controls that cover creation, assignment, review, rotation, and retirement of identities and credentials. For NHIs, it is the difference between a temporary automation asset and a persistent access risk. Strong lifecycle governance keeps ownership and expiry tied to actual business use.
• Continuous Least Privilege: A governance model that re-evaluates access as identity risk changes, rather than only at issuance or periodic review. In cloud environments, this means entitlements can be constrained or revoked when findings, posture, or behaviour indicate the access no longer fits the current state.
• Governance Outcome: A measurable result that shows whether identity control improved security, not just whether a process was completed. In practice, outcome measures include reduced excessive access, fewer orphaned entitlements, and faster remediation after risk is found.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.