Use a single governance model for ownership, review, and offboarding, but apply it to different identity lifecycles. Service accounts need clear technical owners and expiry rules, while AI agents also need controls for delegated scope and runtime behaviour. The goal is one inventory and one policy spine, not separate shadow processes.
Why This Matters for Security Teams
Service accounts and AI agents look similar in an inventory, but they fail differently in practice. Service accounts are usually tied to a system task, while AI agents can decide which tools to call, which data to retrieve, and how to chain actions at runtime. That means an IGA programme that only tracks ownership and periodic review will miss the real risk: delegated authority that changes with context. Current guidance suggests treating both as non-human identities, but not as identical ones.
The operational goal is one policy spine for discovery, owner assignment, review cadence, and offboarding, then separate control logic for static versus autonomous behaviour. For service accounts, the main questions are who owns it, why it exists, and when it expires. For agents, the questions expand to what the agent may do, under which conditions, and how runtime decisions are constrained. This is why the OWASP NHI Top 10 and NIST AI Risk Management Framework are useful together: one addresses identity exposure, the other addresses AI governance risk. In practice, many security teams encounter misuse only after an agent has already accessed a sensitive tool or inherited overbroad access from a legacy service account.
How It Works in Practice
A unified IGA model starts with a single inventory record for every non-human identity, including service accounts, bots, and agents. That record should capture business owner, technical owner, environment, purpose, downstream dependencies, last use, and revocation path. From there, the control pattern diverges.
For service accounts, best practice is still relatively mature: bind each account to a named system, assign a technical owner, minimize standing privilege, and set expiry or review triggers for dormant accounts. For AI agents, the inventory must also include delegated scope, tool permissions, approval boundaries, and the policy engine used at runtime. That is where intent-based authorization becomes relevant. Instead of granting broad pre-defined access, the system evaluates whether the agent’s current request is allowed in context. The CSA MAESTRO agentic AI threat modeling framework and the OWASP Agentic AI Top 10 both point toward this runtime view.
- Use one authoritative registry, but tag identities by lifecycle: service account, automation bot, or autonomous agent.
- Require technical ownership and expiry rules for service accounts, with no unowned accounts allowed.
- For agents, attach policy-as-code to each tool call and re-evaluate access at request time.
- Prefer short-lived credentials and workload identity over static secrets, especially where the agent can act without direct human supervision.
NHIMG research on AI agents as a new attack surface shows why this matters: 80% of organisations report agents already performing actions beyond intended scope, and 33% say agents accessed inappropriate or sensitive data. These controls tend to break down in highly federated environments where teams can create shadow identities faster than IGA workflows can classify them.
Common Variations and Edge Cases
Tighter governance often increases operational overhead, requiring organisations to balance faster automation against stronger review and runtime enforcement. That tradeoff becomes visible when service accounts and agents share the same backend systems but not the same risk profile.
One common edge case is an agent that authenticates through a service account. In that model, the account should not inherit the agent’s full behavioural latitude. The account remains a credential container, while the agent’s permissions must be constrained by separate policy at runtime. Another edge case is ephemeral task workers that behave like service accounts for minutes at a time. Those are best handled with short-lived workload identity rather than long-lived static credentials, because TTL matters differently when an identity can chain tool use autonomously.
There is no universal standard for this yet, but current guidance suggests using one offboarding process and one access review calendar across both classes, while using separate control questions: “who owns it and when does it expire” for service accounts, and “what can it decide, under what context, and how is that enforced now” for agents. NHIMG’s Top 10 NHI Issues and Lifecycle Processes for Managing NHIs are useful references when building those distinctions into policy. The model fails when organisations treat agent permissions as a one-time IAM grant instead of a continuously evaluated decision.
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 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Runtime agent misuse and overbroad tool access are central to this question. |
| CSA MAESTRO | TRM | MAESTRO addresses agent threat modeling, delegated scope, and control boundaries. |
| NIST AI RMF | AI RMF governs accountability and ongoing risk management for autonomous agents. |
Classify each agent tool grant as a runtime risk and re-check it against current context.
Related resources from NHI Mgmt Group
- How should teams govern access when AI agents and service accounts share the same business systems?
- How should security teams govern Active Directory service accounts?
- How should security teams govern AI agents that use service accounts and MCP tools?
- Should healthcare teams use the same zero trust model for AI agents and service accounts?
