Yes, because agents increasingly operate through delegated access, policy decisions, and auditable workflows that affect system trust. If the business case ignores access controls, logging, and review overhead, the organisation underestimates the cost of governing the agent itself. That makes finance, IAM, and AI governance interdependent.
Why This Matters for Security Teams
AI agent ROI modelling is not just a finance exercise when the agent can request access, move data, or trigger actions under delegated authority. The real cost of an agent includes identity controls, approval logic, logging, review, incident response, and ongoing policy tuning. If those costs are omitted, the business case can make risky automation look cheaper than it really is. NHI Management Group’s research on AI Agents: The New Attack Surface report shows why this matters: 80% of organisations report agents have already acted beyond intended scope.
Security and identity teams should care because ROI assumptions often decide whether an agent is deployed at all, and whether it is deployed with proper guardrails or as an informal pilot that later becomes production. That means ROI can indirectly shape privilege boundaries, data exposure, and auditability. This is especially true when agent behaviour is dynamic and the access pattern cannot be reduced to a single static role. In practice, many security teams encounter the real governance cost only after the agent has already been granted broad access and started producing exceptions rather than through intentional design.
How It Works in Practice
For agentic systems, ROI should be modelled as total operating cost, not just task automation savings. That means estimating identity issuance, privilege review, policy evaluation, secrets rotation, logging retention, detection engineering, and human oversight per workflow. The emerging best practice is to treat the agent as a workload with its own identity, then attach runtime authorisation and short-lived credentials to each task rather than issuing broad standing access. Guidance from the NIST AI Risk Management Framework supports this kind of governance-by-design approach, while the OWASP Agentic AI Top 10 and CSA MAESTRO agentic AI threat modeling framework both reinforce that agentic risk must be assessed at the action level.
A practical ROI model usually includes:
- Per-agent identity setup, such as workload identity, service accounts, or OIDC-based assertions.
- Just-in-time access provisioning for each task, with automatic revocation on completion.
- Policy-as-code evaluation at runtime, especially for tool use, data retrieval, and write actions.
- Audit and evidence costs, including logs that support investigation and compliance review.
- Fallback handling for denied actions, escalation paths, and exception approvals.
When teams ignore these items, the model understates both cost and risk. NHI Management Group’s Ultimate Guide to NHIs and OWASP NHI Top 10 both point to the same operational reality: identity controls are part of the product cost, not an afterthought. These controls tend to break down when teams try to retrofit them into multi-agent pipelines with shared tools and asynchronous approvals because tracing who authorized what becomes ambiguous.
Common Variations and Edge Cases
Tighter governance often increases delivery overhead, requiring organisations to balance faster experimentation against stronger control coverage. That tradeoff becomes visible in environments where agents are low-risk assistants versus systems that can initiate transactions, modify records, or chain multiple tools. In the lower-risk case, some teams accept broader scopes and lighter review, but current guidance suggests that this should be explicit and time-bound rather than assumed. For high-impact use cases, agent ROI should include control costs from the start, because a cheap pilot can become an expensive remediation if access is later expanded without proper boundaries.
There is no universal standard for how to price security overhead into AI agent ROI yet, but a practical rule is to assign cost to every control that reduces blast radius or improves accountability. That includes policy enforcement, secret management, replayable logs, and periodic access recertification. NHI Management Group’s 52 NHI Breaches Analysis is useful here because it shows how often identity failures become business failures. If an agent is allowed to operate across systems with human-like autonomy, the ROI model must assume that misuse, prompt injection, or overreach will eventually happen, not treat those events as rare edge cases.
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, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A07 | Agentic systems need runtime controls, not just upfront ROI assumptions. |
| CSA MAESTRO | GOV-02 | MAESTRO ties governance overhead to agentic risk and operating cost. |
| NIST AI RMF | AI RMF supports measuring and managing risk as part of business value. | |
| OWASP Non-Human Identity Top 10 | NHI-03 | Secret lifecycle costs directly affect the true cost of agent operations. |
| NIST CSF 2.0 | PR.AC-4 | Least privilege and access control are core to agent governance cost. |
Include governance staffing, approval flows, and evidence capture in the agent business case.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 19, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
