Ownership should sit with the team that can actually change the exposed access path, usually a combination of MLOps, security, and the system owner for the connected tool or data source. If no one owns the agent’s permissions, the organisation will record findings without shrinking blast radius or closing the privilege gap.
Why This Matters for Security Teams
When AI agents can call APIs, query internal data, or trigger business workflows, the ownership question becomes a control question, not a reporting question. Findings about prompt injection, overbroad tool access, or unsafe delegation should map to the team that can reduce exposure in practice. That usually means the MLOps function, the security team, and the owner of the connected application or data source working together, with clear accountability for remediation. The NIST AI Risk Management Framework is useful here because it frames AI governance as an organisational responsibility, not a purely technical task.
Security teams often get this wrong by treating AI testing results like a model-only issue. That misses the real blast radius: the agent may not be dangerous because of the model itself, but because it can reach customer records, payment actions, code repositories, or privileged workflows. NHI Management Group sees the same failure pattern repeatedly: findings are logged, risk is acknowledged, and then no one with change authority is assigned to narrow the agent’s permissions or adjust the connected system.
How It Works in Practice
The practical ownership model should follow the path of control. If the issue is a broken prompt boundary or unsafe agent policy, MLOps may own the fix. If the issue is excessive access to a SaaS platform, database, or internal workflow, the system owner must own the permission change. Security should own the finding lifecycle, severity, and validation, but not be the only team expected to implement remediation. That division matches how agentic systems are evaluated in current guidance from the CSA MAESTRO agentic AI threat modeling framework and the OWASP Agentic AI Top 10.
- Assign a named owner for the agent, the model runtime, and each connected tool or data source.
- Separate finding triage from remediation ownership so security does not become the default fix team.
- Track permission issues as blast-radius problems, not just application defects.
- Require evidence that privileges were reduced, scoped, or time-bound after remediation.
- Re-test the agent against the same workflow, not just the model endpoint.
Where agents use external tools, the ownership chain should also include the system of record for those tools. A finding about excessive write access to an ERP or ticketing platform cannot be closed by the model team alone. In those cases, best practice is to record a single accountable owner and supporting remediation owners, then verify that the least-privilege change actually took effect. These controls tend to break down in shared-platform environments where no single team can change both the agent policy and the downstream entitlement model because remediation stalls between platform, application, and governance teams.
Common Variations and Edge Cases
Tighter ownership often increases coordination overhead, requiring organisations to balance speed of triage against the risk of leaving an agent overprivileged. That tradeoff becomes sharper when the agent spans multiple business units, vendors, or cloud tenants, because no one team can see the full access path. In those cases, current guidance suggests creating a single risk owner with delegated remediation tasks rather than distributing accountability so widely that it disappears.
There is no universal standard for this yet, but the direction of travel is clear. If the finding concerns model behaviour alone, the model team may own it. If the finding concerns tool access, data exposure, or transaction authority, the business system owner must share responsibility. For higher-risk environments, the MITRE ATLAS adversarial AI threat matrix helps teams classify whether the issue is an AI attack path, while NIST SP 800-53 Rev 5 Security and Privacy Controls is useful for mapping ownership to access control and incident response responsibilities.
One practical exception is vendor-hosted agents where the organisation cannot change the underlying model stack. Even then, the company still owns its exposure, because it controls the connected systems, credentials, and approval gates. If that accountability is not explicit, remediation becomes a ticketing exercise rather than a reduction in operational risk.
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 MITRE ATLAS 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 |
|---|---|---|
| NIST AI RMF | GOV | AI governance requires clear accountability for findings and remediation. |
| OWASP Agentic AI Top 10 | A2 | Tool and permission abuse are central risks in agentic applications. |
| CSA MAESTRO | T1 | Threat modeling agentic systems requires ownership across model and tool boundaries. |
| MITRE ATLAS | Adversarial AI threats help classify whether a finding is model or access-path driven. | |
| NIST CSF 2.0 | GV.OV-01 | Governance needs accountable oversight for security findings and risk treatment. |
Use ATLAS to distinguish model attacks from business-system abuse and assign the right fix owner.
Related resources from NHI Mgmt Group
- What should organisations watch when AI agents are connected to business systems?
- How should security teams limit the risk from AI agents that have access to production systems?
- How should security teams govern AI agents that can access enterprise systems?
- How should security teams govern ecommerce AI agents that can touch payment systems?
