AI agent security should be owned jointly, with engineering responsible for safe implementation, security for policy and control design, and compliance for visibility into data use and governance evidence. The article’s emphasis on collaboration reflects a practical reality: no single team can manage agent autonomy, business impact, and oversight alone. Shared responsibility is essential for effective control.
How responsibility should be divided across security, compliance, and engineering
AI agent security works best when responsibility is split by function, not by ownership turf. Engineering needs to build safe defaults, constrain tools and permissions, and make secure behaviour the normal path. Security needs to define the control model, risk boundaries, and approval logic. Compliance needs enough visibility to verify that governance, data handling, and audit evidence are actually present.
The practical benefit of this division is that it matches how agent risk appears in real systems: code, policy, and evidence failure often happen in different places. Engineering can reduce unsafe behaviour at design time, while security sets the rules that keep autonomy bounded. Compliance then checks whether the organisation can prove those rules are being followed.
This is also why responsibility should be joint rather than sequential. If security only reviews after deployment, control gaps become expensive to fix. If compliance is brought in only at the end, evidence is often incomplete or unavailable. Shared ownership creates earlier feedback loops and makes the control model easier to operate as the number of agents grows.
What each team should own in practice
Engineering should own implementation choices that determine whether an agent can act safely, such as scoped permissions, tool restrictions, approval checkpoints, logging hooks, and failure containment. Security should own the policy decisions that define what the agent may do, what must be reviewed, and what conditions trigger escalation. Compliance should own the mapping between behaviour and evidence, including data-use records, retention expectations, and governance artefacts.
That split is important because AI agent controls are not just documentation exercises. They affect how autonomy is granted, how requests are authorised, how actions are attributed, and whether sensitive operations can be traced after the fact. If any one team tries to carry all of that, controls tend to become either too permissive or too brittle.
For agent-heavy environments, the most useful boundary is usually this: engineering implements the guardrails, security defines the guardrails, and compliance verifies the guardrails can be evidenced. The work overlaps, but the accountability should not. Each team should know which decisions it can make, which it must challenge, and which it must be able to explain later.
Why shared responsibility matters as agents become more capable
As agent autonomy increases, the blast radius of a bad decision increases with it. The same agent that can complete a small task can also chain actions, reuse context, or trigger downstream systems in ways that are harder to supervise. That is why agent security cannot be treated as a pure application feature or a pure policy problem.
Security teams need to keep the control model focused on least privilege, delegated authority, and per-action checks, while engineering ensures those controls are actually enforced in the runtime. For a deeper breakdown of how AI agent control boundaries should be designed, see the AI Agent Authorisation Guide. For the broader security model around agent threat surfaces, the Agentic AI Security Guide is a useful reference point.
Compliance becomes more important, not less, as autonomy grows, because governance questions shift from "what was configured" to "what can be demonstrated." That means audit trails, data-use records, approval evidence, and change records need to be designed into the system rather than reconstructed later. A team that cannot produce evidence for agent behaviour does not really have governance, only assumptions.
Risk and Threat Considerations
Shared responsibility fails when each team assumes another team is covering a control gap. The result is usually over-privileged agents, unclear approval paths, weak logging, or inconsistent handling of sensitive data. In agentic systems, those failures can turn into privilege abuse, unsafe tool use, unauthorized data movement, or governance blind spots.
Failure mechanism: Engineering ships an agent with broad access, security assumes policy will constrain it later, and compliance discovers too late that the evidence trail is incomplete or the control cannot be proven.
Impact: The organisation may lose the ability to bound agent action, explain why an action was allowed, or demonstrate that data was handled under approved governance. That increases operational risk, audit friction, and the blast radius of a compromised or misbehaving agent.
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 addresses the attack surface, NIST SP 800-53 Rev 5 and NIST AI RMF set the technical controls, and ISO/IEC 42001:2023 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI agent responsibility splits around privilege, approvals, and bounded authority. |
| Recommendation — Enforce per-action authorization and least privilege for agent capabilities. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Review, Analysis, and Reporting | Compliance needs usable evidence for agent actions and governance checks. |
| AC-6 — Least Privilege | Engineering and security must limit what agents can access and do. | |
| Recommendation — Review agent logs regularly and retain evidence for governance and audit. Restrict agent permissions to the minimum required for each task. | ||
| ISO/IEC 42001:2023 | A.5.2 — AI policy | Shared responsibility needs organisational AI policy and role clarity. |
| Recommendation — Define AI policy roles, accountability, and approval boundaries for agent use. | ||
| NIST AI RMF | GOVERN — GOVERN | Agent security ownership requires governance, accountability, and oversight. |
| Recommendation — Assign governance owners for AI risk, controls, and oversight evidence. | ||
Practitioner Guidance
What to verify: Each agent should have a named owner, a documented approval path, and a clear evidence trail for its permissions, data access, and high-risk actions. If any of those three are missing, the control model is incomplete even if the agent appears to work normally.
Decision rule: If a control affects what the agent can do, security should define it; if it affects how it is built, engineering should implement it; if it affects how it is proven, compliance should validate it. When a decision crosses those lines, require joint review rather than informal handoff.
Practitioner takeaway: The goal is not to make one team responsible for everything, but to make sure every material agent decision is owned, enforced, and evidenced by the right function at the right time.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on September 27, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
