Accountability should be split by operating model. AppSec usually owns the applications, while identity and data security own classification and credential guardrails. For AI coding agents that run on company devices and networks, AppSec and the SOC need joint accountability because the activity is runtime behavior, not just code creation. Clear ownership prevents the gap where everyone assumes someone else is watching.
How accountability should be split across app, agent, identity, and data controls
Accountability should follow the control plane, not the organisational chart. Application security owns application risk, identity security owns authentication and privilege guardrails, and data security owns classification and handling controls. That split works only if it is explicit about who approves exceptions, who fixes issues, and who is on point when an AI coding agent behaves at runtime rather than just producing code.
For vibe coding, the hardest part is that the security impact often appears after the prompt, when an agent can touch files, tokens, cloud resources, or production-adjacent systems. That makes the question less about “who wrote the code” and more about “who governs the action path.” In practice, AI Coding Agents Security Guide is the right reference point for the runtime controls that sit around the developer workflow, while the broader operating model still needs a named owner for each layer.
Where the work crosses identity and privilege boundaries, the accountability model should treat agent access the same way it treats any other high-impact access path: someone must own issuance, someone must own review, and someone must own revocation. Identity Security Programme Guide helps anchor that ownership model, especially where shared responsibility often turns into no responsibility at all. For non-human actors and tokens, NHI Lifecycle Management Guide is the clearest reminder that lifecycle control, not just policy wording, is what keeps access bounded.
Where AppSec ends and identity or data ownership begins
AppSec should normally own the security of the application artifact, the codebase, the build path, and the secure design choices inside the software itself. Identity teams should own the rules for who or what may authenticate, what privilege is granted, and how long that access lasts. Data security should own classification, retention, movement restrictions, and any control that governs how sensitive data can be used by humans or agents.
The boundary matters because vibe coding creates mixed accountability if teams confuse content creation with operational authority. A coding agent may generate a patch, but it may also consume secrets, call APIs, or open a path into systems the developer does not fully understand. In that case, the owning question is not whether the code was helpful, it is whether the runtime access, approval path, and data exposure were governed by the right team.
That is why a single “AI owner” is usually too vague to be useful. The better model is a RACI-style split where AppSec owns secure software outcomes, identity owns access and privilege, data security owns data handling, and the platform or SOC owns monitoring and response for activity that occurs during execution. If the same agent can modify code and invoke privileged tools, the control ownership must be explicit before the exception ever reaches production.
What joint ownership looks like when agents run on company devices
For AI coding agents running on corporate endpoints, the answer is joint accountability between AppSec and the SOC, with identity and data teams supporting the guardrails they control. AppSec defines safe operating patterns for the agent, including allowed tools, sandboxing expectations, and release criteria. The SOC owns detection and escalation when the agent’s runtime behavior looks anomalous, destructive, or out of policy.
This joint model is necessary because the risk is not limited to bad code. The agent can create a live operational event, such as deleting data, exfiltrating secrets, or invoking cloud actions in a way that looks like ordinary developer activity until the damage is done. The security owner therefore needs visibility into execution telemetry, not just source control history.
At scale, the practical test is whether each team can answer three questions without handoff confusion: who approved the agent’s access, who can revoke it, and who investigates misuse. If those answers differ by environment, tool, or credential type, the governance model is probably too fragmented to be reliable.
Risk and Threat Considerations
The main failure mode is a shared-responsibility gap, where AppSec assumes identity owns the access issue, identity assumes the platform owns the agent, and the SOC only sees the incident after the damage is visible. That is especially dangerous when an agent can use real credentials or reach production-adjacent systems, because the control failure is not hypothetical, it becomes an execution path.
Failure mechanism: An over-scoped agent, a weak approval boundary, or missing runtime monitoring lets the agent perform actions that exceed the developer’s intent or the organisation’s policy, with no single owner reacting fast enough.
Impact: The result can be secret exposure, destructive changes, data loss, or unauthorised access to business systems, followed by delayed detection and unclear incident ownership.
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 sets the technical controls, and ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent access and privilege scope are central to who owns runtime agent control. |
| Recommendation — Define ownership for agent identities and privilege boundaries before allowing tool access. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Accountability depends on limiting agent and user permissions to the minimum needed. |
| AU-6 — Audit Record Review, Analysis, and Reporting | Joint accountability requires runtime monitoring and review of agent actions. | |
| Recommendation — Enforce least privilege for agent and developer access to sensitive systems. Review agent and developer audit records for misuse, drift, and unauthorized actions. | ||
| ISO/IEC 27001:2022 | A.5.15 — Access control | Ownership split hinges on clearly governed access rules across apps, identity, and data. |
| A.8.2 — Privileged access rights | Overprivileged coding agents are an explicit accountability risk in this subject. | |
| Recommendation — Assign and enforce access control ownership for each sensitive control boundary. Restrict and review privileged access rights for AI coding agents and operators. | ||
Practitioner Guidance
What to prioritise: Define ownership by control type before you define it by tool. AppSec should own secure design and release rules, identity should own authentication and privilege guardrails, data security should own classification and handling, and the SOC should own runtime monitoring and response.
Decision rule: If the agent can act with a company credential, reach sensitive data, or trigger operational change, treat it as a runtime security problem, not just a development productivity problem. That means the control owner must be able to approve, observe, and revoke access without waiting for a separate team to interpret the risk.
What to verify: Every high-impact agent should have a named owner, a documented approval path, an audit trail for issued permissions, and a clear escalation path for misuse or drift. If any of those are missing, the operating model is incomplete, even if the tooling looks mature.
Practitioner takeaway: The safest accountability model is the one that makes runtime authority visible, revocable, and attributable, because vibe coding failures become security incidents when no one owns the agent’s actions after the prompt is sent.
Related resources from NHI Mgmt Group
- Why do coding agents create blind spots in existing identity and data security controls?
- Who is accountable when identity security controls fail across team boundaries?
- Who is accountable when identity security controls fail across IAM, PAM, and NHI programmes?
- Who is accountable when policy changes are not reflected across apps, APIs, and data controls?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 30, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
