Who should approve changes to managed settings for an agentic coding assistant?

Why This Matters for Security Teams

Managed settings are not a convenience layer for an agentic coding assistant. They define what the assistant can read, change, execute, and persist. Once those boundaries expand, the assistant’s identity boundary expands with them. That is why approval should sit with the same governance teams that already control privileged infrastructure, especially when settings affect tool access, workspace scope, or hook execution. NHI Management Group’s Analysis of Claude Code Security shows how quickly code-assistant controls can become operational security controls.

This is not a theoretical risk. Agentic systems do not behave like fixed-service accounts, and their access patterns shift with prompts, tasks, and connected tools. Guidance from the OWASP Agentic AI Top 10 treats expansion of tool authority as a core security concern, not a product preference. In practice, security teams encounter the real risk only after an assistant has already been allowed to reach a broader filesystem, trigger automation, or touch repositories that were never intended for routine use.

How It Works in Practice

Approval should follow a change-control model for privileged access, not a simple developer preference workflow. The right reviewers are usually security, IAM, platform engineering, and, where applicable, application owners for the repositories or environments the assistant can affect. The decision should focus on whether the change alters the assistant’s operational boundary, not whether the settings are merely convenient for a coding task.

Current best practice is to treat the managed settings as policy, then review them like policy. That means documenting who can approve changes, what triggers re-approval, and how reversions happen if the assistant starts acting outside its intended scope. The NHI Lifecycle Management Guide is useful here because the lifecycle question is the real issue: when settings change, the identity’s effective privileges change too. Pair that with runtime oversight principles in the NIST AI Risk Management Framework, which emphasizes governance, mapping, measurement, and monitoring rather than one-time approval.

• Approve any setting that expands file access, command execution, network reach, or hook invocation.
• Require separate review for changes that affect production systems, secrets stores, or CI/CD pipelines.
• Record the business justification for each boundary increase and the rollback path.
• Treat exceptions as time-bound, not permanent, unless they are explicitly revalidated.

For teams formalizing this model, the CSA MAESTRO agentic AI threat modeling framework is helpful for mapping how a setting change could enable lateral movement, data exposure, or unintended execution. These controls tend to break down when settings are self-service across many repositories because no single owner sees the cumulative privilege growth.

Common Variations and Edge Cases

Tighter approval often increases friction, so organisations need to balance speed for developers against the blast-radius reduction that governance provides. That tradeoff becomes most visible in fast-moving engineering teams, where users want to adjust assistant behavior without waiting for a full security review. The answer is not to remove approval, but to tier it.

For low-risk changes, a platform owner may approve within a pre-approved policy envelope. For changes that expand access to protected code, production secrets, or automation hooks, security and IAM should co-sign. Where there is no universal standard yet, current guidance suggests using the same control logic you would apply to PAM or privileged build systems: stronger change authority for anything that can execute, exfiltrate, or propagate beyond the intended workspace. NHI Management Group’s Top 10 NHI Issues and the OWASP NHI Top 10 both reinforce the same operational point: when autonomous systems gain broader reach, approval must move with the risk, not with the team requesting convenience.

Edge cases include open-source assistants running locally, assistants embedded in CI jobs, and managed settings inherited from templates. In those environments, approval often fails when nobody owns the template, so the effective boundary changes without review.

Related resources from NHI Mgmt Group

• When does a coding assistant create more risk than it reduces?
• How do security teams decide whether a coding assistant is suitable for sensitive work?
• What makes agentic AI an NHI governance issue?
• What breaks when machine identity changes do not propagate across systems?