How should organisations decide between central AI review and delegated control?

Use central governance to define standards, approval criteria, and guardrails, then delegate routine decisions into reusable policy templates. If every project needs a manual review, governance becomes a bottleneck and teams route around it. The right model is federated execution with consistent control baselines, not universal committee approval.

What central AI review should decide, and what it should not

Central review is best used to set the non-negotiables: risk appetite, approval thresholds, prohibited use cases, evidence requirements, and the minimum control baseline every team must meet. It should not become the place where every ordinary decision is re-litigated. If the centre approves everything, it becomes the bottleneck and weakens compliance in practice.

A useful test is whether the decision changes the organisation-wide guardrail or only applies the guardrail. Central teams should own the policy, the exception criteria, and the escalation path. Delivery teams should handle routine approvals inside those boundaries, provided the control is reusable, auditable, and enforced consistently.

That model matters most when AI work is repeated across multiple products, business units, or vendors. The more similar the decision, the stronger the case for delegating it into templates, because the main value of central review is standardisation, not throughput by committee.

When delegated control is safer than manual approval

Delegation is appropriate when the decision is low ambiguity, the risk is already characterised, and the control can be expressed as a policy template rather than a one-off judgement. A delegated model works well for routine uses such as approved model classes, standard data handling patterns, or pre-cleared deployment patterns that do not change the organisation’s risk posture.

The key condition is consistency. If local teams can decide, they must decide against the same criteria, with the same logging, the same escalation triggers, and the same review evidence. That is what keeps delegation from turning into policy drift. For control design, the NIST AI Risk Management Framework is useful because it emphasises governance, mapping, measurement, and management rather than ad hoc approval habits.

Delegation is also stronger when supported by explicit security controls rather than informal trust. Where AI services consume APIs or internal tools, the control boundary should be enforceable through documented access and approval rules, not through manual memory of what the team said was allowed. In that sense, the OWASP Agentic AI Top 10 is a relevant reminder that identity, privilege, tool use, and delegation must remain bounded even when execution is distributed.

How to keep federated execution from becoming fragmented governance

Federated execution works only when the centre publishes a small number of reusable policy patterns that teams can apply without redesigning the control each time. The centre defines the decision logic, while local owners apply it to their own use case and retain evidence of compliance. That separation keeps governance scalable without abandoning oversight.

The most important practical safeguard is to distinguish between policy design and policy enforcement. The central function designs and updates the standard. Local teams enforce it in their workflows, but they do not reinterpret it on the fly. If a project cannot fit the template, it should route to exception handling rather than inventing a local variant.

Where AI systems are embedded in wider operational or regulatory environments, the same pattern applies to accountability. A governance framework such as the ISO/IEC 42001:2023 AI Management System Standard is helpful because it supports repeatable oversight, documented roles, and managed exceptions rather than blanket committee control.

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 AI RMF sets the technical controls, and ISO/IEC 42001:2023 defines the regulatory obligations.

Framework	Control / Reference	Relevance
NIST AI RMF	Govern	AI governance decisions require defined oversight, roles, and risk criteria.
Recommendation — Define AI governance roles, risk thresholds, and oversight routines before delegating operational decisions.
ISO/IEC 42001:2023	4.4 — AI management system	The question is about structuring organisation-wide AI governance and delegated control.
Recommendation — Establish an AI management system that standardises policy while allowing controlled delegation.
OWASP Agentic AI Top 10	ASI03 — Identity & Privilege Abuse	Delegated AI control must still constrain who or what can act and approve.
	ASI02 — Tool Misuse	Federated execution depends on bounded use of tools and reusable policy templates.
Recommendation — Constrain delegated agent actions to approved privileges and escalation paths. Restrict tool use to pre-approved workflows and policy-backed execution paths.

Practitioner Guidance

What to prioritise: Separate policy decisions from case-by-case approvals. If the question is about standards, thresholds, or risk appetite, keep it central; if the question is about a routine application of those standards, delegate it.

What to verify: Make sure every delegated path still produces the same evidence, logs, and escalation trigger as the central path. If teams can approve locally but cannot prove how they applied the baseline, the control is not really federated, it is fragmented.

Common mistake: Treating manual review as a sign of control strength. In practice, universal review often signals immature governance, because it prevents scale, slows delivery, and encourages teams to work around the process.

Practitioner takeaway: The right operating model is central rule-setting with delegated execution under a fixed baseline. Governance should decide the boundary once, then make routine decisions cheap, consistent, and auditable inside that boundary.