Should AI access be managed through identity controls or network blocking?

Why this is usually an identity decision, not a network decision

For AI access, the durable question is usually who or what is allowed to use the application, under what conditions, and with what audit trail. Network blocking can be a useful perimeter safeguard, but it is brittle when users can switch networks, use sanctioned SaaS from elsewhere, or rely on approved integrations. Identity controls persist across location and give you revocation, accountability, and policy enforcement.

That is why governed adoption tends to rely on access management rather than blanket denial. If the organisation expects some AI use to remain approved, the control plane should follow the user or workload, not just the network edge.

What identity controls give you that blocking cannot

Identity controls let you distinguish approved access from casual use, third-party access, and broken-offboarding risk. They also let you separate interactive users from service or workload access, which matters when AI tools are called through APIs, browser extensions, or automation. With SSO and access revocation, you can disable use centrally without relying on every endpoint or network path being equally controllable.

That distinction becomes more important as AI is embedded into normal work. A network block can stop a direct website visit, but it does not solve entitlement drift, shared credentials, or the need to know which account actually used the service. Identity-based control is the better fit when the question is governance, not prohibition.

For teams building a repeatable model, the same access logic used for IAM and IGA Basics applies: authenticate the actor, assign only the access needed, and review it over time. Where the AI access is non-human or service mediated, the underlying identity model becomes even more important, as described in Ultimate Guide to NHIs — What are Non-Human Identities.

When network blocking still has a place

Network blocking is useful when the policy goal is speed, containment, or a temporary freeze while governance is being built. It can reduce shadow use, limit exposure during an incident, and buy time for policy design. It is also a sensible control for systems that are not yet approved for external AI access or where legal, privacy, or data handling constraints require a hard stop.

But as a long-term control it is easy to overestimate. Employees can route around it, approved vendors may have multiple endpoints, and blocking tells you little about who attempted access, which account succeeded, or whether the usage was appropriate. Blocking is strongest as a short-term boundary or a supplementary safeguard, not as the main operating model.

That is consistent with broader control guidance in NIST SP 800-53 Rev 5 Security and Privacy Controls, which treats access control, identification, authentication, and audit as separate control functions. It also aligns with the practical access model in CIS Controls v8, where account management and logging support enforcement more reliably than network denial alone.

Risk and Threat Considerations

AI access handled only through network blocking creates a false sense of control. Users may move to mobile networks, home connections, or sanctioned integrations, while the organisation still lacks a clear record of who accessed what, from which account, and whether access was revoked when employment or role changed.

Failure mechanism: The control is enforced at the wrong layer, so access persists through alternative paths, shared accounts, or third-party integrations even when one network route is blocked.

Impact: Organisations lose offboarding control, accountability, and visibility, which increases the chance of unauthorized use, policy drift, and harder incident response.

Practitioner Guidance

What to prioritise: Use identity controls as the primary approval mechanism for any AI service you intend to permit. Reserve network blocking for temporary containment, unapproved tools, or environments where the right answer is still “no”.

What to verify: Check that every approved AI application is behind SSO, that access can be revoked centrally, and that logs show the human or workload identity actually using it. If those three signals are missing, the access model is not yet governed.

Practitioner takeaway: If you need durable, auditable AI adoption, manage access where the decision can be enforced and reviewed, which is the identity layer, not the network edge.

Related resources from NHI Mgmt Group

• What happens when workload-to-workload access is managed through secrets instead of centralized identity controls?
• What happens when AI workloads are allowed through a network segment instead of being tied to identity-verified access?
• What breaks when OT connectivity is managed with network-centric controls instead of identity-driven access?
• Why does routing AI agents through identity controls reduce access risk?