Because users can reach AI capabilities through browser sessions, mobile devices, personal accounts, or approved applications with embedded AI features. A perimeter block may reduce obvious exposure, but it does not create ownership, inventory, or data access governance for the AI channel itself.
Why network blocks miss the real control point
Network blocks target one path to an AI service, but shadow ai use is a behavior and governance problem, not just a connectivity problem. Users can shift to browser-based sessions, personal devices, mobile apps, or approved products that quietly embed AI features, so the control does not actually answer who can use AI, what data they can send, or which services are approved.
The practical weakness is that a block can reduce casual access while leaving the underlying AI channel unmanaged. If users can still reach a model through a different interface, the organisation still lacks inventory, ownership, and policy enforcement over the activity itself.
What changes when the AI entry point is not the network
Shadow AI often rides on sanctioned infrastructure and sanctioned identities, which makes perimeter logic too coarse to distinguish approved from unapproved use. An employee may access the same external model through a personal account, a browser extension, or a feature inside a familiar SaaS product, and the network sees only ordinary web traffic.
That is why the control objective needs to move from blocking destinations to governing the AI interaction. Discovery, approved-use boundaries, and data handling rules matter more than simple URL denial when the real exposure is prompt content, uploaded files, or downstream retention of sensitive information.
Discovery has to look for the channels where shadow AI actually appears, including OAuth grants, API keys, endpoint activity, and embedded AI features in business applications. A useful starting point is Shadow AI and AI Agent Discovery Guide, because the problem is usually found in usage paths, not in a single blocked domain.
Why governance, not just denial, is the durable control
Once AI is available through many routes, the durable control is ownership. Someone has to decide which tools are permitted, which data classes may be used, which accounts may connect, and how new AI features in third-party software are reviewed before they are adopted.
That is also where account and integration risk becomes visible. When an AI feature depends on third-party authorization or an unmanaged token, the organisation may lose track of where data flows and who can revoke access. Vercel Context.ai OAuth Supply Chain Breach is a good reminder that shadow AI can enter through ordinary SaaS integration paths, not just through a rogue app download.
Governance also needs lifecycle rules for the AI capability itself. If a tool, model, or assistant is not inventoried, owned, and reviewed, then network filtering only postpones the question of control. A practical policy baseline is Agentic AI Security Policy Template, because it treats registration, oversight, and retirement as first-class requirements.
Risk and Threat Considerations
Blocking known AI sites can create a false sense of containment. The main risk is uncontrolled data sharing through alternate channels, plus unmanaged third-party integrations that bypass the perimeter while still handling company information. In practice, that can expose sensitive prompts, customer data, credentials, or regulated content even when the main firewall rule is working as designed.
Failure mechanism: Users route around the block with browser sessions, mobile devices, personal accounts, extensions, or embedded AI features, so the organisation loses visibility into where AI is being used and what information is being sent.
Impact: Sensitive data can leave approved systems without inventory, approval, retention controls, or revocation paths, which raises confidentiality, compliance, and third-party risk at the same time.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
CIS Controls v8, NIST SP 800-53 Rev 5 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| CIS Controls v8 | CIS-5 — Account Management | Shadow AI use depends on unmanaged accounts and app access paths. |
| Recommendation — Inventory and govern accounts that can access approved and shadow AI services. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Shadow AI often relies on tokens, API keys, and OAuth grants. |
| AC-20 — Use of External Information Systems | Users reach shadow AI through personal devices and external services. | |
| Recommendation — Track and revoke AI-related authenticators, tokens, and keys promptly. Restrict and approve external systems used to process organisational data. | ||
| NIST CSF 2.0 | GV.OC-01 — Organizational Context | Shadow AI is a governance and operating-context problem, not only a network one. |
| ID.AM-01 — Physical Devices and Systems Inventory | Shadow AI control starts with discovering where AI-enabled tools are actually used. | |
| Recommendation — Define how AI use fits organisational objectives, risk appetite, and approved channels. Maintain an inventory of AI-enabled applications, endpoints, and integrations. | ||
Practitioner Guidance
What to prioritise: Start by inventorying where AI already exists in sanctioned applications, browsers, and third-party services, then identify which data classes each path can touch. If you only block destinations, you will miss the more common problem, which is approved software that quietly adds AI capability.
What to verify: Confirm that each allowed AI path has an owner, an approval status, and a clear revocation method for tokens, OAuth grants, or embedded features. If you cannot answer those three questions, the control is not yet governance, it is only denial.
Practitioner takeaway: Network controls are useful as friction, but shadow AI is controlled by visibility, ownership, and data rules, not by perimeter filtering alone.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on October 8, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org