They need inventory, local telemetry and policy coverage across all the places agents run, including IDEs, terminals and browser-based assistants. Gateway logs only show traffic that passes through the gateway. Off-gateway hooks, SIEM export and identity-aware monitoring help reveal activity that would otherwise remain outside governed paths.
Why gateway logs miss shadow AI activity
Gateway logs are useful, but they only cover traffic that actually transits the gateway. shadow ai often appears through browser assistants, IDE plug-ins, terminal tools, local desktop apps and embedded copilots that may never touch the governed path, which means the detection problem is really an inventory and telemetry problem as much as a network one.
That is why security teams need visibility into the places users and agents run, not just the network edge. In practice, the first question is whether the organisation can see where AI is being used, by whom, and from which endpoint or workspace before it tries to interpret network evidence.
Where teams build that broader view, the value is in correlating local signals with central controls. An Shadow AI and AI Agent Discovery Guide is useful here because it frames discovery across OAuth grants, API keys, endpoint signals and cloud activity rather than treating the gateway as the only evidence source.
What telemetry exposes off-gateway AI use
Off-gateway detection works when multiple weak signals are combined into one usable picture. Local process telemetry can show AI assistants launching, browser telemetry can show sanctioned or unsanctioned assistants in use, and identity-aware monitoring can tie that activity back to user accounts, tokens and consented app access. SIEM export matters because it lets those signals be searched, correlated and retained outside the tool where they originated.
Teams should also look for policy coverage gaps. If an assistant is installed on an endpoint, allowed through a browser extension, or connected through a personal account, gateway inspection may never see the request even though business data is being handled. A broader inventory of approved tools, approved identities and approved data paths is therefore a prerequisite for meaningful detection.
That is especially important when discovery depends on identity and access patterns. The AI Security Posture Management (AI-SPM) Guide maps AI asset discovery, workload identity and attack paths, which fits the need to detect AI use that is visible in endpoint, cloud and identity telemetry rather than gateway traffic alone.
How to turn discovery into governed coverage
Detection becomes reliable only when the organisation can separate sanctioned AI from shadow AI and then enforce different controls for each. That usually means combining endpoint coverage, browser controls, SaaS discovery, identity logs and policy enforcement so that a tool cannot be “allowed” in one place and invisible in another. Discovery should end with a clear ownership decision for each tool, connector or account.
For the governance side, the practical issue is not whether a model is being used, but whether the usage is attributable and revocable. If the only evidence lives in a gateway, teams may miss locally installed assistants, personal accounts, or tools that reuse cached credentials. In that case, response options are delayed because the asset was never added to the monitoring or access model in the first place.
The most useful reference point for this operating model is the Enterprise AI Copilot Security Guide, because it focuses on monitoring AI use, governing connectors and reducing over-sharing in the environments where shadow use typically emerges.
Risk and Threat Considerations
Shadow AI is risky because it creates blind spots in data handling, access governance and incident response. If teams depend on gateway logs alone, they can miss assistants that run locally, consume sensitive prompts outside approved controls, or connect to third-party services through user-authorised tokens.
Failure mechanism: The organisation assumes the gateway is the control plane, but the real activity happens on endpoints, in browsers or inside developer tools that bypass that inspection point. That leaves usage undiscovered until data loss, policy violations or credential abuse are already in play.
Impact: Undetected AI use can expand the attack surface, weaken accountability and make it impossible to prove which tools touched which data. It also delays containment because teams cannot revoke what they cannot see.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 addresses the attack and risk surface, while CIS Controls v8 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| CIS Controls v8 | CIS-8 — Audit Log Management | Shadow AI detection depends on collecting and centralising logs from endpoints and SaaS, not just gateways. |
| Recommendation — Centralise endpoint and identity logs so off-gateway AI activity can be correlated and searched. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Review, Analysis, and Reporting | Off-gateway telemetry only helps when teams analyse and correlate it into actionable detections. |
| IA-5 — Authenticator Management | Shadow AI often involves tokens, OAuth grants and cached credentials that must be inventoried and managed. | |
| Recommendation — Correlate local, browser and identity events to expose AI use that bypasses gateway inspection. Inventory and rotate AI-related credentials, tokens and OAuth grants before they drift out of governance. | ||
| OWASP Non-Human Identity Top 10 | NHI-02 — Secret Leakage | Shadow AI commonly hides in exposed tokens, API keys and other secret material outside gateway logs. |
| NHI-10 — Human Use of NHI | Users often operate AI tools through personal accounts or local assistants beyond governed paths. | |
| Recommendation — Scan endpoints and SaaS for leaked AI keys and tokens that indicate unmanaged use. Detect when people are using AI credentials or assistants outside approved organisational controls. | ||
Practitioner Guidance
What to prioritise: Start with asset and identity discovery, then layer endpoint and browser telemetry on top of gateway data. If a control only sees the network edge, treat it as partial coverage, not as proof that AI use is governed.
What to verify: Confirm that AI usage telemetry is available from the places where work actually happens, including developer machines, managed browsers and sanctioned desktop assistants. The useful test is whether you can answer who used the tool, where it ran and what account or token it used.
Common mistake: Teams often overvalue proxy visibility because it is easy to operationalise. That approach misses the exact cases shadow AI is designed to exploit, which are the locally executed or user-mediated paths outside the gateway.
Practitioner takeaway: The goal is not to replace gateway logs, but to make them one input among several so that AI use is visible across endpoint, identity and policy layers before it becomes an unmanaged path.
Related resources from NHI Mgmt Group
- How should security teams govern shadow AI without relying on discovery alone?
- How should security teams detect malicious AI tool calls without relying only on logs?
- How should security teams detect AI activity in production without relying only on cloud logs?
- How should security teams detect AI-written malware without relying on signatures?