Network telemetry helps find unsanctioned AI use by showing connections to known AI services, unusual DNS lookups, web traffic to browser-based chat tools, and data uploads to unfamiliar endpoints. Security teams can correlate that traffic with user identity, device posture, proxy logs, and API calls to spot shadow AI, exfiltration, and policy violations.
How network telemetry exposes shadow AI paths
Network telemetry is useful because unsanctioned AI often leaves network traces even when users try to keep it informal. Connection patterns can reveal repeated calls to public AI services, browser sessions to chat tools, DNS queries for model providers, and uploads to unfamiliar endpoints. Those signals become more valuable when you can line them up against user, device, and proxy context.
It is the difference between seeing an isolated request and seeing a pattern that suggests an approved workflow has been bypassed. If the same workstation repeatedly reaches consumer AI domains during business hours, or if a device starts sending content to endpoints that do not match its normal application profile, telemetry can move the issue from suspicion to a triageable lead.
What the network data is actually telling you
The strongest signals are usually behavioural, not absolute. A single HTTPS request is weak evidence on its own, but repeated access to known AI domains, new DNS names associated with browser-based chat, or data bursts that do not fit the user’s normal traffic profile can indicate shadow AI use. The same applies when a proxy log shows uploads to a service that is not on the approved software list.
Correlation is what turns raw traffic into a finding. Network telemetry becomes much more actionable when it is matched with identity, device posture, browser history, and API activity, because that combination can distinguish sanctioned experimentation from policy-breaking use, personal account use, or a third-party integration that was never reviewed.
Turning detections into governance and response
Once telemetry suggests unsanctioned AI, the next step is to decide whether you are dealing with harmless curiosity, an approved tool used in the wrong way, or a genuine exposure path. A traffic-only view can tell you that AI was used; governance context tells you whether that use violated data handling rules, bypassed procurement, or introduced unmanaged data transfer.
That is why network telemetry is most valuable when it feeds a broader control loop, not a one-off alert. Findings should inform allowlists, proxy rules, SaaS review, user education, and exception handling, especially where the traffic points to browser-based tools, personal accounts, or integrations that can move data outside approved channels.
Risk and Threat Considerations
Unsanctioned AI use creates exposure because the traffic often carries sensitive prompts, source material, or credentials to endpoints the organisation has not reviewed. The main risk is not just policy non-compliance, but uncontrolled data movement and a blind spot around where corporate information is being processed.
Failure mechanism: Users reach public or browser-based AI services through ordinary web and DNS paths, so the activity can look like routine SaaS traffic unless it is correlated with identity, device, and content-transfer patterns.
Impact: Organisations can miss shadow AI, fail to detect exfiltration, and lose the ability to enforce data handling rules before sensitive material is shared with an external service.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP API Security Top 10 addresses the attack and risk surface, while NIST CSF 2.0 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM-01 — Networks and network devices are monitored to detect potential cybersecurity events | Network telemetry is the core detection method for shadow AI traffic. |
| DE.AE-02 — Detected cybersecurity events are analyzed to understand attack targets and methods | Correlating traffic with identity and device context helps explain shadow AI use. | |
| PR.AA-05 — Identity assertions and credentials are validated | Identity correlation is needed to tie AI traffic to a responsible user or account. | |
| Recommendation — Monitor network traffic for unknown AI destinations and anomalous upload patterns. Correlate telemetry with user, device, and proxy context to classify suspicious AI use. Validate the user identity associated with the observed AI traffic before taking action. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Record Review, Analysis, and Reporting | Telemetry review and correlation are audit-analysis activities needed to spot shadow AI. |
| IA-5 — Authenticator Management | Unsanctioned AI often involves accounts, tokens, or API keys that should be controlled. | |
| Recommendation — Review proxy, DNS, and API logs for AI-related patterns and exception cases. Control and rotate credentials or tokens used to access AI services. | ||
| OWASP API Security Top 10 | API2 — Broken Authentication | Telemetry can expose unauthorized API access to AI services or gateways. |
| API8 — Security Misconfiguration | Misconfigured AI access paths often show up as unexpected traffic or endpoints. | |
| Recommendation — Inspect API calls to confirm they are authenticated through approved channels. Check AI-related traffic for misrouted or overly permissive service exposure. | ||
Practitioner Guidance
What to prioritise: Start with the highest-confidence telemetry joins, known AI service destinations, unusual DNS activity, and uploads that line up with users who should not be handling AI tools in that way. A weak single signal is rarely enough; a cluster of signals is what should trigger action.
What to verify: Confirm whether the destination is approved, whether the user has a legitimate business need, and whether the device or browser was used to move sensitive data. If you can only see traffic and cannot tell what was sent, treat the case as a visibility gap that needs tighter logging, not as a closed incident.
Practitioner takeaway: Network telemetry is most effective when it identifies both the AI destination and the context around the user action, because unsanctioned AI is usually a governance and data-handling problem long before it becomes a technical one.
Related resources from NHI Mgmt Group
- Guide: Unsanctioned AI Agents and MCP Servers Guide
- What should organisations do when browser telemetry reveals unsanctioned AI use?
- When should organisations use AI to help manage NHIs?
- What breaks when an AI agent can find and use exposed secrets in its workspace?
- Why do passwords remain dangerous when attackers use AI to find vulnerabilities?
Deepen Your Knowledge
Free weekly newsletter
Subscribe to the NHI & AI Identity Journal
The latest on NHI and Agentic AI security – articles, research, breaches, news and events every week.
Bonus 33% off our NHI Course when you subscribe.
Reviewed and updated by the NHIMG editorial team on November 9, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org