How should security teams handle AI tool visibility when most usage is legitimate but some activity is suspicious?

Why This Matters for Security Teams

AI tools often sit in a gray zone: they are part productivity software, part external dependency, and part potential attacker channel. That makes visibility harder than with conventional applications, because most activity is normal until it is not. Security teams need enough telemetry to distinguish approved assistants from unsanctioned use, while preserving the ability to investigate suspicious process chains, unusual sign-ins, and unexpected outbound connections. NIST SP 800-53 Rev 5 Security and Privacy Controls remains useful here because it frames monitoring, access control, and auditability as linked controls rather than separate tasks.

The practical risk is not just shadow IT. AI interfaces can be abused for phishing, data exfiltration, token theft, or scripted hands-on-keyboard activity that looks legitimate at first glance. Teams that treat AI usage as a simple allow or block decision usually miss the more important question: which identities, devices, and workflows are trusted enough to use AI, and what signals should be considered abnormal for each group? In practice, many security teams encounter the malicious use of an approved AI tool only after identity abuse or endpoint compromise has already occurred, rather than through intentional monitoring design.

How It Works in Practice

The most reliable approach is to build a baseline of normal AI adoption across identity, endpoint, network, and SaaS telemetry, then measure deviations against that baseline. That usually means correlating sign-ins, process creation, DNS lookups, browser activity, and command-line arguments with the user, device, and vendor domain involved. If a tool is approved, its routine access patterns should become recognizable quickly. If the same account begins invoking the tool from new geographies, a new device class, or an unusual parent process, the event deserves review.

Operationally, teams should classify AI usage into at least three buckets: sanctioned business use, unsanctioned but non-malicious use, and suspicious use. That helps avoid overreacting to normal experimentation while still surfacing risk. Useful monitoring signals include:

• Identity anomalies such as impossible travel, unfamiliar device posture, or repeated failed sign-ins before successful access.
• Endpoint anomalies such as script launchers spawning browsers, archive utilities, or remote access tools around AI sessions.
• Network anomalies such as nonstandard DNS requests, unexpected API destinations, or traffic to newly seen domains.
• Workflow anomalies such as large prompt bursts, copy-paste into sensitive systems, or AI usage immediately before data movement.

For governance, pair detection with policy. Approved AI tools should be tied to specific identities, business purposes, and data handling rules. Where possible, enforce conditional access and logging that make the user, the device, and the session context visible together. Guidance from CISA's Zero Trust Architecture guidance is relevant because it reinforces continuous verification instead of static trust. These controls tend to break down in highly decentralized environments where users can install browser extensions, create personal accounts, and bypass centralized telemetry through unmanaged devices.

Common Variations and Edge Cases

Tighter AI monitoring often increases privacy, operational, and logging overhead, requiring organisations to balance visibility against user experience and data minimisation. There is no universal standard for exactly which AI telemetry every enterprise must collect, so best practice is evolving based on business risk, regulatory exposure, and the sensitivity of the data being handled.

Some environments need stronger controls than others. In regulated industries, prompts and outputs may be subject to retention or audit obligations, while in research or engineering teams, aggressive inspection can create friction and drive users to unsanctioned tools. The key tradeoff is that broad blocking can reduce exposure but also reduce visibility, because teams lose the signals that reveal misuse. Current guidance suggests focusing on proportionate monitoring: enough to detect abnormal behavior, not so much that every legitimate use looks suspicious.

Edge cases matter. Shared workstations, contractors, BYOD, and virtual desktops can make identity-to-device correlation messy, and agentic workflows can produce legitimate bursts of activity that resemble automation abuse. If the organisation uses OWASP guidance for large language model applications, it should be adapted to the actual toolchain rather than applied as a generic checklist. The safest operational model is to treat ai visibility as an identity-and-telemetry problem first, then refine the policy layer as usage patterns mature.

Related resources from NHI Mgmt Group

• How should security teams handle AI agent visibility?
• How should security teams handle tool discovery for AI agents in MCP environments?
• How should security teams handle AI app usage that appears only in browser sessions?
• How should security teams handle risks from AI browser extensions?