Look for the users who combine frequent AI usage with multiple platforms, personal accounts, and direct connectors to enterprise data. Those power users often create a disproportionate share of the organisation’s exposure, so they should be the first group to receive monitoring, policy review, and targeted governance controls.
Who counts as the highest-risk AI user?
The highest-risk AI users are usually not the loudest users, they are the ones whose day-to-day behaviour creates the broadest blast radius. Look for people who use AI often, across several tools, through personal accounts, and with direct paths into enterprise data or production workflows. That combination tends to concentrate exposure, even when each individual action seems harmless.
The practical question is whether the user is acting as a simple consumer of AI output or as a high-leverage bridge between AI systems and sensitive business information. When a single user can move data between a browser assistant, a personal account, and internal systems, that user becomes a governance priority because policy gaps, logging gaps, and approval gaps all meet in one place.
Users also become higher risk when they sit at the intersection of experimentation and authority. A power user may be trusted to test new tools, connect them to shared data, or automate routine work, but those same freedoms make it easier to bypass intended controls. Teams should treat repeated AI usage plus broad access as a signal that the user may be creating an unsupervised shadow workflow, not just using a helpful productivity feature.
What signals show disproportionate AI exposure?
The strongest signal is not volume alone. It is the mix of frequency, variety, and sensitivity: frequent prompting, multiple AI services, account sprawl, plug-ins or connectors, and access to customer, financial, operational, or source-code data. A user with all of those traits can create more exposure than a whole department of occasional users because one mistake can propagate across several systems.
Personal accounts are especially important because they usually sit outside enterprise visibility and enforcement. If a user copies internal material into consumer AI tools, or uses a personal subscription alongside work systems, the organisation may lose policy enforcement, retention control, and record keeping at the same time. That makes the user worth flagging even before any specific incident occurs.
It also helps to look for behavioural indicators such as repeated approval requests, use of unvetted browser extensions, sharing prompts or outputs through unmanaged channels, and a pattern of connecting AI tools to more data sources over time. The risk rises when AI use is not just frequent, but increasingly embedded in work execution.
How should teams prioritise review and controls?
Prioritisation should start with the users who can combine AI access with sensitive data access and operational authority. Those are the people most likely to need a policy review first, because their usage pattern can reveal whether the organisation has accepted an invisible exception or an unmanaged integration path.
Teams should review the user’s account posture, connected tools, approved use cases, and data access together, rather than as separate lists. NIST Cybersecurity Framework 2.0 is useful here because it pushes teams to coordinate governance, protection, detection, and response instead of treating AI usage as a one-off exception. For user verification and strong authentication decisions, NIST SP 800-63 Digital Identity Guidelines helps teams separate routine access from higher-assurance access paths.
For users who connect AI to APIs, enterprise apps, or internal data stores, the review should be more than awareness training. It should confirm whether the integration is approved, whether the permissions are minimal, and whether the use of external services is visible to security and data owners. That is where OWASP API Security Top 10 is relevant, because AI connectors often fail in the same places as other exposed integration points: authorisation, inventory, and access scope.
Risk and Threat Considerations
High-risk AI users are attractive because they can turn ordinary productivity behaviour into data exposure, unauthorised access paths, or unreviewed automation. The main danger is not that they use AI, but that they can stitch together tools, accounts, and data sources faster than governance can see.
Failure mechanism: Frequent use across multiple platforms, especially with personal accounts and direct connectors, creates shadow workflows that bypass approved data handling, weaken traceability, and expand the impact of a single prompt, plugin, or credential mistake.
Impact: Sensitive data can leak into unmanaged services, enterprise controls can be bypassed, and one user can become a high-value entry point for policy violations, account misuse, or downstream compromise.
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 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-01 — Risk Management Strategy | Prioritising highest-risk AI users is a governance and risk prioritisation problem. |
| Recommendation — Define risk thresholds for AI user groups and prioritise oversight for the highest-exposure profiles. | ||
| NIST SP 800-63 | AAL — Authenticator Assurance Level | High-risk AI users often need stronger assurance before accessing sensitive tools and data. |
| Recommendation — Require higher-assurance authentication for users connecting AI to sensitive enterprise resources. | ||
| OWASP API Security Top 10 | API5 — Broken Function Level Authorization | AI connectors and tool access can fail when users reach functions beyond intended scope. |
| Recommendation — Verify that AI-enabled integrations cannot invoke functions beyond the user's approved scope. | ||
Practitioner Guidance
What to prioritise: Start with users who combine heavy AI use with sensitive data access, external connectors, and personal-account activity. Those are the profiles most likely to justify immediate monitoring and governance review.
What to verify: Confirm which tools are connected, what data classes they can reach, and whether the user’s AI activity is visible in logs or covered by an approved policy. If any of those are missing, treat the user as a priority case rather than a routine adopter.
Decision rule: If a user can move enterprise data into an AI service outside managed controls, prioritise that user over someone with higher message volume but no sensitive connectors.
Practitioner takeaway: The best risk signal is not who uses AI the most, but who can combine AI usage with data access, account sprawl, and hidden integration paths in a way that widens blast radius.
Related resources from NHI Mgmt Group
- How should security teams handle risks from AI browser extensions?
- How should security teams govern API keys used for generative AI access?
- How should teams reduce the risk from overprivileged NHIs?
- How should security teams correlate identity and data context to find the highest-risk exposures in AI and SaaS environments?