How can organisations tell normal AI use from suspicious AI use?

Why This Matters for Security Teams

Normal AI use is not defined by the model alone, but by the surrounding context: who invoked it, what data it touched, which tools it used, and whether the action matches an expected work pattern. That is why a reviewer summarising case material may be routine, while an agent exporting source code, generating fresh DeepSeek breach style secret spillage, or reaching for credentials outside its normal task boundary should trigger scrutiny. Current guidance suggests teams should classify AI activity by risk and intent, not by the fact that “AI was involved,” which aligns with the NIST Cybersecurity Framework 2.0 emphasis on risk-based governance and monitoring.

The practical issue is that suspicious AI use often looks productive at first glance. Autonomous workflows can chain actions quickly, making a harmful sequence appear like a legitimate acceleration of business output. That creates blind spots for teams that rely on coarse allowlists or static role definitions. In practice, many security teams encounter the real abuse only after data has already been copied, transformed, or exfiltrated, rather than through intentional detection.

How It Works in Practice

The most reliable approach is context-aware classification. Teams should evaluate each AI action against the user or workload role, the time of request, the data sensitivity, the destination system, and whether the activity fits a known business process. A legal assistant model drafting a contract is normal; the same identity requesting production secrets, bulk file movement, or privileged API calls at 2 a.m. is not. This is where NIST Cybersecurity Framework 2.0 and DeepSeek breach lessons both point in the same direction: focus on exposure-changing behaviour, not just identity labels.

A practical workflow usually includes:

• Baseline normal patterns for each AI workload, business unit, and user cohort.
• Flag deviations in timing, tool use, data volume, and privilege escalation.
• Treat secrets, tokens, and API keys as high-signal objects because they can immediately expand access.
• Require step-up review for actions that export, transform, or relay sensitive information.
• Log intent, input context, and downstream effects so investigators can reconstruct why the action happened.

For agentic systems, the bar is higher. A model with tool access may behave safely for weeks and then suddenly combine search, retrieval, and credential use in a way no human operator anticipated. That is why intent-based authorization and short-lived credentials are becoming more important than static RBAC alone, especially when NIST Cybersecurity Framework 2.0 style monitoring is paired with runtime policy checks. These controls tend to break down when organisations cannot distinguish one workflow from another because logging, identity, and data classification are fragmented across too many systems.

Common Variations and Edge Cases

Tighter detection often increases review burden and false positives, requiring organisations to balance security signal against operational speed. That tradeoff matters because not every unusual AI action is malicious. A finance bot may query a new dataset at month-end, a developer assistant may touch unfamiliar repositories during an incident, and an analyst may work at odd hours during a launch window. Best practice is evolving, and there is no universal standard for this yet, so the decision should be made with local context rather than rigid thresholds alone.

The hardest edge cases are autonomous agents, shared service accounts, and environments where humans and models use the same tools. In those settings, the identity primitive matters: a workload identity, strong telemetry, and just-in-time access provide better evidence than user-devised naming conventions. The DeepSeek breach also shows why secret exposure should be treated as a suspicious signal even when no malware is present. If an AI action exposes tokens, moves laterally, or creates persistence, the behaviour has crossed from normal productivity into security-relevant activity. Organisations that do not separate routine automation from goal-driven agent behaviour often misclassify the first warning signs until after privilege has already expanded.

Related resources from NHI Mgmt Group

• When should organisations use AI to help manage NHIs?
• How can organisations govern AI agents that use service accounts and tokens?
• When should organizations use MCPs for AI identity management?
• How should teams govern AI agents that use MCP?