Why do traditional SIEM and EDR tools miss AI threats?

Why Traditional SIEM and EDR Miss AI Threats

SIEM and EDR are optimized to detect suspicious infrastructure activity, process execution, file changes, and endpoint indicators. AI threats often begin somewhere those tools do not model well: inside prompts, model outputs, orchestration logic, or tool-use decisions. That means the dangerous part is not always the prompt text itself, but the runtime interpretation that turns normal-looking input into an unsafe action. MITRE’s MITRE ATLAS adversarial AI threat matrix helps explain why these attack paths do not fit classic endpoint-centric detection.

This gap is visible in NHI-driven abuse as well. NHIMG research on LLMjacking: How Attackers Hijack AI Using Compromised NHIs shows how attackers target the credentials and identities behind AI services, not just the model itself. When those identities are compromised, the AI workload can be driven through legitimate channels that look routine to SIEM and EDR. In practice, many security teams encounter AI abuse only after an exposed secret, an unexpected tool call, or a downstream data leak has already occurred, rather than through intentional AI-specific detection.

How It Works in Practice

AI threats usually require three layers of visibility: the identity of the workload, the context of the request, and the decision made by the model or agent. Traditional SIEM sees logs after the fact. EDR sees what happens on the host. Neither inherently understands whether an AI assistant was induced to retrieve sensitive files, invoke an internal API, or chain tools in a way that violates policy. That is why current guidance suggests pairing infrastructure telemetry with AI-aware controls, as described in NHIMG’s 52 NHI Breaches Analysis and the OWASP NHI Top 10.

Operationally, defenders need controls that observe the AI control plane, not only the endpoint:

• Log prompt, tool, and retrieval events with sufficient context to reconstruct model behavior.
• Bind AI services to workload identity so requests can be attributed to the specific agent, pipeline, or app instance.
• Use policy-as-code at request time to decide whether a tool call, file access, or API invocation is allowed.
• Issue short-lived credentials or tokens per task, then revoke them automatically when the task ends.
• Correlate model activity with secrets access, data movement, and downstream system changes.

This is why emerging best practice is shifting toward runtime authorization and ephemeral credentials rather than static allowlists. A malicious instruction can be harmless in isolation and still trigger a privileged action once the model interprets it in context. Anthropic’s first AI-orchestrated cyber espionage campaign report illustrates how AI systems can be used to accelerate operational decision-making in ways that legacy detections are not designed to reason about. These controls tend to break down when agents have broad tool access across multiple SaaS, cloud, and code systems because the blast radius is distributed and the behavior is highly dynamic.

Common Variations and Edge Cases

Tighter AI monitoring often increases telemetry volume, policy overhead, and response complexity, requiring organisations to balance visibility against operational cost. There is no universal standard for this yet, so the implementation approach depends on whether the AI workload is a chat assistant, a code agent, a retrieval pipeline, or a multi-agent system. In lower-risk use cases, SIEM can still help with correlation and incident response, but it should not be treated as the primary control for AI behavior.

One important edge case is model abuse that never touches the endpoint in a way EDR can see. Another is prompt injection through retrieved content, where the attacker uses documents, webpages, or tickets as the delivery mechanism. A third is compromised NHI credentials used to call the model through legitimate APIs. NHIMG’s Ultimate Guide to NHIs — Why NHI Security Matters Now and The State of Secrets in AppSec reinforce the same point: identity and secrets hygiene are part of AI threat detection, not separate concerns. The practical takeaway is that organisations need AI-specific logging, identity controls, and runtime policy decisions, while SIEM and EDR remain supporting layers rather than complete detection coverage.

Related resources from NHI Mgmt Group

• Why do AI agents create more IAM risk than ordinary developer tools?
• How does the rise of AI identities impact traditional IAM systems?
• Why do AI agents create a different access-risk profile than traditional applications?
• Why do traditional discovery tools miss shadow AI agents?