Anomaly Detection for AI Agents: Catching What Your SIEM Cannot

Anomaly Detection for AI Agents: Catching What Your SIEM Cannot Your SIEM is good at detecting anomalies in systems that behave deterministically. AI agents do not. Traditional anomaly detection cannot tell whether an agent calling Stripe at 2am is legitimate or the result of prompt injection. Here is how to build detection that can. Why AI Agents Break Traditional Anomaly Detection Baseline is noisy. Agent behaviour depends on user inputs, which are unpredictable. You cannot set a normal API call volume. Intent is invisible to infrastructure tools. Your SIEM sees the HTTP request. Two identical API calls can have completely different risk profiles depending on why the agent made them. Prompt injection looks like legitimate traffic. An attacker manipulating your agent via injected prompts produces perfectly normal-looking API calls. The anomaly is in the decision chain, not the network traffic. What to Detect Behavioural Anomalies Signal Normal Anomalous Tool call volume 50-200/hour 84