What is the role of anomaly detection in AI incident response?

• A. To identify deviations from normal behavior for early detection of threats.
• B. To enforce password policies.
• C. To archive all events for compliance.
• D. To automatically patch vulnerabilities.

Answer: (A)

Anomaly detection in AI incident response focuses on spotting patterns that deviate from what’s normal. By establishing baselines of typical system activity—such as login behavior, API usage, network traffic, and performance metrics—the detection system flags events that don’t fit those patterns. Those deviations often signal security threats, misconfigurations, or compromised assets, allowing responders to investigate sooner, isolate affected components, and contain incidents before they escalate. This approach directly supports faster detection and more meaningful alerts, helping to tie together disparate signals into a coherent incident context within the response workflow.

Enforcing password policies is a preventive control that governs credential requirements, not detection. Archiving all events for compliance is about data retention and auditability, not identifying anomalies in real time. Automatically patching vulnerabilities is a remediation action within vulnerability management, not detecting suspicious activity.