How Artificial Intelligence (AI) Can Increase Threat Detection and Response

AI is becoming foundational to modern threat detection, investigation, and response (TDIR). Core AI technologies used in cybersecurity include supervised ML for alert classification, behavioral analytics for anomaly detection, graph analytics for lateral movement mapping, and LLMs for incident summarization. AI-powered TDIR compresses detection-to-response timelines, reduces noise (one real alert per 138 million observations in one platform), and improves detection of credential-based attacks and data exfiltration. Key challenges include data quality gaps, model drift, explainability requirements, governance concerns, and over-reliance on automation. Best practices include starting with a threat model, engineering telemetry rigorously, keeping humans in the loop for disruptive actions, and ensuring 24x7 coverage since 51% of attacks occur outside business hours.