AI for Cybersecurity: Threat Detection & Incident Response

AI is rapidly reshaping how security teams detect threats, investigate incidents, and run modern Security Operations Centers (SOCs). This course gives IT and security professionals a practical, non-math-heavy introduction to how AI and machine learning are applied across detection, investigation, and response—plus how attackers are using AI against you. You’ll see how anomaly detection, behavioral analytics, and pattern recognition fit into existing tools and workflows, and how AI can enrich alerts, prioritize cases, and accelerate incident timelines. Through concrete, security-focused examples, you’ll practice using AI-assisted techniques to analyze logs, spot deviations from normal behavior, and turn raw indicators of compromise into actionable threat intelligence. You’ll explore how machine learning helps classify malware and detect network intrusions, how AI can support vulnerability prioritization and attack surface analysis, and how to evaluate these capabilities in real tools. The course also dives into adversarial AI: how attackers scale phishing, deepfakes, credential attacks, and evasive malware using AI—and what defensive strategies can help you counter them. You’ll finish by designing an AI-enhanced SOC workflow that integrates alert enrichment, risk scoring, natural language security queries, and playbook-driven response, while considering ethics, privacy, bias, and regulatory requirements. By the end, you’ll be able to confidently discuss, evaluate, and apply AI capabilities within your security operations to help reduce mean time to detect (MTTD) and mean time to respond (MTTR).