AI Malware Detection in the SOC: Operations, Alerts, and Analyst Workflow

Even the strongest classifier becomes a liability if it floods analysts with false positives, hides important context, or never gets retrained as the threat landscape shifts. This course steps past the modeling and into the operational discipline that decides whether AI-driven malware detection actually helps a security operations center. You will work through written scenarios that mirror the rollout, first quarter, and steady-state operation of an AI-driven detection capability inside a SOC. The course also addresses the analyst side, including how to present model decisions in a way that supports rather than replaces human judgment. What you'll learn: - Plan rollout in phases that build trust with analysts, threat hunters, and incident responders - Design alert presentation that includes model confidence, supporting evidence, and recommended actions - Integrate AI detection with SIEM, EDR, and ticketing systems in a way that respects analyst workflow - Build monitoring dashboards that surface model drift, false positive trends, and missed detections - Run continuous tuning cycles that incorporate analyst feedback into retraining datasets - Plan for adversarial conditions including evasion attempts and concept drift as attackers adapt The course begins with the rollout phase, moves through alert presentation and integration, and finishes with monitoring and adversarial considerations. A capstone written exercise asks you to draft a one-year operations plan for AI-driven malware detection in a real SOC scenario. This course is designed for security operations leaders, senior analysts, and technology integrators bringing AI tools into established SOCs. No prior AI experience is required. The course respects how real security teams work and treats trust, analyst workflow, and adversarial resilience as first-class concerns.