📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI for Cybersecurity: Threat Detection and Risk Mitigation, from foundations to a certified capstone project.
Telemetry
• Endpoint, network and identity telemetry: coverage gaps and blind spots
• Mapping detections to MITRE ATT&CK techniques rather than tool alerts
• Log normalisation, enrichment and the cost of retention decisions
Detection
• Baselining user and entity behaviour, and handling legitimate change
• Supervised detection where labels exist; unsupervised where they do not
• Beaconing, lateral movement and exfiltration signatures in practice
• Alert fatigue as the primary failure mode of security analytics
Adversarial
• Evasion of malware and phishing classifiers
• Data poisoning of models trained on customer telemetry
• Prompt injection and data exfiltration through LLM-based tooling
Response
• Risk scoring and alert triage that preserves analyst judgement
• SOAR playbooks and choosing what is safe to automate
• Measuring detection engineering: time to detect, time to contain, coverage
Programme
• Threat modelling for AI-enabled systems and their supply chain
• Purple-team validation of detections against live technique emulation
• Reporting risk reduction in terms an executive can act on
Earn government-registered certification in AI for Cybersecurity: Threat Detection and Risk Mitigation
e-Certificate and e-Marksheet issued on successful completion.
