What You’ll Learn: AI for Proactive Cyber Defense
Move beyond signature-based alerts—deploy AI that sees patterns, predicts breaches, and responds at machine speed.
Detect zero-day attacks and sophisticated threats using supervised and unsupervised ML.
Spot insider threats and compromised accounts through user/entity behavior analytics (UEBA).
Reduce alert fatigue with AI that prioritizes, enriches, and suggests responses.
Ensure transparency, auditability, and bias mitigation in high-stakes security decisions.
Who Should Enrol?
For cybersecurity professionals ready to augment human expertise with intelligent, scalable defense systems.
- Cybersecurity Analysts & Threat Hunters
- SOC Engineers & Incident Responders
- IT Security Managers & CISOs
- Security Operations Professionals
Hands-On Cybersecurity AI Labs
Malware Behavior Classifier
Train an ML model to distinguish benign from malicious process behavior using system logs.
UEBA for Insider Threats
Detect anomalous user activity in simulated enterprise authentication and file access data.
AI-Enhanced SOC Playbook
Design an end-to-end AI-augmented incident response workflow for your organization.
3-Week Cybersecurity AI Syllabus
~36 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: Foundations of AI in Cybersecurity
- Limits of rule-based detection and the rise of ML
- Data sources: logs, netflow, EDR, threat intel
- Ethical risks: bias, privacy, and adversarial attacks
Week 2: Threat Detection & Anomaly Analytics
- Supervised vs. unsupervised threat models
- UEBA for user and entity behavior
- Reducing false positives with contextual AI
Week 3: SOC Automation & Certification
- Integrating AI into SOAR and ticketing systems
- Explainability and audit trails for compliance
- Certification prep & capstone submission
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
Yes—this is an advanced course designed for professionals with foundational knowledge of cybersecurity operations, logging systems (e.g., Splunk, ELK), and threat landscapes.
Yes! You’ll work with anonymized enterprise logs, network traffic data, and malware behavior datasets to train and test AI-powered detection models.