Duration: 1 hour

• Introduction to AI, ML, and Data Science in the context of cybersecurity
• Evolution of AI in cybersecurity: History, present, and future
• Benefits and challenges of using AI in cybersecurity

Activity: Group discussion on known AI-based cybersecurity tools and technologies

Duration: 1.5 hours

• Types of machine learning: Supervised, unsupervised, and reinforcement learning
• Key algorithms used in cybersecurity: Classification, clustering, anomaly detection
• Overview of natural language processing (NLP) for threat intelligence

Activity: Short quiz to reinforce AI/ML concepts

Duration: 2 hours

• AI for intrusion detection and prevention (IDS/IPS)
• Leveraging machine learning for real-time threat detection
• Understanding anomaly detection vs. behavioral analysis
• Case studies: AI for threat hunting

Hands-On Exercise: Setting up a basic machine learning model for anomaly detection in a network dataset

Duration: 1.5 hours

• AI in endpoint protection and response (EPP & EDR)
• Predictive modeling for malware detection
• Automated responses and AI in SOAR (Security Orchestration, Automation, and Response)
• Case study on using AI for phishing detection

Activity: Interactive group discussion on key AI tools used in the industry (e.g., Darktrace, CrowdStrike)

Duration: 1 hour

• Data privacy and ethical concerns in AI
• Common issues: Data quality, labeling, and false positives
• Resource and skill requirements
• Discussing real-world limitations of AI in cybersecurity

Activity: Group exercise on identifying potential biases and ethical issues in AI applications