Module 1: Basic AI concepts related to cybersecurity

• Explain core AI principles and terminology: Machine learning, deep learning, natural language processing, and automation.
• Identify AI applications in security: Use cases for AI in threat detection, defense, and security operations.
• Recognize AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use of generative AI.

Module 2: Securing AI systems

• Implement security controls: Protect AI systems, data, and models using robust technical safeguards.
• Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures.
• Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers.

Module 3: AI-assisted security

• Enhance detection and response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation.
• Automate security workflows: Integrate AI for event triage, alert correlation, and response orchestration.
• Apply AI techniques in operations: Incorporate AI into threat modeling,behavior analysis, and continuous monitoring.

Module 4: AI governance, risk, and compliance

• Understand regulatory frameworks: Identify global governance requirements and their implications for AI adoption.
• Integrate GRC into AI projects: Incorporate governance, risk management,and compliance practices throughout the AI lifecycle.
• Ensure responsible AI use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF.