Intro to AI Security | BMCC Cybersecurity & AI Programme

Course Overview

This course explores the emerging field of AI security, covering threat landscapes unique to artificial intelligence systems, adversarial attacks, model robustness, and governance frameworks. Learn how to secure machine learning systems, identify AI-specific vulnerabilities, and implement responsible AI practices.

Perfect for security professionals entering the AI era, data scientists strengthening model security, or anyone seeking foundational knowledge of AI governance and safety. Includes case studies of real-world AI security incidents.

Cost

FREE

Duration

4 weeks

Pace

Self-Paced

Certificate

Included

Course Curriculum

Week-by-week learning path

Week 1: AI Threat Landscape

Unique vulnerabilities in AI systems. Types of AI attacks (data poisoning, model extraction, adversarial examples). Attack surface of ML pipelines. Real-world AI security breaches and incident case studies.

Week 2: Adversarial Attacks & Defense

Adversarial examples and perturbations. Evasion and poisoning attacks. Defense mechanisms and model robustness. Testing AI systems for vulnerabilities. Interpretability in security contexts.

Week 3: Machine Learning Security

Securing ML pipelines from data to deployment. Data privacy and differential privacy. Model validation and testing best practices. Supply chain security for ML systems. Backdoor attacks and detection.

Week 4: AI Governance & Ethics

Responsible AI principles. Bias detection and mitigation. Regulatory frameworks (EU AI Act, NIST AI RMF). Transparency and accountability in AI systems. AI security roadmap for organizations.

What You'll Learn

AI-Specific Threats

Understand vulnerabilities unique to machine learning systems. Learn attack methodologies and real-world exploitation patterns. Develop intuition for emerging AI security risks.

Security Controls

Implement defense mechanisms for AI systems. Apply robustness testing, adversarial training, and validation techniques. Secure ML pipelines from development to production deployment.

Governance Framework

Navigate AI governance, ethics, and regulatory landscapes. Understand responsible AI practices, bias mitigation, and organizational accountability measures for AI systems.

Next Steps

For AI Security Specialists

Complete this course, then advance to BMCC's HALT AI Hack (intensive 3-hour hackathon) or full WhiteHat curriculum. Build specialized expertise in an emerging, high-demand career field with premium compensation.

For Data Scientists

Strengthen model security and governance knowledge. Understand how to build trustworthy, robust AI systems that withstand adversarial attacks and maintain regulatory compliance across industries.

Introduction to AI Security