Overview What You Will Learn Curriculum Instructor

Course Overview

Artificial Intelligence security is essential as organizations increasingly deploy machine learning models, generative AI applications, large language models, and autonomous AI agents. Securing these systems requires specialized testing and defense techniques to identify adversarial threats, prevent data exposure, protect AI infrastructure, and strengthen resilience against emerging attacks. This course begins with AI security fundamentals, red, blue, and purple teaming, AI attack surface mapping, threat modeling, and adversarial machine learning. It explores MITRE ATLAS, OWASP LLM and Generative AI security, prompt injection, jailbreak testing, retrieval-augmented generation (RAG), vector databases, agentic AI security, automated red teaming, defensive architecture, telemetry, detection engineering, incident response, digital forensics, AI supply-chain security, continuous validation, and regulatory testing through practical security labs and an end-to-end capstone exercise. You will develop practical skills to assess AI vulnerabilities, conduct adversarial testing, implement defensive controls, detect threats, and validate AI security through collaborative red, blue, and purple team exercises.

What You Will Learn

  • Understand AI security fundamentals, modern AI architectures, attack surfaces, trust boundaries, and threat modeling techniques.
  • Perform AI adversarial testing using NIST adversarial machine learning concepts, MITRE ATLAS techniques, and OWASP LLM security methodologies.
  • Identify and test vulnerabilities in generative AI, RAG pipelines, vector databases, agentic AI systems, and tool-connected AI applications.
  • Implement defensive controls, automated security testing, telemetry monitoring, threat detection, incident response, and AI forensic analysis.
  • Conduct collaborative red, blue, and purple team exercises, validate security controls, assess AI supply-chain risks, and produce comprehensive security assessment reports.

Program Curriculum

  • AI Security Fundamentals
  • Red, Blue, and Purple Teaming for AI
  • Chapter 1 Quiz

  • Modern AI System Architecture
  • AI Attack Surface Model
  • Trust Boundaries in AI Systems
  • Chapter 2 Quiz

  • Threat Modeling AI Systems
  • Engagement Scoping and Rules of Engagement
  • Red Team Test Planning
  • Chapter 3 Quiz

  • Adversarial ML Foundations
  • Evasion Attacks
  • Poisoning Attacks
  • Privacy Attacks, Model Extraction, and Misuse
  • Adversarial ML Defenses and Lab
  • Chapter 4 Quiz

  • Understanding MITRE ATLAS and Current AI Adversary Techniques
  • ATLAS-based Attack Scenarios for Red and Blue Teams
  • Chapter 5 Quiz

  • OWASP LLM and GenAI Security
  • Prompt Injection Testing
  • Jailbreak Methodology
  • Information Disclosure, Safety, and Misuse Red Teaming
  • OWASP GenAI Red Teaming Methodology and LLM Red Team Lab
  • Chapter 6 Quiz

  • RAG Security Architecture and Attack Techniques
  • Vector Database Security and RAG Blue Team Controls
  • RAG Security Lab
  • Chapter 7 Quiz

  • Agentic AI Architecture and OWASP Agentic AI Risks
  • Tool-connected AI and MCP-style Security
  • Identity and Authorization for AI Agents
  • Agentic AI Red Teaming and Security Lab
  • Agentic AI Defense
  • Chapter 8 Quiz

  • Red Team Automation Principles and Tooling
  • Automated Test Harnesses and Continuous Security Testing
  • Chapter 9 Quiz

  • AI Defensive Control Stack
  • Input, Context, and Output Defenses
  • Model and Runtime Protection and System Hardening
  • Chapter 10 Quiz

  • AI Security Telemetry
  • Detection Hypotheses and AI-specific Detection Use Cases
  • Statistical and Behavioral Detection and SIEM and SOC Integration
  • Detection Engineering Lab
  • Chapter 11 Quiz

  • AI Incident Classification and Response Lifecycle
  • AI-specific Containment
  • AI Forensic Analysis
  • Recovery, Post-incident Improvement, and Incident Response Tabletop
  • Chapter 12 Quiz

  • AI Supply-chain Components and Threats
  • AI Provenance, Integrity, and Supply-chain Security Controls
  • Chapter 13 Quiz

  • AI Purple Team Methodology
  • Detection Validation and Control Validation
  • Continuous Validation and Red/Blue/Purple Metrics
  • Chapter 14 Quiz

  • AI Security Assurance, Red Team Reporting, and the Red Team Program
  • NIST AI RMF, NIST Cyber AI Profile, and the EU AI Act
  • Chapter 15 Quiz

  • Target System Assessment, Threat Model, and Attack Plan
  • Red Team Execution, Blue Team Validation, Purple Team Retest, and Final Reporting
  • Chapter 16 Quiz
Load more modules

Instructor

Simmi Garg

Simmi Garg is a data privacy educator with a strong focus on the Digital Personal Data Protection (DPDP) Act, 2023, and the evolving global privacy regulatory landscape. She specializes in simplifying complex legal and compliance requirements into practical, easy-to-understand concepts for privacy professionals, compliance practitioners, and business leaders. Her areas of expertise include privacy governance, privacy-by-design, consent management, data subject rights, DPIAs, privacy risk management, and organizational privacy frameworks. Through her courses, Simmi emphasizes practical implementation, real-world scenarios, and regulatory interpretation to help learners confidently apply privacy principles within their organizations. By combining deep knowledge of India's DPDP framework with an understanding of international privacy best practices, she equips professionals with the skills needed to build effective privacy programs and navigate an increasingly complex regulatory environment.

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