AI Red Teaming Training Course

AI Red Teaming Training Course

Introduction

AI Red Teaming Training Course is designed to equip cybersecurity professionals, AI engineers, security researchers, and technology leaders with advanced skills to identify, exploit, and mitigate vulnerabilities in Artificial Intelligence (AI) systems, Machine Learning (ML) models, Large Language Models (LLMs), and Generative AI applications. As organizations rapidly adopt AI-driven solutions, the need for AI security testing, adversarial simulation, prompt injection defense, model vulnerability assessment, AI threat intelligence, and responsible AI governance has become critical. This course provides practical expertise in performing AI penetration testing, adversarial attacks, jailbreak analysis, model robustness evaluation, data poisoning detection, and AI risk management aligned with emerging industry standards.

Participants will learn how to operate as professional AI Red Team specialists, applying offensive security methodologies to discover weaknesses before malicious actors exploit them. Through hands-on labs, attack simulations, and real-world case studies, learners explore LLM security testing, AI attack frameworks, adversarial machine learning, agentic AI security, AI incident response, and secure AI development practices. The course enables organizations to build resilient AI ecosystems by integrating continuous AI security validation, threat modeling, ethical hacking techniques, and proactive defense strategies into their AI lifecycle.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand the principles of AI Red Teaming, offensive AI security, and adversarial testing methodologies. 
2. Perform AI vulnerability assessments across ML models, LLMs, and Generative AI applications. 
3. Identify and exploit AI-specific security weaknesses and attack surfaces. 
4. Conduct prompt injection, jailbreak, and LLM manipulation testing. 
5. Apply adversarial machine learning techniques to evaluate model robustness. 
6. Execute AI penetration testing and security validation exercises. 
7. Detect and mitigate data poisoning, model theft, and model inversion attacks. 
8. Implement AI threat modeling and risk assessment frameworks. 
9. Analyze AI supply chain security risks and third-party model vulnerabilities. 
10. Design effective AI security controls and defensive countermeasures. 
11. Use modern AI security tools, frameworks, and automation platforms. 
12. Establish Responsible AI, governance, and compliance practices. 
13. Develop professional capabilities for AI security engineering and red team operations. 

Target Audience

1. Cybersecurity professionals and penetration testers 
2. AI security engineers and researchers 
3. Machine Learning engineers and data scientists 
4. Security Operations Centre (SOC) analysts 
5. Cloud security architects and DevSecOps professionals 
6. Risk management and compliance specialists 
7. AI product managers and technology leaders 
8. Ethical hackers and red team professionals 

Course Modules

Module 1: Foundations of AI Red Teaming

• Introduction to AI security and offensive AI operations
• Understanding AI attack surfaces and threat landscapes 
• AI Red Team lifecycle and engagement methodology 
• Differences between traditional red teaming and AI red teaming 
• Building AI threat intelligence capabilities 
• Case Study: Enterprise Generative AI Security Assessment

Module 2: AI Threat Modeling and Attack Surface Analysis

• AI system architecture and security mapping 
• Identifying vulnerabilities in AI pipelines 
• Threat modeling frameworks for AI systems 
• Mapping AI assets, risks, and attack vectors 
• Evaluating third-party AI model risks 
• Case Study: AI Supply Chain Attack Simulation

Module 3: Large Language Model (LLM) Red Teaming

• LLM architecture and security challenges 
• Prompt injection attack techniques 
• Jailbreaking and model behavior manipulation 
• Context poisoning and data leakage testing 
• LLM vulnerability assessment frameworks 
• Case Study: Chatbot Jailbreak Assessment

Module 4: Adversarial Machine Learning Attacks

• Fundamentals of adversarial ML 
• Evasion attacks against AI models 
• Data poisoning techniques and detection 
• Model extraction and inversion attacks 
• Improving AI model robustness 
• Case Study: Computer Vision Model Attack Testing

Module 5: Generative AI Security Testing

• Generative AI threat landscape 
• AI content manipulation risks 
• Testing AI agents and autonomous workflows 
• Securing Retrieval-Augmented Generation (RAG) systems 
• Evaluating AI hallucination and reliability risks 
• Case Study: Enterprise RAG Application Security Review

Module 6: AI Penetration Testing Tools and Techniques

• AI security testing frameworks and toolsets 
• Automated AI vulnerability scanning 
• Security testing of ML APIs 
• AI exploit development methodologies 
• Building AI attack simulation environments 
• Case Study: AI API Security Assessment

Module 7: AI Defense, Mitigation, and Secure AI Engineering

• AI security controls and defensive strategies 
• Secure model development practices 
• Input validation and monitoring techniques 
• AI governance and compliance controls 
• Continuous AI security improvement 
• Case Study: Healthcare AI Protection Program

Module 8: Advanced AI Red Team Operations and Incident Response

• Planning AI Red Team engagements 
• Reporting AI vulnerabilities professionally 
• AI security incident response processes 
• Continuous AI attack simulation 
• Building enterprise AI security programs 
• Case Study: AI Security Breach Response Exercise

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.