Adversarial Machine Learning Training Course

Adversarial Machine Learning Training Course

Introduction

Adversarial Machine Learning Training Course is designed to equip cybersecurity professionals, data scientists, AI engineers, and risk specialists with advanced skills to identify, analyze, and mitigate machine learning security threats. As artificial intelligence adoption accelerates across industries, adversaries are developing sophisticated techniques to exploit AI models, deep learning systems, neural networks, and automated decision-making pipelines. This course explores the emerging field of AI security, adversarial attacks, model robustness, machine learning threat intelligence, and trustworthy AI engineering.

Participants will gain practical knowledge of adversarial examples, data poisoning, model evasion, model extraction, privacy attacks, AI red teaming, and defensive machine learning strategies. Through hands-on exercises, simulations, and real-world case studies, learners will develop the ability to secure AI systems against evolving cyber threats. The course emphasizes secure AI development lifecycle practices, explainable AI (XAI), responsible AI governance, threat modeling, and resilient machine learning architectures to help organizations build reliable and attack-resistant AI solutions.

Course Duration

 5 Days

Course Objectives

1. Understand the foundations of Adversarial Machine Learning (AML) and AI cybersecurity risks. 
2. Identify emerging machine learning attack vectors and threat landscapes. 
3. Analyze adversarial examples and model evasion techniques affecting AI systems. 
4. Apply AI threat modeling methodologies to evaluate ML security risks. 
5. Perform data poisoning and training-data security assessments. 
6. Implement robust machine learning defenses against adversarial manipulation. 
7. Conduct AI red teaming and adversarial testing exercises. 
8. Evaluate vulnerabilities in deep learning, neural networks, and generative AI models. 
9. Apply privacy-preserving machine learning techniques. 
10. Secure ML pipelines using MLOps security and DevSecOps practices. 
11. Improve model reliability through AI robustness engineering. 
12. Implement explainable AI and responsible AI security frameworks. 
13. Develop organizational strategies for AI risk management and secure AI governance. 

Target Audience

1. Machine Learning Engineers 
2. Data Scientists and AI Researchers 
3. Cybersecurity Professionals 
4. Security Operations Centre (SOC) Analysts 
5. AI Security Engineers 
6. Penetration Testers and Ethical Hackers 
7. Risk, Compliance, and Governance Professionals 
8. Software Developers Building AI Applications 

Course Modules

Module 1: Introduction to Adversarial Machine Learning

• Fundamentals of AI security and adversarial machine learning
• Evolution of machine learning threats and attack surfaces 
• Relationship between cybersecurity and AI model vulnerabilities 
• Overview of adversarial AI frameworks and research trends 
• Case Study: Security analysis of image recognition models exposed to adversarial manipulation 

Module 2: Adversarial Examples and Model Evasion Attacks

• Understanding adversarial examples in machine learning systems 
• Fast Gradient Sign Method (FGSM) and gradient-based attacks 
• Black-box and white-box adversarial attack strategies 
• Evasion attacks against classification and detection models 
• Case Study: Autonomous vehicle AI systems impacted by adversarial inputs 

Module 3: Data Poisoning and Training Data Attacks

• Understanding malicious manipulation of training datasets 
• Backdoor attacks and hidden model behaviors 
• Data integrity, validation, and provenance challenges 
• Detecting poisoned datasets using security analytics 
• Case Study: Healthcare AI model risks caused by compromised training data 

Module 4: Model Extraction, Inversion, and Privacy Attacks

• Techniques used to steal machine learning models 
• Model extraction and intellectual property risks 
• Membership inference and model inversion attacks 
• Protecting sensitive AI models and training information 
• Case Study: Protecting commercial AI APIs from model theft attempts 

Module 5: Deep Learning and Generative AI Security

• Adversarial threats targeting deep neural networks 
• Security challenges in Large Language Models (LLMs) 
• Attacks against generative AI applications 
• Prompt manipulation and AI system exploitation risks 
• Case Study: Securing enterprise AI assistants against adversarial inputs 

Module 6: Defensive Machine Learning and Robust AI Engineering

• Developing adversarially robust machine learning models 
• Adversarial training and defensive optimization methods 
• Model validation, monitoring, and security testing 
• Building resilient AI development pipelines 
• Case Study: Improving fraud detection models against adversarial attacks 

Module 7: AI Red Teaming and Security Testing

• Principles of adversarial AI penetration testing 
• AI vulnerability assessment methodologies 
• Automated tools for adversarial attack simulation 
• Creating AI security testing frameworks 
• Case Study: Red team assessment of an enterprise AI decision system 

Module 8: AI Governance, Risk Management, and Future Trends

• AI security governance frameworks and standards 
• Managing adversarial ML risks across organizations 
• Responsible AI, transparency, and accountability practices 
• Future challenges in autonomous AI security 
• Case Study: Developing an AI risk management strategy for a financial institution 

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.