AI Data Poisoning and Model Security Training Course

AI Data Poisoning and Model Security Training Course

Introduction

AI Data Poisoning and Model Security Training Course is designed to equip cybersecurity professionals, AI engineers, data scientists, and technology leaders with advanced skills to protect Artificial Intelligence (AI) systems, Machine Learning (ML) models, and Large Language Models (LLMs) from emerging adversarial threats. As organizations increasingly deploy AI-driven solutions, attackers are targeting training datasets, model pipelines, and inference environments through sophisticated techniques such as data poisoning attacks, adversarial manipulation, model theft, backdoor attacks, supply chain compromise, and AI integrity exploitation. This course provides a comprehensive understanding of AI security engineering, trustworthy AI, secure machine learning operations (MLOps), data governance, model validation, and cyber resilience strategies.

Participants will gain practical knowledge of identifying, preventing, and responding to AI model vulnerabilities through threat modeling, dataset protection, anomaly detection, adversarial testing, secure AI lifecycle management, and AI risk governance frameworks. Using real-world case studies and hands-on exercises, learners will explore how organizations can build robust, transparent, explainable, and secure AI ecosystems while maintaining compliance with evolving AI security standards. The course prepares professionals to defend AI systems against modern cyber threats and establish strong AI assurance, model security, and responsible AI practices.

Course Duration

5 days

Course Objectives

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

1. Understand advanced AI data poisoning techniques and machine learning attack surfaces. 
2. Identify vulnerabilities in AI training datasets, pipelines, and model architectures. 
3. Implement secure AI lifecycle management and model protection strategies. 
4. Apply adversarial machine learning defense techniques. 
5. Develop effective AI threat modeling and risk assessment frameworks. 
6. Detect and mitigate backdoor attacks and malicious model manipulation. 
7. Strengthen MLOps security and AI supply chain protection. 
8. Apply data integrity, validation, and governance controls. 
9. Perform AI red teaming and security testing activities. 
10. Implement robust model monitoring and anomaly detection mechanisms. 
11. Understand privacy-preserving machine learning security practices. 
12. Establish AI governance, compliance, and responsible AI frameworks. 
13. Design resilient enterprise AI security architectures. 

Target Audience

1. AI Security Engineers 
2. Machine Learning Engineers 
3. Data Scientists and Data Engineers 
4. Cybersecurity Professionals 
5. Security Operations Centre (SOC) Analysts 
6. Cloud Security Architects 
7. AI Product Managers and Technology Leaders 
8. Risk, Compliance, and Governance Professionals 

Course Modules

Module 1: Fundamentals of AI Data Poisoning and Model Threats

• Introduction to AI security challenges and emerging attack landscapes 
• Understanding machine learning pipelines and attack surfaces 
• Types of data poisoning attacks and adversarial threats 
• AI model vulnerabilities across development and deployment stages 
• Building an AI security threat intelligence framework 
• Case Study: Analysis of a compromised machine learning dataset where attackers manipulated training data to influence model predictions.

Module 2: Data Poisoning Attack Techniques and Detection

• Clean-label and dirty-label poisoning attacks 
• Targeted versus untargeted poisoning strategies 
• Dataset contamination methods and attacker objectives 
• Data validation and integrity monitoring techniques 
• Machine learning approaches for poisoning detection 
• Case Study: Investigation of a healthcare AI model affected by manipulated patient training records.

Module 3: Adversarial Machine Learning and Model Manipulation

• Understanding adversarial examples and model exploitation 
• Model evasion and manipulation techniques 
• Gradient-based attacks and AI model weaknesses 
• Defensive machine learning strategies 
• Robust model training methodologies 
• Case Study: Security assessment of an image recognition AI system exposed to adversarial input attacks.

Module 4: AI Model Security Engineering

• Secure AI architecture design principles 
• Model encryption and access control mechanisms 
• Protecting AI intellectual property and model assets 
• Preventing model theft and extraction attacks 
• Secure deployment practices for production AI systems 
• Case Study: Protection of a financial institution’s fraud detection model from unauthorized extraction attempts.

Module 5: Secure Data Management and AI Supply Chain Security

• Data governance frameworks for AI security 
• Securing third-party datasets and AI components 
• AI software supply chain risks 
• Dataset provenance tracking and verification 
• Building trusted AI development environments 
• Case Study: Evaluation of a supply chain attack involving a compromised open-source AI dataset.

Module 6: AI Security Testing, Red Teaming, and Validation

• AI penetration testing methodologies 
• Red teaming AI systems for vulnerabilities 
• Automated security testing tools for ML models 
• Vulnerability assessment of AI pipelines 
• Developing AI security testing strategies 
• Case Study: Red team exercise identifying weaknesses in an enterprise chatbot powered by machine learning.

Module 7: AI Monitoring, Governance, and Incident Response

• Continuous AI model monitoring techniques 
• Detecting abnormal model behavior 
• AI security incident response planning 
• Model rollback and recovery strategies 
• Regulatory and ethical AI security requirements 
• Case Study: Response framework for an AI recommendation system affected by malicious training data.

Module 8: Advanced AI Model Protection and Future Security Trends

• Zero Trust principles for AI environments 
• Privacy-enhancing machine learning technologies 
• Federated learning security challenges 
• Generative AI and LLM security considerations 
• Future trends in AI cyber defense 
• Case Study: Designing a secure enterprise AI platform resistant to emerging adversarial attacks.

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.