AI Security Testing Training Course

AI Security Testing Training Course

Introduction

AI Security Testing Training Course is designed to equip cybersecurity professionals, AI engineers, security analysts, and technology leaders with advanced skills to identify, assess, and mitigate security vulnerabilities in Artificial Intelligence (AI) systems, Machine Learning (ML) models, Generative AI applications, and Large Language Models (LLMs). As organizations rapidly adopt AI-driven solutions, the need for AI assurance, adversarial testing, model security, AI risk management, penetration testing, vulnerability assessment, and responsible AI governance has become critical. This course explores modern AI security testing frameworks, threat modeling techniques, automated security validation, adversarial simulations, and emerging AI attack surfaces.

Participants will gain practical expertise in performing AI penetration testing, red teaming, prompt injection testing, data poisoning analysis, model robustness evaluation, API security testing, privacy risk assessment, and AI compliance validation. Through real-world case studies, hands-on labs, and industry-based scenarios, learners will understand how to secure AI ecosystems against evolving cyber threats while implementing Zero Trust AI security principles, secure AI development lifecycle (Secure MLOps), continuous AI monitoring, and proactive threat intelligence strategies.

Course Duration

5 days

Course Objectives

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

1. Understand AI security testing fundamentals, frameworks, and emerging cybersecurity challenges. 
2. Perform AI vulnerability assessments and penetration testing across AI systems. 
3. Identify and exploit AI-specific attack vectors and threat scenarios. 
4. Apply adversarial machine learning testing techniques to evaluate model resilience. 
5. Conduct LLM security testing and Generative AI vulnerability analysis. 
6. Implement AI threat modeling and risk assessment methodologies. 
7. Test AI applications against prompt injection, jailbreak, and data leakage attacks. 
8. Evaluate machine learning model robustness, reliability, and security controls. 
9. Apply AI red teaming methodologies for proactive defense validation. 
10. Secure AI pipelines using MLOps security and DevSecOps practices. 
11. Perform AI API security testing and cloud AI security assessments. 
12. Implement AI governance, compliance, and responsible AI security controls. 
13. Develop advanced capabilities for continuous AI security monitoring and incident response. 

Target Audience

1. Cybersecurity professionals and penetration testers 
2. AI engineers and machine learning developers 
3. Security operations centre (SOC) analysts 
4. Cloud security specialists 
5. DevSecOps and MLOps engineers 
6. Risk, compliance, and governance professionals 
7. Security architects and technology leaders 
8. Ethical hackers and vulnerability researchers 

Course Modules

Module 1: Fundamentals of AI Security Testing

• Introduction to AI security concepts and attack surfaces 
• Understanding AI threat landscapes and security risks 
• AI lifecycle security assessment methodologies 
• AI security testing frameworks and standards 
• Building an AI security testing strategy 
• Case Study: Analyzing security weaknesses in an enterprise AI chatbot deployment and identifying potential attack paths.

Module 2: AI Threat Modeling and Risk Assessment

• Creating AI-specific threat models 
• Identifying adversarial threats against AI systems 
• Applying STRIDE and AI risk assessment techniques 
• Mapping AI threats using MITRE ATLAS framework 
• Developing AI security risk mitigation plans 
• Case Study: Threat modeling an AI-powered financial fraud detection system to identify possible manipulation scenarios.

Module 3: AI Vulnerability Assessment and Penetration Testing

• AI application penetration testing methodologies 
• Discovering vulnerabilities in AI-powered applications 
• Testing AI APIs, interfaces, and integrations 
• Automated AI security scanning techniques 
• Reporting AI security vulnerabilities 
• Case Study: Conducting penetration testing on an AI customer support platform to identify security gaps.

Module 4: Adversarial Machine Learning Security Testing

• Understanding adversarial attacks on ML models 
• Testing model robustness against manipulation 
• Evaluating evasion and extraction attacks 
• Performing adversarial sample testing 
• Improving model resilience strategies 
• Case Study: Testing an image recognition AI model against adversarial input manipulation.

Module 5: Generative AI and LLM Security Testing

• Identifying vulnerabilities in Large Language Models 
• Testing prompt injection vulnerabilities 
• Assessing jailbreak and bypass techniques 
• Evaluating AI hallucination security risks 
• Securing Retrieval-Augmented Generation (RAG) systems 
• Case Study: Security testing an enterprise GenAI assistant to prevent confidential data exposure.

Module 6: AI Red Teaming and Attack Simulation

• AI red team methodologies and frameworks 
• Simulating AI-focused cyber attacks 
• Testing model abuse scenarios 
• Conducting AI security stress testing 
• Developing attack simulation reports 
• Case Study: Performing AI red teaming against a virtual assistant used in healthcare services.

Module 7: AI Data Security and Privacy Testing

• Testing AI training data security 
• Identifying data poisoning risks 
• Evaluating privacy leakage vulnerabilities 
• Securing datasets and data pipelines 
• Implementing AI privacy protection techniques 
• Case Study: Assessing a recommendation engine for unauthorized extraction of sensitive user information.

Module 8: AI Security Operations and Continuous Testing

• Implementing continuous AI security monitoring 
• Integrating AI security testing into DevSecOps pipelines 
• AI incident response and threat detection 
• Security automation for AI environments 
• Building an AI security assurance program 
• Case Study: Creating a continuous security testing framework for a cloud-based AI platform.

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.