Certified AI Penetration Tester – Purple Team (CAIPT-PT)

Certified AI Penetration Tester – Purple Team (CAIPT-PT) Certification Course by Tonex

The Certified AI Penetration Tester – Purple Team (CAIPT-PT) certification is designed for cybersecurity professionals and AI practitioners who aim to secure AI systems by combining offensive and defensive strategies. This certification equips candidates with the skills to simulate attacks (Red Team) on AI models and build robust defenses (Blue Team) against vulnerabilities. CAIPT-PT bridges the gap between AI development and cybersecurity, ensuring the security and integrity of AI-driven systems in high-stakes environments.

Learning Objectives:

• Understand the security risks and vulnerabilities specific to AI systems and machine learning models.
• Conduct penetration testing on AI models, datasets, and pipelines.
• Apply defensive strategies to protect AI systems against adversarial attacks.
• Develop secure coding practices for AI algorithms and frameworks.
• Utilize tools and techniques to evaluate and improve AI system robustness.
• Create actionable reports to improve the security posture of AI systems.
• Collaborate effectively in Purple Team operations to ensure AI system safety.

Target Audience:

• Cybersecurity professionals (Red Teamers, Blue Teamers, and Purple Teamers).
• AI/ML practitioners looking to secure AI systems.
• Penetration testers and ethical hackers.
• Data scientists interested in adversarial machine learning.
• Security engineers and AI developers.
• IT managers and compliance officers responsible for AI governance.

Course Modules:

Module 1: Introduction to AI Security

• Basics of AI and ML.
• AI applications and attack surface.
• Threat models in AI systems.

Module 2: Offensive AI Security (Red Team)

• Adversarial machine learning: evasion, poisoning, and extraction.
• Attacks on datasets and data pipelines.
• Vulnerabilities in AI models and APIs.
• AI fuzzing techniques.

Module 3: Defensive AI Security (Blue Team)

• Hardening AI systems and models.
• Detecting and mitigating adversarial attacks.
• Secure AI coding practices and frameworks.
• Logging and monitoring AI security.

Module 4: Purple Team Dynamics

• Red Team and Blue Team collaboration in AI contexts.
• Simulating AI attacks and building defenses.
• Case studies and collaborative exercises.

Module 5: AI Penetration Testing Lifecycle

• Scoping AI penetration tests.
• Identifying vulnerabilities in AI pipelines.
• Testing tools and frameworks for AI security.

Module 6: AI Governance, Compliance, and Ethics

• Legal and ethical considerations in AI security.
• AI regulatory frameworks and standards.
• Reporting and compliance strategies.

Module 7: Hands-on Labs and Simulations

• Performing adversarial attacks.
• Building and testing defenses.
• Tools for Purple Team operations.

Exam Domains:

1. AI Fundamentals (10%)

• Basics of AI/ML systems and their architecture.
• Common AI applications and attack surfaces.

2. Adversarial Attacks and Techniques (25%)

• Evasion, poisoning, model extraction, and inference attacks.
• Tools for adversarial machine learning.

3. Defensive Measures (25%)

• AI model hardening techniques.
• Monitoring, logging, and securing AI systems.

4. Purple Team Collaboration (20%)

• Red Team and Blue Team collaboration.
• Scenarios for detecting and mitigating threats.

5. AI Security Frameworks and Compliance (10%)

• Governance and ethics in AI security.
• Compliance requirements for AI systems.

6. AI Penetration Testing Methodology (10%)

• Lifecycle of AI penetration testing.
• Tools and best practices for testing.

Exam Question Types:

1. Multiple Choice Questions (MCQs)

• Scenario-based and theoretical questions to test conceptual understanding.

2. Fill-in-the-Blank

• Identify missing components in AI security workflows or configurations.

3. Drag-and-Drop

• Match vulnerabilities to mitigation strategies or threat models to attack types.

4. Simulations and Practical Tasks

• Virtual labs requiring candidates to identify and exploit vulnerabilities in AI systems.

5. Case Studies

• Analyze real-world AI security incidents and propose solutions.

6. Short Answer Questions

• Describe methods for securing AI pipelines or addressing specific threats.

This program ensures that participants emerge with a balanced skillset to assess, exploit, and defend AI systems in real-world environments, making them valuable assets in the cybersecurity and AI sectors.