Certified Artificial Intelligence Security Professional (CAISP) |Industry-aligned certification

The Certified Artificial Intelligence Security Professional (CAISP) is an advanced, industry-aligned certification designed to validate real-world expertise in securing AI, machine learning, and large language model (LLM) systems across their entire lifecycle.

As organizations rapidly adopt AI for decision-making, automation, and customer interaction, new attack surfaces have emerged—ranging from data poisoning and adversarial machine learning to prompt injection, model theft, and AI supply-chain compromise. CAISP is built to address these challenges head-on.

This certification focuses on practical, scenario-based security decision-making, not theoretical AI concepts or vendor-specific tooling. Candidates are tested through a 6-hour, 600-question professional exam that simulates real incidents, architectural reviews, threat modeling exercises, red-team findings, and governance challenges faced by AI security professionals.

CAISP is vendor-neutral, role-agnostic, and globally relevant, making it suitable for security engineers, cloud architects, SOC teams, red teams, risk professionals, and leaders responsible for deploying or defending AI systems in production environments.

By earning CAISP, professionals demonstrate their ability to identify, assess, mitigate, and respond to AI-specific security threats in modern enterprise, cloud, and AI-native infrastructures.

CAISP Syllabus – 30 Modules

Domain 1: AI & ML Security Foundations

1. AI System Architecture & Attack Surface Overview

2. Machine Learning Lifecycle Security Risks

3. Threat Modeling for AI Systems

4. Security Responsibilities in AI Teams

Domain 2: Data Security & Integrity

5. Training Data Collection & Trust Boundaries

6. Data Poisoning Attacks & Detection

7. Label Manipulation & Dataset Tampering

8. Secure Data Pipelines for ML

Domain 3: Adversarial Machine Learning

9. Adversarial Examples & Evasion Attacks

10. Model Manipulation & Weight Attacks

11. Robustness Testing & Defensive Strategies

12. Red Teaming ML Models

Domain 4: Model Privacy & Confidentiality

13. Model Inversion Attacks

14. Membership Inference Attacks

15. Model Extraction & Theft

16. Protecting Intellectual Property in AI

Domain 5: LLM & Generative AI Security

17. Prompt Injection & Indirect Prompt Attacks

18. Jailbreaks, Alignment Bypass & Abuse

19. RAG (Retrieval-Augmented Generation) Security Risks

20. Securing AI Assistants & Chatbots

Domain 6: AI Deployment & MLOps Security

21. Securing Model Deployment & APIs

22. CI/CD & MLOps Pipeline Threats

23. Cloud & Container Security for AI Workloads

24. Monitoring, Logging & Drift Detection

Domain 7: Governance, Risk & Compliance

25. AI Risk Assessment & Control Frameworks

26. Secure AI Design Principles & Policies

27. Regulatory, Ethical & Compliance Considerations

Domain 8: Incident Response & Defense

28. AI Security Incident Detection & Response

29. Forensics & Post-Incident Analysis for AI Systems

30. Continuous Improvement & AI Security Maturity