Artificial Intelligence Security Examination (AISX)

The Artificial Intelligence Security Examination (AISX) is a comprehensive, industry-aligned certification designed to evaluate real-world skills in securing artificial intelligence systems, machine learning pipelines, and large language model (LLM) deployments. As AI adoption accelerates across industries, so do the risks associated with data poisoning, model theft, prompt injection, adversarial attacks, and regulatory non-compliance. AISX is built to address these modern challenges with depth, realism, and technical rigor.

Unlike traditional multiple-choice exams that rely on theoretical memorization, AISX is a 6-hour, scenario-based assessment consisting of 600 carefully engineered MCQs. Each question simulates real-world AI security situations encountered by professionals working in cybersecurity, AI engineering, cloud security, and risk governance roles.

Why AISX Matters in Today’s AI-Driven World

Artificial intelligence systems are no longer experimental—they power financial decisions, healthcare diagnostics, autonomous systems, surveillance platforms, and enterprise automation. With this growth comes an expanded attack surface that traditional cybersecurity certifications do not fully address.

AISX bridges this gap by focusing exclusively on AI-specific threat models, including:

• Machine learning data poisoning

• Adversarial input manipulation

• Model extraction and inversion

• LLM prompt injection and jailbreaks

• AI supply chain risks

• Ethical, legal, and compliance failures

The examination validates not just knowledge, but decision-making under pressure, mimicking how AI security incidents unfold in real operational environments.

Exam Format & Structure

⏱ Duration: 6 Hours
🧠 Format: Scenario-Based MCQs (600 Questions)
🔒 Mode: Individual, Proctored

AISX is intentionally long and demanding to measure cognitive endurance, analytical thinking, and consistency—critical traits for AI security professionals. The exam follows a progressive difficulty curve, moving from foundational understanding to advanced attack-defense reasoning.

Each scenario may include:

• AI system architectures

• ML pipeline workflows

• Threat actor behavior

• Security logs or incident narratives

• Governance and compliance dilemmas

Candidates must select the single best answer, reflecting real-world best practices rather than theoretical perfection.

Who Should Take AISX?

AISX is designed for professionals and advanced learners who work with or secure AI systems, including:

• AI & Machine Learning Engineers

• Cybersecurity Professionals

• Cloud & DevSecOps Engineers

• AI Red Team & Blue Team Members

• Security Architects

• Risk, Governance & Compliance Professionals

• Researchers in AI safety and adversarial ML

This certification is not beginner-focused. It is intended for those who want to demonstrate industry-ready competence in AI security.

What Makes AISX Industry-Standard?

• Scenario-first design aligned with real incidents

• Vendor-neutral AI security principles

• Coverage of both offensive and defensive AI security

• Strong focus on LLMs and modern AI systems

• Emphasis on ethical, legal, and regulatory responsibilities

• Proctored, individual-based assessment ensuring credibility

AISX is built to complement—not replace—traditional cybersecurity certifications by adding a dedicated AI security specialization.

✅ AISX SYLLABUS – 30 MODULES

1. Fundamentals of Artificial Intelligence Systems

2. Machine Learning Lifecycle & Security Risks

3. AI Threat Modeling Methodologies

4. Adversarial Machine Learning Concepts

5. Data Collection & Dataset Integrity

6. Training Data Poisoning Attacks

7. Feature Manipulation & Label Flipping

8. Model Architecture Security

9. Model Extraction & Stealing Techniques

10. Model Inversion & Privacy Attacks

11. Adversarial Input & Evasion Attacks

12. AI API Security & Abuse Prevention

13. Large Language Model (LLM) Architecture

14. Prompt Injection Attacks

15. Jailbreaking & Prompt Leakage

16. LLM Data Privacy & Context Exposure

17. AI Supply Chain & Third-Party Risks

18. Cloud-Based AI Deployment Security

19. Secure MLOps & CI/CD Pipelines

20. Monitoring & Detection of AI Attacks

21. AI Incident Response & Forensics

22. AI Red Teaming Strategies

23. AI Blue Team Defense Techniques

24. Bias, Fairness & Ethical AI Security

25. Regulatory & Compliance Frameworks for AI

26. AI Governance & Risk Management

27. Secure AI System Design Principles

28. AI Safety vs AI Security

29. Case Studies of Real-World AI Breaches

30. Future Trends in AI Security Threats