Machine Learning Security Professional (MLSP) Certification Exam

The Machine Learning Security Professional (MLSP) certification is an industry-standard, scenario-based examination designed to validate advanced expertise in securing machine learning systems across their entire lifecycle.

As machine learning systems increasingly power financial platforms, healthcare diagnostics, autonomous systems, and cloud services, security failures in ML pipelines now represent critical business and national risks. Traditional cybersecurity certifications do not adequately address adversarial ML, data poisoning, model theft, and inference-time attacks. MLSP fills this gap.

The MLSP exam is built for professionals who must defend, assess, design, or audit machine learning systems in real-world environments.

Why MLSP?

Unlike training courses or theory-based tests, MLSP focuses on decision-making under realistic attack scenarios. Candidates are evaluated on their ability to recognize threats, choose appropriate defenses, and understand trade-offs between security, performance, privacy, and scalability.

This certification is vendor-neutral and aligned with modern enterprise ML deployments.

Exam Overview

• Exam Duration: 6 Hours

• Total Questions: 600 Multiple-Choice Questions

• Question Style: Scenario-Based, Real-World Incidents

• Difficulty Level: Advanced to Expert

• Delivery: Online Examination

• Certification Body: SecByte

Who Should Take This Exam?

The MLSP certification is ideal for:

• Machine Learning Engineers working on production systems

• Cybersecurity Professionals securing AI platforms

• MLOps & Platform Engineers

• Red Team & Blue Team Security Engineers

• AI Risk & Compliance Professionals

• Cloud Security Architects

Skills Validated by MLSP

• Identifying ML-specific attack surfaces

• Defending against adversarial and poisoning attacks

• Securing ML pipelines and CI/CD workflows

• Protecting models from theft and inversion

• Implementing privacy-preserving ML techniques

• Responding to ML security incidents

✅ MLSP EXAM SYLLABUS (30 MODULES)

Domain 1: ML Security Foundations

1. Machine Learning Threat Landscape

2. ML Lifecycle & Attack Surfaces

3. Risk Modeling for ML Systems

Domain 2: Data Security & Integrity

4. Training Data Poisoning Attacks

5. Label Manipulation & Data Bias Attacks

6. Backdoor & Trojan Data Insertion

7. Data Provenance & Trust Pipelines

Domain 3: Adversarial Machine Learning

8. Adversarial Example Fundamentals

9. White-Box vs Black-Box Attacks

10. Evasion Attacks in Production

11. Robustness Testing & Stress Evaluation

Domain 4: Model Security

12. Model Extraction & Stealing Attacks

13. Model Inversion Attacks

14. Intellectual Property Protection

15. Model Watermarking & Fingerprinting

Domain 5: Privacy & Abuse

16. Membership Inference Attacks

17. Differential Privacy in ML

18. Federated Learning Security

19. Privacy vs Utility Trade-offs

Domain 6: MLOps & Supply Chain Security

20. ML CI/CD Pipeline Security

21. Dependency & Package Poisoning

22. Model Registry & Artifact Protection

23. Secure Model Deployment Practices

Domain 7: Cloud & API ML Security

24. ML API Abuse & Rate Limiting

25. Authentication & Authorization for ML

26. Multi-Tenant ML Security Risks

Domain 8: Monitoring & Incident Response

27. Drift Detection & Abuse Monitoring

28. ML Security Incident Response

29. Red Teaming Machine Learning Systems

30. Governance, Compliance & AI Risk Management