ML Security & Compliance Workshop¶

This advanced-level workshop covers security practices, governance frameworks, audit trails, and compliance requirements for production ML systems.

Introduction¶

As machine learning systems move from experimentation to production, security and compliance become critical concerns. ML models introduce unique attack vectors—model poisoning, adversarial examples, data leakage—that traditional security practices don't fully address. At the same time, regulations like GDPR require organizations to provide explanations for automated decisions, maintain audit trails, and support data subject access requests.

In this workshop, you'll learn how to:

• Implement security controls for ML artifacts and pipelines
• Establish model governance frameworks with access control
• Build immutable audit trails for ML decision-making
• Generate compliance reports and model cards

Overview of the Exercises¶

Hands-On Sessions¶

Start with the security basics, then proceed through the exercises in order:

• Exercise 1 - Security Basics
• Exercise 2 - Governance Implementation
• Exercise 3 - Audit Logging
• Exercise 4 - Compliance Reporting

Directory Structure¶

ml_security_compliance/
├── notebooks/
│   ├── 01_security_basics.ipynb
│   ├── 02_governance_implementation.ipynb
│   ├── 03_audit_logging.ipynb
│   └── 04_compliance_reporting.ipynb
├── scripts/
│   └── audit_logger.py
├── requirements.txt
└── README.md

Prerequisites¶

• Python 3.9+
• Basic understanding of MLflow and experiment tracking
• Familiarity with MLOps concepts from the beginner/intermediate workshops
• Access to an MLflow tracking server (for Exercise 2)

Technologies Used¶