Secure AI Code & Libraries with Static Analysis

Overview

Master comprehensive static analysis workflows for AI security using industry-standard tools like Bandit, Semgrep, and pip-audit. Learn to identify AI-specific vulnerabilities including insecure pickle deserialization, hardcoded secrets in training scripts, and dependency risks that traditional security tools miss. Through hands-on labs with real vulnerable ML codebases, you'll configure automated security scanning in CI/CD pipelines, create custom detection rules for TensorFlow/PyTorch patterns, and implement supply chain security with SBOM generation. Address the unique challenges of ML projects with 50+ dependencies while establishing production-ready security policies. This course is ideal for anyone involved in AI development, automation, or system design, including software developers, data professionals, tech managers, and curious learners who want to understand modern multi-agent systems and how to govern them responsibly. Learners don’t need deep AI expertise to get started. A basic understanding of programming concepts and some familiarity with tools like Python or visual workflow builders will make the experience smoother, but the course guides you step by step from core ideas to more advanced design patterns. By course completion, you'll proactively secure AI systems against the growing threat landscape targeting machine learning workflows, preventing costly post-deployment fixes through early vulnerability detection in development processes.

Syllabus

Introduction to Secure AI Development and Static Analysis

This module establishes the foundation for secure AI development by teaching learners why traditional security approaches fall short for machine learning systems and how static analysis tools provide proactive vulnerability detection. Students will master the essential skills of configuring and integrating industry-standard security tools like Bandit, Semgrep, and PyLint into their AI development workflows, while understanding the unique threat landscape that AI/ML systems face in production environments.

Identifying AI-Specific Code Vulnerabilities with Static Analysis

This module focuses on practical application of static analysis techniques to detect real security weaknesses commonly found in AI codebases. Students will learn to identify and remediate critical vulnerabilities including insecure model deserialization, hardcoded credentials in training scripts, and unsafe data pipeline operations, while developing custom detection rules tailored to AI-specific security patterns that generic tools often miss.

Securing Third-Party AI Libraries and License Compliance

This module extends security analysis beyond first-party code to address the complex supply chain risks inherent in AI development's heavy reliance on external libraries. Students will master automated dependency scanning workflows using tools like pip-audit and Snyk to identify vulnerabilities in AI libraries, ensure license compliance across diverse open-source packages, and implement comprehensive supply chain security policies with Software Bill of Materials (SBOM) generation for production ML systems.