Secure AI Code & Libraries with Static Analysis

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

• Configure Bandit, Semgrep, and PyLint to detect AI vulnerabilities like insecure deserialization and hardcoded secrets
• Apply static analysis to fix AI vulnerabilities and create custom detection rules for AI security patterns
• Implement dependency scanning with pip-audit, Safety, and Snyk to assess vulnerabilities and compliance

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work