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.
Secure AI Code & Libraries with Static Analysis
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您将学到什么
Configure Bandit, Semgrep, PyLint to detect AI vulnerabilities: insecure model deserialization, hardcoded secrets, unsafe system calls in ML code.
Apply static analysis to fix AI vulnerabilities (pickle exploits, input validation, dependencies); create custom rules for AI security patterns.
Implement pip-audit, Safety, Snyk for dependency scanning; assess AI libraries for vulnerabilities, license compliance, and supply chain security.
您将获得的技能
- Dependency Analysis
- Vulnerability Scanning
- Vulnerability Assessments
- Supply Chain
- AI Personalization
- Analysis
- Open Source Technology
- Secure Coding
- AI Security
- Application Security
- MLOps (Machine Learning Operations)
- Threat Modeling
- Continuous Integration
- PyTorch (Machine Learning Library)
- Program Implementation
- DevSecOps
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
该课程共有3个模块
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.
涵盖的内容
4个视频2篇阅读材料1次同伴评审
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.
涵盖的内容
3个视频1篇阅读材料1次同伴评审
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.
涵盖的内容
4个视频1篇阅读材料1个作业2次同伴评审
提供方
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