What is static analysis for AI security in this course?

In this course, static analysis means examining AI code and library dependencies without running them so you can catch security issues early. The emphasis is on building a repeatable security workflow for machine learning projects instead of relying on one-off checks after development is finished.

When would you use static analysis for AI security?

You would use it while writing or updating AI code, adding third-party libraries, or preparing changes for review so problems are found before release. It is especially useful in ML projects where model loading, data handling, and large dependency sets can hide security risks.

How does static analysis for AI security fit into a broader workflow?

It fits into the build-and-test phase as an early security check that supports coding, review, and dependency management. In this course, it becomes part of a connected workflow that starts with local scans and extends into automated checks in CI/CD and supply chain tracking.

How is static analysis for AI security different from runtime testing?

Static analysis inspects code structure and patterns without executing the program, while runtime testing looks at behavior after the code runs. The course focuses on static analysis because it can reveal risky coding patterns and missing safeguards before those issues ever appear in testing or production.

Do you need any prerequisites before learning static analysis for AI security?

A basic understanding of programming concepts and some familiarity with Python are helpful for this course. You do not need deep AI expertise, but it helps to be comfortable reading code and following how security checks fit into development work.

What tools, platforms, or methods are used in this course?

The course uses static code scanning tools such as Bandit and Semgrep, along with dependency scanning tools such as pip-audit. It also shows how those checks connect to CI/CD workflows and supply chain practices like SBOM generation.

What specific tasks will you practice or complete in this course?

You will practice scanning AI code and dependencies, interpreting and fixing common vulnerabilities, writing custom rules for AI-specific patterns, and adding automated checks to a CI/CD pipeline. You will also generate dependency records such as SBOMs so security review becomes more repeatable.

Secure AI Code & Libraries with Static Analysis

本课程是 AI Security: Security in the Age of Artificial Intelligence 专项课程的一部分

位教师：Aseem Singhal

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

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

您将学习的工具

PyTorch (Machine Learning Library)

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本课程是 AI Security: Security in the Age of Artificial Intelligence 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有3个模块

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.

单元详情

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

4个视频总计28分钟

Welcome to Secure AI Code and Libraries with Static Analysis4分钟
Why Secure AI Development Matters9分钟
What is Static Analysis9分钟
Setting Up Static Analysis Tooling7分钟

2篇阅读材料总计10分钟

Welcome to the Course: Course Overview5分钟
The State of AI Security: Why Static Analysis is Critical5分钟

1次同伴评审总计20分钟

Hands-On-Learning: AI Startup Security Audit Crisis20分钟

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.