AI Security & Safety
About This Learning Path
As AI systems become increasingly integrated into critical infrastructure and decision-making processes, ensuring their security and safety is paramount. This learning path provides a comprehensive exploration of AI security challenges, vulnerabilities, and mitigation strategies.
You'll learn how to identify and defend against adversarial attacks, implement secure development practices, and navigate the complex ethical and governance considerations of AI deployment. By the end of this path, you'll have the knowledge and skills to build more secure and trustworthy AI systems.
What You'll Learn
- Identify and mitigate common AI security vulnerabilities
- Implement adversarial robustness techniques for machine learning models
- Design secure AI systems with appropriate safeguards
- Conduct effective security testing for AI applications
- Apply ethical principles and governance frameworks to AI development
- Develop strategies for responsible AI deployment
Prerequisites
- Basic understanding of machine learning concepts
- Familiarity with cybersecurity fundamentals
- Experience with Python programming (recommended but not required)
Who This Path Is For
AI Developers
Who want to build more secure and robust AI systems
Security Professionals
Looking to expand their expertise to AI security
Technology Leaders
Responsible for AI governance and risk management
Your Progress
Ready to Begin?
Start your journey into AI security and learn how to build safer, more secure AI systems.
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