AI Security Foundations
Learn the fundamentals of AI security and how to protect AI systems from emerging threats
Lessons (7)
1. Why AI Security Matters Now More Than Ever
Organizations are deploying AI faster than they're securing it. This innovation-security gap makes AI security one of the most urgent disciplines in technology today.
2. The New AI Attack Surface
AI systems introduce entirely new attack vectors that exploit the AI's logic, context, and autonomy—not just vulnerabilities in code.
3. The AI Security Lifecycle
Effective AI security isn't a single product or one-time fix. It's a continuous, disciplined process woven into the entire lifecycle of an AI system.
4. The Four Pillars of Modern AI Security
The four pillars—Policy, Red Teaming, Runtime Guardrails, and Monitoring—form a continuous cycle that ensures security is not an afterthought but a core component of how AI is developed, deployed, and managed.
5. Securing AI Agents and Multimodal Systems
AI agents and multimodal systems introduce new dimensions of risk that require specialized understanding. The four pillars still apply, but the threats and defenses must evolve.
6. The Practitioners: Roles and Responsibilities
Securing AI is fundamentally a team sport. It requires close collaboration across multiple roles, from executive leadership to engineers implementing controls.
7. Your Journey into AI Security
You now have a foundation in AI security. Here's how to build on it and continue your learning.