How to Build Trustworthy AI — Allie Howe
AI Summary
In this video, Ally How from Growth Cyber discusses the critical importance of building trustworthy AI systems. She explains what trustworthy AI means, exploring the concepts of AI security and AI safety — how the outside world can harm AI applications and how AI applications can harm the world, respectively. The video covers real-world incidents highlighting trust issues in AI, such as data leaks, harmful chatbot behaviors, and vulnerabilities to prompt injections and jailbreaks. Ally outlines the new security paradigm called ML SecOps (Machine Learning Security Operations), emphasizing the need for comprehensive AI lifecycle protection including build, test (AI red teaming), and runtime security. Runtime security is highlighted as especially crucial for detecting and mitigating prompt injections, off-topic or unsafe content, and ensuring AI safety during deployment. She also discusses regulatory compliance, potential business risks, and the competitive advantage of demonstrating trustworthy AI. Examples include protecting AI models from serialization attacks, continuously testing AI models for safety and security, and applying runtime guardrails to prevent unsafe outputs. The video encourages developers and companies to prioritize trustworthy AI to unlock revolutionary innovations confidently and responsibly.