Guests:
- Umesh Shankar, Distinguished Engineer, Chief Technologist for Google Cloud Security
- Scott Coull, Head of Data Science Research, Google Cloud Security
Topics covered:
- What does it mean to “teach AI security”? How did we make SecLM? And also: why did we make SecLM?
- What can “security trained LLM” do better vs regular LLM?
- Does making it better at security make it worse at other things that we care about?
- What can a security team do with it today? What are the “starter use cases” for SecLM?
- What has been the feedback so far in terms of impact - both from practitioners but also from team leaders?
- Are we seeing the limits of LLMs for our use cases? Is the “LLM is not magic” finally dawning?
Resources:
- “How to tackle security tasks and workflows with generative AI” (Google Cloud Next 2024 session on SecLM)
- EP136 Next 2023 Special: Building AI-powered Security Tools - How Do We Do It?
- EP144 LLMs: A Double-Edged Sword for Cloud Security? Weighing the Benefits and Risks of Large Language Models
- Supercharging security with generative AI
- Secure, Empower, Advance: How AI Can Reverse the Defender’s Dilemma?
- Considerations for Evaluating Large Language Models for Cybersecurity Tasks
- Introducing Google’s Secure AI Framework
- Deep Learning Security and Privacy Workshop
- Security Architectures for Generative AI Systems
- ACM Workshop on Artificial Intelligence and Security
- Conference on Applied Machine Learning in Information Security
