Challenging Misconceptions in Model Training for Security Tasks

Guests: 

• Umesh Shankar, Distinguished Engineer, Chief Technologist for Google Cloud Security

• Scott Coull, Head of Data Science Research, Google Cloud Security

Topics:

• 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