Where is the Balance Between AI Innovation and Security?

02/23/24 • 31 min

There is a complex interplay between innovation and security in the age of GenAI. As the digital landscape evolves at an unprecedented pace, Daniel, Caleb and Ashish share their insights on the challenges and opportunities that come with integrating AI into cybersecurity strategies

Caleb challenges the current trajectory of safety mechanisms in technology and how overregulation may inhibit innovation and the advancement of AI's capabilities. Daniel Miessler, on the other hand, emphasizes the necessity of accepting technological inevitabilities and adapting to live in a world shaped by AI. Together, they explore the potential overreach in AI safety measures and discuss how companies can navigate the fine line between fostering innovation and ensuring security.

Questions asked:

(00:00) Introduction

(03:19) Maintaining Balance of Innovation and Security

(06:21) Uncensored LLM Models

(09:32) Key Considerations for Internal LLM Models

(12:23) Balance between Security and Innovation with GenAI

(16:03) Enterprise risk with GenAI

(25:53) How to address enterprise risk with GenAI?

(28:12) Threat Modelling LLM Models

Questions asked:

(00:00) Introduction

(03:19) Maintaining Balance of Innovation and Security

(06:21) Uncensored LLM Models

(09:32) Key Considerations for Internal LLM Models

(12:23) Balance between Security and Innovation with GenAI

(16:03) Enterprise risk with GenAI

(25:53) How to address enterprise risk with GenAI?

(28:12) Threat Modelling LLM Models

Previous Episode

Breaking Down AI's Impact on Cybersecurity

What does AI mean for Cybersecurity in 2024? Caleb and Ashish sat down with Daniel Miessler. This episode is a must listen for CISOs and cybersecurity practitioners exploring AI's potential and pitfalls. From the intricacies of Large Language Models (LLM) and API security to the nuances of data protection, Ashish, Caleb and Daniel unpack the most pressing threats and opportunities facing the cybersecurity landscape in 2024.

Questions asked:

(00:00) Introduction

(06:06) A bit about Daniel Miessler

(06:23) Current State of Artificial General Intelligence

(13:57) What going to change in security with AI?

(16:40) AI’s role in spear phishing

(19:10) AI’s role in Recon

(21:08) Where to start with AI Security?

(26:48) AI focused cybersecurity startups

(31:12) Security Challenges with self hosted LLMs

(39:34) Are the models becoming too restrictive

Resources spoken about during the episode:

Unsupervised Learning

Next Episode

AI's role in Security Operation Automation

What is the current reality for AI automation in Cybersecurity? Caleb and Ashish spoke to Edward Wu, founder and CEO of Dropzone AI about the current capabilities and limitations of AI technologies, particularly large language models (LLMs), in the cybersecurity domain. From the challenges of achieving true automation to the nuanced process of training AI systems for cyber defense, Edward, Caleb and Ashish shared their insights into the complexities of implementing AI and the importance of precision in AI prompt engineering, the critical role of reference data in AI performance, and how cybersecurity professionals can leverage AI to amplify their defense capabilities without expanding their teams.

Questions asked:

(00:00) Introduction

(05:22) A bit about Edward Wu

(08:31) What is a LLM?

(11:36) Why have we not seen entreprise ready automation in cybersecurity?

(14:37) Distilling the AI noise in the vendor landscape

(18:02) Solving challenges with using AI in enterprise internally

(21:35) How to deal with GenAI Hallucinations?

(27:03) Protecting customer data from a RAG perspective

(29:12) Protecting your own data from being used to train models

(34:47) What skillset is required in team to build own cybersecurity LLMs?

(38:50) Learn how to prompt engineer effectively