AI CyberSecurity Podcast Kaizenteq Team

How can AI change a Security Analyst's workflow? Ashish and Caleb caught up with Ely Kahn, VP of Product at SentinelOne, to discuss the revolutionary impact of generative AI on cybersecurity. Ely spoke about the challenges and solutions in integrating AI into cybersecurity operations, highlighting how can simplify complex processes and empowering junior to mid-tier analysts.



Questions asked:

(00:00) Introduction

(03:27) A bit about Ely Kahn

(04:29) Current State of AI in Cybersecurity

(06:45) How AI could impact Cybersecurity User Workflow?

(08:37) What are some of the concerns with such a model?

(14:22) How does it compare to a analyst not using this model?

(21:41) Whats stopping models for going into autopilot?

(30:14) The reasoning for using multiple LLMs

(34:24) ChatGPT vs Anthropic vs Mistral



You can discover more about SentinelOne's Purple AI here!

How is AI transforming traditional approaches to offensive security, pentesting, security posture management, security assessment, and even code security? Caleb and Ashish spoke to Rob Ragan, Principal Technology Strategist at Bishop Fox about how AI is being implemented in the world of offensive security and what the right way is to threat model an LLM.



Questions asked:

(00:00) Introductions

(02:12) A bit about Rob Ragan

(03:33) AI in Security Assessment and Pentesting

(09:15) How is AI impacting pentesting?

(14:50 )Where to start with AI implementation in offensive Security?

(18:19) AI and Static Code Analysis

(21:57) Key components of LLM pentesting

(24:37) Testing whats inside a functional model?

(29:37) Whats the right way to threat model an LLM?

(33:52) Current State of Security Frameworks for LLMs

(43:04) Is AI changing how Red Teamers operate?

(44:46) A bit about Claude 3

(52:23) Where can you connect with Rob



Resources spoken about in this episode:

https://www.pentestmuse.ai/

https://github.com/AbstractEngine/pentest-muse-cli

https://docs.garak.ai/garak/

https://github.com/Azure/PyRIT

https://bishopfox.github.io/llm-testing-findings/

https://www.microsoft.com/en-us/research/project/autogen/

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

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

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

AI Security using LLM, AI Agents & more can be used to innovate cyber security practices. In this episode Ashish and Caleb sit down to chat about the nuances of creating custom AI agents, the implications of prompt engineering, and the innovative uses of AI in detecting and preventing security threats. From discussing the complexity of Data Loss Prevention (DLP) in today's world to debating the realistic timeline for the advent of Artificial General Intelligence (AGI).



Questions asked:

(00:26) The impact of GenAI on Workforce

(04:11) Understanding Artificial General Intelligence

(05:57) Using Custom Agents in OpenAI

(09:37) Exploring Custom AI Agents: Definition and Uses

(12:08) Security Concerns with Custom AI Agents

(14:32) AI's Role in Data Protection

(18:41) AI’s Role in API Security

(20:56) Complexity of Data Protection with AI

(25:42) Protecting Against Prompt Injections in AI Systems

(27:53) Prompt Engineering and Penetration Testing

(31:16) Risks of Prompt Engineering in AI Security

(37:03) What's Hot in AI Security and Innovation?