Host Caleb Sima and Ashish Rajan caught up with experts Daniel Miessler (Unsupervised Learning), Joseph Thacker (Principal AI Engineer, AppOmni) to talk about the true vulnerabilities of AI applications, how prompt injection is evolving, new attack vectors through images, audio, and video and predictions for AI-powered hacking and its implications for enterprise security. Whether you're a red teamer, a blue teamer, or simply curious about AI's impact on cybersecurity, this episode is packed with expert insights, practical advice, and future forecasts. Don’t miss out on understanding how attackers leverage AI to exploit vulnerabilities—and how defenders can stay ahead. Questions asked: (00:00) Introduction (02:11) A bit about Daniel Miessler (02:22) A bit about Rez0 (03:02) Intersection of Red Team and AI (07:06) Is red teaming AI different? (09:42) Humans or AI: Better at Prompt Injection? (13:32) What is a security vulnerability for a LLM? (14:55) Jailbreaking vs Prompt Injecting LLMs (24:17) Whats new for Red Teaming with AI? (25:58) Prompt injection in Multimodal Models (27:50) How Vulnerable are AI Models? (29:07) Is Prompt Injection the only real threat? (31:01) Predictions on how prompt injection will be stored or used (32:45) What’s changed in the Bug Bounty Toolkit? (35:35) How would internal red teams change? (36:53) What can enterprises do to protect themselves? (41:43) Where to start in this space? (47:53) What are our guests most excited about in AI? Resources Daniel's Webpage - Unsupervised Learning Joseph's Website

In this jam-packed episode, with our panel we explored the current state and future of AI in the cybersecurity landscape. Hosts Caleb Sima and Ashish Rajan were joined by industry leaders Jason Clinton (CISO, Anthropic), Kristy Hornland (Cybersecurity Director, KPMG) and Vijay Bolina (CISO, Google DeepMind) to dive into the critical questions surrounding AI security. We’re at an inflection point where AI isn’t just augmenting cybersecurity—it’s fundamentally changing the game. From large language models to the use of AI in automating code writing and SOC operations, this episode examines the most significant challenges and opportunities in AI-driven cybersecurity. The experts discuss everything from the risks of AI writing insecure code to the future of multimodal models communicating with each other, raising important questions about trust, safety, and risk management. For anyone building a cybersecurity program in 2024 and beyond, you will find this conversation valuable as our panelist offer key insights into setting up resilient AI strategies, managing third-party risks, and navigating the complexities of deploying AI securely. Whether you're looking to stay ahead of AI's integration into everyday enterprise operations or explore advanced models, this episode provides the expert guidance you need Questions asked: (00:00) Introduction (02:28) A bit about Kristy Hornland (02:50) A bit about Jason Clinton (03:08) A bit about Vijay Bolina (04:04) What are frontier/foundational models? (06:13) Open vs Closed Model (08:02) Securing Multimodal models and inputs (12:03) Business use cases for AI use (13:34) Blindspots with AI Security (27:19) What is RPA? (27:47) AI’s talking to other AI’s (32:31) Third Party Risk with AI (38:42) Enterprise view of risk with AI (40:30) CISOs want Visibility of AI Usage (45:58) Third Party Risk Management for AI (52:58) Starting point for AI in cybersecurity program (01:02:00) What the panelists have found amazing about AI

In this episode of the AI Cybersecurity Podcast, Caleb and Ashish sat down with Vijay Bolina, Chief Information Security Officer at Google DeepMind, to explore the evolving world of AI security. Vijay shared his unique perspective on the intersection of machine learning and cybersecurity, explaining how organizations like Google DeepMind are building robust, secure AI systems. We dive into critical topics such as AI native security, the privacy risks posed by foundation models, and the complex challenges of protecting sensitive user data in the era of generative AI. Vijay also sheds light on the importance of embedding trust and safety measures directly into AI models, and how enterprises can safeguard their AI systems. Questions asked: (00:00) Introduction (01:39) A bit about Vijay (03:32) DeepMind and Gemini (04:38) Training data for models (06:27) Who can build an AI Foundation Model? (08:14) What is AI Native Security? (12:09) Does the response time change for AI Security? (17:03) What should enterprise security teams be thinking about? (20:54) Shared fate with Cloud Service Providers for AI (25:53) Final Thoughts and Predictions

What were the key AI Cybersecurity trends at ⁠BlackHat USA⁠? In this episode of the AI Cybersecurity Podcast, hosts ⁠Ashish Rajan⁠ and ⁠Caleb Sima⁠ dive into the key insights from Black Hat 2024. From the AI Summit to the CISO Summit, they explore the most critical themes shaping the cybersecurity landscape, including deepfakes, AI in cybersecurity tools, and automation. The episode also features discussions on the rising concerns among CISOs regarding AI platforms and what these mean for security leaders. Questions asked: (00:00) Introduction (02:49) Black Hat, DEF CON and RSA Conference (07:18) Black Hat CISO Summit and CISO Concerns (11:14) Use Cases for AI in Cybersecurity (21:16) Are people tired of AI? (21:40) AI is mostly a side feature (25:06) LLM Firewalls and Access Management (28:16) The data security challenge in AI (29:28) The trend with Deepfakes (35:28) The trend of pentest automation (38:48) The role of an AI Security Engineer

In this episode of the AI Cybersecurity Podcast, we dive deep into the latest findings from Google's DeepMind report on the misuse of generative AI. Hosts Ashish and Caleb explore over 200 real-world cases of AI misuse across critical sectors like healthcare, education, and public services. They discuss how AI tools are being used to create deepfakes, fake content, and more, often with minimal technical expertise. They analyze these threats from a CISO's perspective but also include an intriguing comparison between human analysis and AI-generated insights using tools like ChatGPT and Anthropic's Claude. From the rise of AI-powered impersonation to the manipulation of public opinion, this episode uncovers the real dangers posed by generative AI in today’s world. Questions asked: (00:00) Introduction (03:39) Generative Multimodal Artificial Intelligence (09:16) Introduction to the report (17:07) Enterprise Compromise of GenAI systems (20:23) Gen AI Systems Compromise (27:11) Human vs Machine Resources spoken about during the episode: Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data

How much can we really trust AI-generated code more over Human generated Code today? How does AI-Generated code compare to Human generated code in 2024? Caleb and Ashish spoke to Guy Podjarny, Founder and CEO at Tessl about the evolving world of AI generated code, the current state and future trajectory of AI in software development. They discuss the reliability of AI-generated code compared to human-generated code, the potential security risks, and the necessary precautions organizations must take to safeguard their systems. Guy has also recently launched his own podcast with Simon Maple called The AI Native Dev, which you can check out if you are interested in hearing more about the AI Native development space. Questions asked: (00:00) Introduction (02:36) What is AI Generated Code? (03:45) Should we trust AI Generated Code? (14:34) The current usage of AI in Code Generated (18:27) Securing AI Generated Code (23:44) Reality of Security AI Generated Code Today (30:22) The evolution of Security Testing (37:36) Where to start with AI Security today? (50:18) Evolution of the broader cybersecurity industry with AI (54:03) The Positives of AI for Cybersecurity (01:00:48) The startup Landscape around AI (01:03:16) The future of AppSec (01:05:53) The future of security with AI

Which AI Security Framework is right for you? As AI is gaining momentum, we are starting to see quite a few frameworks appearing but the question is, which one should you start with and can AI help you decide! Caleb and Ashish tackle this challenge head-on, comparing three major AI security frameworks: Databricks, NIST, and OWASP Top 10. They break down the key components of each framework, discuss practical implementation strategies, and provide actionable insights for CISOs and security leaders. They may have had some help along the way. Questions asked: (00:00) Introduction (02:54) Databricks AI Security Framework (DASF) (06: 38) Top 3 things from DASF by Claude 3 (07:32) Top 3 things from DASF by ChatGPT (08:46) DASF Use Case Scenario (11:01) Thoughts on DASF (13:18) OWASP Top 10 for LLM Models (20:12) Google's Secure AI Framework (SAIF) (21:31) NIST AI Risk Management Framework (25:18) Claude 3 summarises NIST RMF for 5 year old (28:00) ChatGPT compares NIST RMF and NIST CSF (28:48) How do the frameworks compare? (36:46) Summary of all the frameworks Resources from this episode: Databricks AI Security Framework (DASF) OWASP Top 10 for LLM NIST AI Risk Management Framework Google Secure AI Framework

What is the current state and future potential of AI Security? This special episode was recorded LIVE at BSidesSF (thats why its a little noisy), as we were amongst all the exciting action. Clint Gibler, Caleb Sima and Ashish Rajan sat down to talk about practical uses of AI today, how AI will transform security operations, if AI can be trusted to manage permissions and the importance of understanding AI's limitations and strengths. Questions asked: (00:00) Introduction (02:24) A bit about Clint Gibler (03:10) What top of mind with AI Security? (04:13) tldr of Clint’s BSide SF Talk (08:33) AI Summarisation of Technical Content (09:47) Clint’s favourite part of the talk - Fuzzing (15:30) Questions Clint got about his talk (17:11) Human oversight and AI (25:04) Perfection getting in the way of good (30:15) AI on the engineering side (36:31) Predictions for AI Security Resources from this coversation: Caleb's Keynote at BSides SF Clint's Newsletter

Key AI Security takeaways from RSA Conference 2024, BSides SF 2024 and all the fringe activities that happen in SF during that week. Caleb and Ashish were speakers, panelists, participating in several events during that week and this episode captures all the highlights from all the conversations they had and they trends they saw during what they dubbed the "Cybersecurity Fringe Festival” in SF. Questions asked: (00:00) Introduction (02:53) Caleb’s Keynote at BSides SF (05:14) Clint Gibler’s Bsides SF Talk (06:28) What are BSides Conferences? (13:55) Cybersecurity Fringe Festival (17:47) RSAC 2024 was busy (19:05) AI Security at RSAC 2024 (23:03) RSAC Innovation Sandbox (27:41) CSA AI Summit (28:43) Interesting AI Talks at RSAC (30:35) AI conversations at RSAC (32:32) AI Native Security (33:02) Data Leakage in AI Security (30:35) Is AI Security all that different? (39:26) How to filter vendors selling AI Solutions?

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?

How to efficiently secure, scale and deploy LLMs in an Enterprise? Kicking off 2024 with the final instalment of our AI Cybersecurity Primer. In this episode Caleb and Ashish talk about large language models (LLMs), their deployment in enterprise settings, and the nuances of their operation. They explore the challenges and opportunities in ensuring the security of these systems, emphasising the importance of cybersecurity measures in the evolving landscape of AI. Questions asked: (00:00) Introduction (02:23) Deployment of LLM System (07:13) Deployment in an Enterprise (12:01) Threats with LLMs (15:30) Protecting Data (18:17) LLMs and Compliance (19:51) LLM Control Plane (26:36) Whats hot in AI! (36:57) Vendor risk assessment If you found this episode valuable, you can catch Part-1 & Part 2 of the AI Primer Series here - If you have any questions about AI & it's security please drop that as a comment or reach out to us on info@kaizenteq.com #aicybersecurity #largelanguagemodels #ai

You cant protect what you don't understand. We are continuing Part 2 of our AI Primer on the AI Cybersecurity Podcast to understand what role AI will play in the world of cybersecurity. In this episde, Caleb and Ashish are levelling up the playing field, talking all things LLMs (Large Language Models), GenAI and laying the foundations with AI primers for cybersecurity in the season 1 of AI CyberSecurity Podcast. Questions asked: (00:00) Introduction (02:34) Evolution of LLM and GenAI (09:20) How does LLM work? (17:15) Differentiating between LLMs (22:05) The cost of running LLMs (23:43) Deploying an LLM (26:10 Big Companies vs Startups (32:21) Whats hot in AI this week! If you found this episode valuable, listen to Part-1 of the AI Primer Series ! If you have any questions about AI & it's security please drop that as a comment or reach out to us on info@kaizenteq.com

To understand what role AI will play in the world of cybersecurity, it important to understand the technology behind it. Caleb and Ashish are levelling up the playing field and laying the foundations with AI primers for cybersecurity in the season 1 of AI CyberSecurity Podcast. What was discussed: (00:00) Introduction (02:36) Learning about AI/ML (08:00) Acronyms of AI (10:49) AGI - Artificial General Intelligence (11:29) Three states of AGI (13:48) AI/ML in Security Products (17:03) Different kinds of learning (21:51) Whats hot in the AI Section!!

Ashish Rajan and Caleb Sima, who have been Cybersecurity practitioners and CISOs for over a decade, are combining forces to bring to you how CyberSecurity can be applied to AI without FUD. Each episode discuss a AI Theme and What's Hot in AI. You can expect the episodes on your favorite Podcast Player every two weeks. This is a Audio & Video podcast so you can find video of each episode on AI CyberSecurity Podcast YouTube Channel If you have any AI & CyberSecurity queries or topics you would like us to cover, please reach out to us on info@kaizenteq.com You can also check out our sister podcast - Cloud Security Podcast for all your cloud and cloud native security topics.