Using AI Reasoning to Prevent AI Scams скачать в хорошем качестве

Using AI Reasoning to Prevent AI Scams

Alan Lefort (CEO, @StrongestLayer) discusses how LLM-powered reasoning is transforming phishing security from reactive pattern-matching to predictive threat detection, and why traditional rule-based systems can no longer defend against sophisticated AI-generated phishing attacks. SHOW: 965 SHOW TRANSCRIPT: The Cloudcast #965 Transcript (https://docs.google.com/document/d/18...) SHOW VIDEO:    / @thecloudcastnet    CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotw NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS" (https://cloudcastbasics.net/) SPONSORS: • [Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”. • [TestKube] TestKube is Kubernetes-native testing platform, orchestrating all your test tools, environments, and pipelines into scalable workflows empowering Continuous Testing. Check it out at TestKube.io/cloudcast (https://testkube.io/cloudcast) SHOW NOTES: • Website (https://www.strongestlayer.com/) • StrongestLayer Research (https://www.darkreading.com/cyber-ris...) Topic 1 - Welcome to the show Alan. Tell us about your background and your involvement in Cybersecuity. Topic 2 - Let's start with the core challenge. You've said that "if only AI can defend against weaponized AI" - what specific gap in traditional email security did you identify that led to this philosophy? How are AI-powered phishing attacks fundamentally different from what we've seen before? Topic 3 - How does this attack vector demonstrate the limitations of rule-based security systems, and why can't traditional pattern matching keep up? Topic 4 - Let's break down your TRACE (Threat Reasoning and AI Correlation Engine) architecture. You've described it as "LLM-as-master" rather than "LLM-as-add-on." What does this fundamental architectural difference mean for threat detection, and how does it help? Topic 5 - You discuss "pre-campaign detection," which involves identifying potential phishing campaigns weeks before emails are sent. This sounds like moving from reactive to predictive security. How does your system correlate technical intelligence with business context to achieve this early warning capability? Topic 6 - From an implementation standpoint, how do organizations integrate LLM-powered reasoning into their existing security stacks? What's the deployment model, and how do you handle the challenge of reasoning about business context without exposing sensitive organizational data? FEEDBACK? • Email: show at the cloudcast dot net • Bluesky: @cloudcastpod.bsky.social (https://bsky.app/profile/cloudcastpod...) • Twitter/X: @cloudcastpod (  / cloudcastpod  ) • Instagram: @cloudcastpod (  / cloudcastpod  ) • TikTok: (  / cloudcastpod  ) FEEDBACK? • Email: show @ reasoning dot show • Bluesky: @reasoningshow.bsky.social (https://bsky.app/profile/cloudcastpod...) • Twitter/X: @ReasoningShow (https://x.com/ReasoningShow) • Instagram: @ (  / cloudcastpod  ) reasoningshow • TikTok: @reasoningshow (  / reasoningshow  )