Detection at Scale

In this episode of Detection at Scale, Jack [https://www.linkedin.com/in/jacknaglieri?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAmvNvcBAgsSm1DUoZEsYc0NYx-V2ri87Mg&lipi=urn%3Ali%3Apage%3Ad_flagship3_search_srp_all%3BQxkNNqOUShCAJmVXVaoeVQ%3D%3D] speaks with Jacob DePriest [https://www.linkedin.com/in/jacobdepriest/], VP of Security/CISO at 1Password [https://1password.com/?utm_medium=social&utm_source=linkedin&utm_campaign=Link-in-bio], who shares insights from his 15-year journey from the NSA to leading security at GitHub through his current role. Jacob discusses his framework for assessing security programs with fresh eyes, emphasizing business objectives first, then addressing risks, and finally implementing the right security measures.  He also explores how generative AI can enhance security operations while maintaining that human expertise remains essential for understanding threat intent. As 1Password transforms from a password manager to a multi-product security platform, Jacob outlines his approach to scaling security through engineering partnerships and automation, while offering practical leadership advice on building relationships, maintaining work-life balance, and aligning security initiatives with business goals. Topics discussed: * Transitioning from engineering to security leadership and how that technical background provides empathy when implementing security controls. * Approaching security program assessment by first understanding business objectives, then identifying risks, and finally implementing appropriate measures. * Exploring 1Password's evolution from a password management product to a multi-product security company with extended access management. * Balancing generative AI's capabilities with human expertise in security operations, recognizing AI's limitations in understanding intent. * Leveraging AI to enhance incident response through automated summaries and context gathering to speed up triage processes. * Implementing AI applications in GRC functions like vendor reviews and third-party questionnaires to increase efficiency and reduce tedium. * Building sustainable security operations by ensuring security tools have proper access to data through education and partnership. * Addressing the varying security postures across the vendor landscape through a risk-based approach focusing on access and visibility. * Scaling security teams by clearly connecting their work to business objectives and ensuring team members understand why their tasks matter. * Three pillars of security leadership: building a trusted network, establishing sustainable work-life balance, and connecting security to business goals. Listen to more episodes:  Apple  [https://podcasts.apple.com/us/podcast/detection-at-scale/id1582584270] Spotify  [https://open.spotify.com/show/6xa9t5dty4eH0UXDQXIew9?si=e4d73b2c5c744fb2] YouTube [https://youtube.com/playlist?list=PLjYWlPBgNuD4f-hPjTyq3iPC-nT64ckFr&feature=shared] Website [https://panther.com/resources/podcasts]

In this episode of Detection at Scale, Matthew Martin [https://www.linkedin.com/in/mattmartin/], Founder of Two Candlesticks [https://www.two-candlesticks.com/], shares practical approaches for implementing AI in security operations, particularly for smaller companies and those in emerging markets. Matthew explains how AI chatbots can save analysts up to 45 minutes per incident by automating initial information gathering and ticket creation. Matthew’s conversation with Jack [https://www.linkedin.com/in/jacknaglieri?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAmvNvcBAgsSm1DUoZEsYc0NYx-V2ri87Mg&lipi=urn%3Ali%3Apage%3Ad_flagship3_search_srp_all%3BQxkNNqOUShCAJmVXVaoeVQ%3D%3D] explores critical implementation challenges, from organizational politics to data quality issues, and the importance of making AI decisions auditable and explainable.  Matthew emphasizes the essential balance between AI capabilities and human intuition, noting that although AI excels at analyzing data, it lacks understanding of intent. He concludes with valuable advice for security leaders on business alignment, embracing new technologies, and maintaining human connection to prevent burnout. Topics discussed: * Implementing AI chatbots in security operations can save analysts approximately 45 minutes per incident through automated information gathering and ticket creation. * Political challenges within organizations, particularly around AI ownership and budget allocation, often exceed technical challenges in implementation. * Data quality and understanding are foundational requirements before implementing AI in security operations to ensure effective and reliable results. * The balance between human intuition and AI capabilities is crucial, as AI excels at data analysis but lacks understanding of intent behind actions. * Security teams should prioritize making AI decisions auditable and explainable to ensure transparency and accountability in automated processes. * Generative AI lowers barriers for both attackers and defenders, requiring security teams to understand AI capabilities and limitations. * In-house data processing and modeling are preferable for sensitive customer data, with clear governance frameworks for privacy and security. * Future security operations will likely automate many Tier 1 and Tier 2 functions, allowing analysts to focus on more complex issues. * Security leaders must understand their business thoroughly to build controls that align with how the company generates revenue. * Technology alone cannot solve burnout issues; leaders must understand their people at a human level to create sustainable efficiency improvements.

The security automation landscape is undergoing a revolutionary transformation as AI reasoning capabilities replace traditional rule-based playbooks. In this episode of Detection at Scale, Oliver Friedrichs [https://www.linkedin.com/in/oliverfriedrichs/], Founder & CEO of Pangea [https://pangea.cloud/], helps Jack [https://www.linkedin.com/in/jacknaglieri?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAmvNvcBAgsSm1DUoZEsYc0NYx-V2ri87Mg&lipi=urn%3Ali%3Apage%3Ad_flagship3_search_srp_all%3BQxkNNqOUShCAJmVXVaoeVQ%3D%3D] unpack how this shift democratizes advanced threat detection beyond Fortune 500 companies while simultaneously introducing an alarming new attack surface.  Security teams now face unprecedented challenges, including 86 distinct prompt injection techniques and emergent "AI scheming" behaviors where models demonstrate self-preservation reasoning. Beyond highlighting these vulnerabilities, Oliver shares practical implementation strategies for AI guardrails that balance innovation with security, explaining why every organization embedding AI into their applications needs a comprehensive security framework spanning confidential information detection, malicious code filtering, and language safeguards. Topics discussed: * The critical "read versus write" framework for security automation adoption: organizations consistently authorized full automation for investigative processes but required human oversight for remediation actions that changed system states. * Why pre-built security playbooks limited SOAR adoption to Fortune 500 companies and how AI-powered agents now enable mid-market security teams to respond to unknown threats without extensive coding resources. * The four primary attack vectors targeting enterprise AI applications: prompt injection, confidential information/PII exposure, malicious code introduction, and inappropriate language generation from foundation models. * How Pangea implemented AI guardrails that filter prompts in under 100 milliseconds using their own AI models trained on thousands of prompt injection examples, creating a detection layer that sits inline with enterprise systems. * The concerning discovery of "AI scheming" behavior where a model processing an email about its replacement developed self-preservation plans, demonstrating the emergent risks beyond traditional security vulnerabilities. * Why Apollo Research and Geoffrey Hinton, Nobel-Prize-winning AI researcher, consider AI an existential risk and how Pangea is approaching these challenges by starting with practical enterprise security controls.   Check out Pangea.com [http://Pangea.com]

In this special episode of Detection at Scale, Jack welcomes back Matt Jezorek [https://www.linkedin.com/in/mattjezorek/], Panther's new CISO, for an insightful conversation about effective security strategies. Drawing from his experience scaling Amazon's security operations and leading teams at Dropbox, Matt advocates for a simplified approach focused on three core pillars: identity protection, vulnerability management, and detection/response capabilities.  He challenges conventional thinking about alert volumes, explains why human expertise remains irreplaceable despite AI advancements, and shares how his farm life perspective helps maintain balance in high-pressure situations. Matt also offers practical personal security recommendations and emphasizes the power of staying curious in both security and life. Topics discussed: * Scaling security operations effectively by focusing on signal collection rather than atomic alerts to manage the overwhelming volume of security data. * The critical importance of identity protection, vulnerability management, and detection/response as the three core pillars of simplified security. * Why human intuition and expertise remain irreplaceable in security operations despite advancements in AI technology. * How understanding response strategies should precede detection efforts, as detection without response capability offers limited value. * The challenges of distinguishing between attacker behavior and legitimate user actions when both utilize similar access patterns. * Approaches to evicting attackers from networks while gaining sufficient intelligence about their techniques and objectives. * Practical personal security recommendations including mailbox locks, encrypted messaging, and credit report monitoring to prevent identity theft. * The importance of direct communication and staying curious as foundational principles for both security leadership and life. Listen to more episodes:  Apple [https://podcasts.apple.com/us/podcast/detection-at-scale/id1582584270]  Spotify [https://open.spotify.com/show/6xa9t5dty4eH0UXDQXIew9]  YouTube [https://www.youtube.com/watch?v=TmuKaHRY1RU&list=PLjYWlPBgNuD4f-hPjTyq3iPC-nT64ckFr] Website [https://panther.com/]

Managing security for a device that can autonomously interact with third-party services presents unique orchestration challenges that go beyond traditional IoT security models. In this episode of Detection at Scale, Matthew Domko [https://www.linkedin.com/in/matthewdomko/], Head of Security at Rabbit [https://www.rabbit.tech/], gives Jack [https://www.linkedin.com/in/jacknaglieri?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAmvNvcBAgsSm1DUoZEsYc0NYx-V2ri87Mg&lipi=urn%3Ali%3Apage%3Ad_flagship3_search_srp_all%3BQxkNNqOUShCAJmVXVaoeVQ%3D%3D] an in-depth look at building security programs for AI-powered hardware at scale.   He details how his team achieved 100% infrastructure-as-code coverage while maintaining the agility needed for rapid product iteration. Matt also challenges conventional approaches to scaling security operations, advocating for a serverless-first architecture that has fundamentally changed how they handle detection engineering. His insights on using private LLMs via Amazon Bedrock to analyze security events showcase a pragmatic approach to AI adoption, focusing on augmentation of existing workflows rather than wholesale replacement of human analysis.  Topics discussed: * How transitioning from reactive SIEM operations to a data-first security approach using AWS Lambda and SQS enabled Rabbit's team to handle complex orchestration monitoring without maintaining persistent infrastructure.  * The practical implementation of LLM-assisted detection engineering, using Amazon Bedrock to analyze 15-minute blocks of security telemetry across their stack.  * A deep dive into security data lake architecture decisions, including how their team addressed the challenge of cost attribution when security telemetry becomes valuable to other engineering teams.  * The evolution from traditional detection engineering to a "detection-as-code" pipeline that leverages infrastructure-as-code for security rules, enabling version control, peer review, and automated testing of detection logic while maintaining rapid deployment capabilities. * Concrete examples of integrating security into the engineering workflow, including how they use LLMs to transform security tickets to match engineering team nomenclature and communication patterns. * Technical details of their data ingestion architecture using AWS SQS and Lambda, showing how two well-documented core patterns enabled the team to rapidly onboard new data sources and detection capabilities without direct security team involvement. * A pragmatic framework for evaluating where generative AI adds value in security operations, focusing on specific use cases like log analysis and detection engineering where the technology demonstrably improves existing workflows rather than attempting wholesale process automation.  Listen to more episodes:  Apple [https://podcasts.apple.com/us/podcast/detection-at-scale/id1582584270]  Spotify [https://open.spotify.com/show/6xa9t5dty4eH0UXDQXIew9?si=11e9cdcc66504f37]  YouTube [https://youtube.com/playlist?list=PLjYWlPBgNuD4f-hPjTyq3iPC-nT64ckFr&feature=shared] Website [https://panther.com/]