Detection at Scale

In this episode of Detection at Scale, Jack speaks with Erik Bloch, VP of Security, Illumio, about why most security operations teams aren't ready for AI tools and what fundamental processes must be in place first. Erik challenges the industry's obsession with new technologies, sharing stories from his experience transforming underperforming security teams at major companies like Cisco, Salesforce, and Atlassian.  His conversation with Jack explores how to measure what actually matters in security operations, from team capacity utilization to business outcome dispositions, and why proper ticketing systems and actionable metrics are prerequisites for any advanced tooling to be effective. Topics discussed: The importance of establishing fundamental processes like ticketing systems and metrics before implementing AI tools in security operations. How to measure team capacity utilization and resource allocation to identify when security operations teams are operating beyond sustainable levels. Why traditional security metrics like mean time to detect are often vanity metrics that don't provide actionable business intelligence. The critical need for security leaders to communicate in business language with concrete data rather than anecdotal risk assessments. How managed service providers will likely be the first to successfully adopt AI tools due to their standardized processes. The challenge of proving AI tool effectiveness when most organizations lack baseline metrics to measure improvement against established benchmarks. Why security teams gravitate toward building custom tools and how this impacts their approach to adopting commercial AI solutions. The role of MCP in enabling security teams to create their own agents and integrate multiple tools. How AI should focus on eliminating routine tasks like phishing email analysis rather than trying to catch advanced persistent threats. The framework for implementing AI tools by starting with business outcomes, defining metrics, identifying capabilities, and then inserting automation.  Listen to more episodes: Apple  Spotify  YouTube Website

Drawing from his experience building enterprise SOCs and teaching thousands of security professionals, John Hubbard, Cyber Defense Curriculum Lead at SANS Institute and host of the Blueprint podcast, tells Jack about how AI is revolutionizing security operations centers, including balancing AI automation with fundamental analyst skills. They also explore practical AI applications in alert contextualization, team performance analysis, and the future vision of natural language interfaces for complex security tasks.  John emphasizes the importance of teaching both traditional methods and AI-enhanced approaches, ensuring security teams can leverage technology while maintaining critical thinking capabilities. He also discusses considerations around local versus cloud-based AI models and offers actionable advice for security professionals looking to future-proof their careers in an increasingly automated landscape.   Topics discussed: How AI transforms alert contextualization by dynamically incorporating business context and asset information for better triage decisions. The educational challenge of teaching both foundational security methods and AI-enhanced approaches to maintain analyst skills. Practical applications of AI in SOC operations, including automated phishing triage and mass analysis of analyst performance data. The evolution toward natural language interfaces that could enable complex security tasks like packet analysis through conversational commands. Custom agent development versus relying on vendor-provided AI solutions, including the technical challenges and coding requirements involved. Future SOC architecture predictions featuring interconnected agents, MCP protocols, and the abstraction of traditional security analyst tasks. Local versus cloud-based AI model considerations, including data privacy concerns, computational requirements, and trust implications. The critical question of oversight in automated security operations and who monitors AI agents in increasingly autonomous systems. Performance analysis capabilities enabled by AI's ability to process written text and logs at scale for team improvement insights. Practical advice for security professionals to embrace discomfort, invite AI into problem-solving, and establish mentoring relationships for career growth. Listen to more episodes:  Apple  Spotify  YouTube Website

Elliot Colquhoun, VP of Information Security + IT at Airwallex, has developed a cutting-edge AI-driven security program, protecting 1,800 employees with just 9 engineers. He discusses the revolutionary approach of using AI to contextualize security alerts, mimicking top engineer decision-making. Elliot shares his journey from Palantir to fintech, emphasizing a focus on hiring engineers with entrepreneurial skills rather than traditional backgrounds. He also explores navigating global regulatory compliance while maintaining security integrity, highlighting the future of adaptive security solutions.

In this episode of Detection at Scale, Jack speaks with Jacob DePriest, VP of Security/CISO at 1Password, 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  Spotify  YouTube Website

In this episode of Detection at Scale, Matthew Martin, Founder of Two Candlesticks, 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 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, Founder & CEO of Pangea, helps Jack 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  

Matt Jezorek, CISO at Panther and a former security leader at Amazon and Dropbox, shares insights on simplifying security operations. He emphasizes focusing on identity protection, vulnerability management, and detection/response. Matt argues that human intuition remains vital, even as AI advances. He discusses navigating the complexities of security data and the importance of strategic response. Additionally, he reflects on how his farm life perspective aids in handling high-pressure situations and the importance of staying curious in both security and life.

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, Head of Security at Rabbit, gives Jack 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  Spotify  YouTube Website

Mor Levi, VP of Detection, Analysis, & Response at Salesforce, shares her expertise on integrating AI in security operations. She reveals how Agent Force achieved 90% automation in triage while maintaining effectiveness. Topics include securing AI implementations, the evolving roles of security analysts, and the importance of data quality. Mor discusses the balance between AI efficiency and human creativity, emphasizing the need for strategic thinking in an increasingly automated landscape. Real-world examples provide insights into both the challenges and successes of AI in enterprise security.

In this episode of Detection at Scale, Jack speaks to Brandon Kovitz, Senior Manager of Detection & Response at Outreach, shares his insights on the evolving landscape of cybersecurity. He discusses the critical role of generative AI in enhancing detection and response capabilities, emphasizing the importance of understanding data to maximize security tools' effectiveness.    Brandon also highlights the balance between human intuition and AI, noting that while AI can analyze vast amounts of data, it lacks the nuanced understanding of intent that only humans can provide. Tune in to learn how organizations can leverage AI while maintaining essential human oversight in their security strategies!    Topics discussed: The importance of operationalizing detection and response capabilities to enhance security posture in a cloud-native, SaaS-first environment.   Leveraging generative AI to improve data analysis and streamline detection processes, ultimately enabling faster responses to emerging cyber threats.   The critical balance between AI capabilities and human intuition, emphasizing that human expertise is essential for understanding intent behind actions in cybersecurity.   Understanding the data landscape is vital for maximizing the effectiveness of security tools and ensuring a strong return on investment.   The role of automation in reducing the noise from tier one and tier two security alerts, allowing teams to focus on complex issues.   Insights on building a detection-as-code pipeline to facilitate rapid implementation of security measures in response to emerging vulnerabilities.   The significance of collaboration between security teams and privacy experts to ensure compliance and protect customer data in AI initiatives.   The future of cybersecurity operations, including the potential for AI to automate many routine tasks and enhance overall operational efficiency.   The necessity for ongoing education and adaptation in the cybersecurity field to keep pace with technological advancements and evolving threats.     Resources Mentioned:  Brandon Kovitz on LinkedIn Outreach website