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  

In this special episode of Detection at Scale, Jack welcomes back Matt Jezorek, 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  Spotify  YouTube Website

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

JJ Tang, CEO and Co-founder of Rootly and former Instacart innovator, shares his insights on transforming incident management. He discusses why it's crucial to view incident management as a cultural shift rather than just a tooling problem. Tang emphasizes breaking down silos between security and other teams to improve communication. He highlights the role of security practitioners as educators, the importance of data analysis in preventing incidents, and strategies to foster a culture of reliability across organizations.

Thijn Bukkems, Threat Hunting Lead at Grammarly, shares his expertise in building robust security intelligence programs. He emphasizes working backwards from response strategies to create effective threat detection mechanisms. Collaboration across teams is crucial to avoid silos and uncover valuable insights. Thijn discusses maximizing existing resources, enhancing security efficiency through adaptable tools, and the importance of internal threat modeling. He highlights the need to prioritize tasks and balance analytical research with practical solutions in the ever-evolving landscape of cybersecurity.

Saksham Tushar, the Head of Security Operations & Threat Detection Engineering at CRED, dives into the intricacies of compliance in a fast-paced tech environment. He discusses how CRED streamlines complex compliance requirements and leverages automation to enhance threat detection. Saksham highlights the importance of verifying automated outcomes and using Python libraries for swift incident investigations. Additionally, he emphasizes the need for contextual understanding of security incidents and the integration of threat intelligence to create a robust security operations framework.

Dan Cao is the Engineering Manager of Security Incident and Response at Netflix, and Josh Liburdi is a Staff Security Engineer at Brex. They dive into the shift toward developer-centric security operations and the challenge of balancing big platforms with bespoke tools. The importance of critical thinking and foundational skills in cybersecurity is emphasized. They share strategies for building resilient security teams through effective mentorship and culture, highlighting the need for adaptability in our ever-evolving tech landscape.