Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

This hands-on workshop is designed for security professionals interested in learning how to integrate advanced AI capabilities into their detection and response workflows. Attendees will receive practical, step-by-step instruction on leveraging the power of Claude Code, a sophisticated AI agent, to significantly enhance security operations within the LimaCharlie platform for detection engineering use cases.

Users are connecting AI tools without understanding the security implications. In this week's Intel Chat, Chris Luft and Matt Bromiley discuss a security breach at Vercel that originated from a compromised third-party AI tool used by one of its employees. The attacker gained control of the employee's Google Workspace account, which provided access to Vercel's internal environment.

This video walks through how single agents and multi-agent systems are built and run inside the LimaCharlie platform. Agents in LimaCharlie are defined declaratively. Each agent specifies the model it runs, its instructions, the tools it can access, what events trigger it, and the guardrails it operates under. This approach makes agents version controllable, reviewable, and portable across tenants.

This week on the podcast, Marc and Corey sit down with Paul Harris, CEO of BGLA and Futurity Corp at WatchGuard's Impact Partner Conference in Tulum, to explore the evolving cybersecurity landscape across Latin America. Paul shares his journey from early days in cybersecurity to leading organizations in the region, while breaking down the biggest concerns facing LATAM SMBs today. The conversation also covers how AI is reshaping cybersecurity, the challenges of securing partners across diverse markets, and practical advice for business leaders looking to stay ahead of cyber risk in LATAM.

GitGuardian Senior Cybersecurity Researcher Gaetan Ferry’s latest research shows that AI-generated passwords are leaving fingerprints in the wild. In this interview, he explains how he used Markov chains, a century-old statistical model, to detect patterns in passwords generated by modern LLMs, attribute them to model families, and identify 28,000 likely LLM-generated passwords across public GitHub. The findings are a warning for teams adopting AI coding agents.