Masters of Data

On this episode of Masters of Data, we take you inside the Dojo AI demo we're bringing to the show floor at Black Hat. We walk through the SOC Analyst Agent, Mobot, and MCP back to back. SOC Analyst Agent triages every tier one alert down to the one that actually matters, and Mobot picks up from there, running the investigation in plain English to track down other phishing victims and lateral movement. We also show how MCP pulls that same insight into Claude or Slack. SOC leads tired of alert fatigue and analyst burnout will want the numbers here: 100% of tier one alerts triaged automatically, and 25 hours a week back per person.

0:00:00 – Introduction and Black Hat demo preview

On this episode of Masters of Data, we unpack what it actually means to expect AI to be the primary interface for everything we do. We dig into the pull toward centralizing work in a single hub like Claude versus staying spread across specialized tools like Slack, Asana and Zoom, and where the line sits between helpful automation and letting an agent speak on your behalf. We also get into the "chief of staff" agent workflow for daily roundups and why specialized, best-of-breed tools aren't going anywhere, even as hubs get smarter. If you're weighing how much of your workflow to hand over to AI versus keeping it in tools built for the job, this one's for you.

0:00:00 – Introduction

In this episode of Masters of Data, We sat down with Jeremy Powell, CISO at Sumo Logic, to unpack how AI is reshaping security operations from the SOC floor to the boardroom. Jeremy walks us through Sumo Logic's agentic security program, where AI agents now handle first-line triage, red-team the company's own defenses, and cut incident response time by 60%. We also get into what it means to lead a security team when execution is no longer the bottleneck and empathy becomes a core leadership skill. This one is for security leaders trying to move AI agents out of pilot mode and into real production workflows.

0:00:00 – Introduction and welcoming Jeremy Powell

On this episode of Masters of Data, we revisit the predictions Adam White, Zoe Hawkins, and David Girvin made at the end of last year, checking our own scorecard halfway through 2026. The hits: agents running amok and deleting databases, MCP becoming the backbone for tracking what agents actually do, growing security gaps around personal data, and a collective rejection of low-quality AI content. The misses: we underestimated how fast companies would cut staff for AI, then quietly start rehiring once the agents couldn't cover the work, and we're still arguing about whether token burn is a cost problem or a coming attack vector. Security and marketing leaders weighing their own AI rollout will find plenty to steal here, especially the case for building governance before the connect button gets clicked.

0:00:00 – Revisiting last year's AI predictions

SOC analyst burnout gets talked about constantly, but the data angle doesn't get enough credit. In this episode, Adam White, Zoe Hawkins, and David Girvin dig into how messy, incomplete, or untrustworthy data leaves security teams grinding away on the wrong things, chasing CVEs that will never be exploited while the real threats walk right through the front door. They cover vulnerability management theater, why phishing keeps winning, and how the volume demands of modern SIEMs are pushing teams toward agentic automation, whether they're ready or not. Security practitioners who've ever felt like they're working hard but still losing will recognize the frustration this episode puts into words.

0:00:00 – Introduction

0:00:44 – Why burnout and alert fatigue are losing their meaning

0:03:18 – Working toward the wrong solution

0:05:10 – The CVE treadmill and vulnerability management theater

0:09:47 – Thinking like a business: threat modeling with real stakes

0:11:55 – Phishing as the persistent, evolving top threat

0:15:26 – SIEM data coverage and the cost of big data

0:18:04 – Agentic automation and the speed problem

0:19:10 – The productivity-security trade-off

0:22:37 – Wrap-up

We've all been there: you buy the SIEM, check the box, and wonder six months later why nothing's actually working. In this episode, Adam White, Zoe Hawkins, and David Girvin dig into what it really takes to get a SIEM running properly, from threat modeling your business objectives first to the iterative tuning work that never quite ends. We also get into the AI hype cycle in security, why probabilistic tools have real limits in regulated environments, and the vendor lock-in math that makes switching harder than it looks. Worth a listen for any security practitioner or team lead who's staring at a half-configured deployment and wondering whether the problem is the tool or the implementation.

0:00:00 – Introduction

On this episode of Masters of Data, we dive into metacognition (thinking about thinking) and why knowing how your own brain works makes you a better AI user. We explore how different prompting styles, from engineering every detail to anticipating every failure mode, reflect how we each think, and why giving AI the right context is the difference between a brilliant result and a confident hallucination. We also get real about the cognitive cost of AI-fueled productivity: doing thirty things shallowly is not the leisure-filled future we were promised, and burnout is catching up fast. Plus, we make a strong case for using AI as your personal blind spot detector, because sometimes you need a machine to tell you what you are missing.

In this episode of Masters of Data, we tackle one of tech's buzziest debates: vibe coding versus production-ready software. We break down where AI-assisted "just make it work" coding genuinely shines (think POCs, prototypes, and getting stakeholder buy-in fast) and where it falls dangerously short when someone tries to ship it to ten thousand enterprise users. We also dig into David's agentic engineering workflow, security risks like malicious MCP servers and supply chain attacks, and why turning a vibe-coded prototype into real software still takes months, not days. Bottom line: vibes are great, but they are not a deployment strategy.