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A single click can be the start of a breach, but the request that happens right before that click loads a page is even more revealing. We sit down with Ken Carnesi, Chief Executive Officer & Co-Founder, DNSFilter to unpack why DNS security is such a powerful point of control in modern cybersecurity and why so many attacks touch the domain name system somewhere along the way. If you’ve ever heard the saying “it’s always DNS,” you’ll understand it at a deeper level by the end of this conversation. 

We walk through how DNS filtering works in real life, from blocking obvious malicious domains to catching brand-new phishing links before traditional tools have time to catch up. Ken shares how machine learning can categorize and re-categorize domains fast, why “good yesterday” does not mean “safe today,” and what that means for enterprise security teams trying to protect employees, guest Wi‑Fi, and hard-to-manage devices like IoT and specialized equipment. We also get honest about limits: DNS is not the only layer you need, and defense in depth still matters. 

Then we go straight into the AI shift. Ken explains DNSFilter’s Luminate launch, including Agent Shield, built for a world where AI agents generate their own traffic and may soon outnumber humans online. We also talk about the attacker side, why the pace of “Black October” feels different, and a practical takeaway for CISOs: start using the same AI-powered tools against your own environment more frequently so you can react in days and hours, not weeks. 

If you care about DNS security, phishing protection, threat intelligence, and what AI means for the next era of network security, hit subscribe, share this with a teammate, and leave a review with your biggest question about securing AI agents.
Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n
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AI agents can shrink security work from days to minutes, but only if you can trust what you’re running. That’s why we sat down with Justin Buchanan, practice leader for Tenable’s Agentic AI Accelerator, to unpack a problem we don’t talk about enough: security teams are building powerful agentic AI workflows in private silos, even though cybersecurity has always thrived on sharing.

We dig into the Cyber Agents Exchange, an open source repository built for cybersecurity professionals to share AI agents, skill files, MCP servers, and playbooks across the tools you actually use. Justin walks us through concrete examples that hit daily SOC pain, like an agent that answers the brutally practical question “what do I need to fix today?” by combining vulnerability context with sources like MITRE and CISA KEV. We also cover hunt automation that ties together SIEM and EDR data so analysts spend less time reconciling timestamps and more time making decisions.

Then we get serious about AI security risk and governance. Justin explains how contributions are baseline reviewed to catch credential exposure, undocumented outbound calls, and unsafe behavior, plus what it takes to earn a higher-confidence “vetted” status with deeper inspection and sandbox testing. We also talk about Swarm community build events, why more non-traditional builders are shipping real cybersecurity automation, and what CISOs need to scale agentic AI safely inside the enterprise.

If you’re experimenting with AI agents for cybersecurity, threat hunting automation, SOC efficiency, or secure open source adoption, this conversation gives you a practical framework. Subscribe, share this with your team, and leave a review with the AI agent you most want to build next.
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AI didn’t make application security “new” so much as it made it faster. When exploits can be drafted, refined, and scaled with AI assistance, the window to detect and fix vulnerabilities gets brutally short. That urgency is why we sat down with Grant Ongers — OWASP Foundation, a longtime application security leader and OWASP project lead, to talk about what’s changing, what isn’t, and what teams should do right now.

We start with how OWASP works in the real world: open source security projects, global AppSec conferences, and local chapters that help grow the next generation of AppSec talent. From there, we dig into the two big AI shifts Grant sees everywhere: accelerated exploitation and a lowered barrier to entry for attackers. If the “bad guys” can move faster, we need to move faster too, and that means pairing solid fundamentals with smarter, AI-assisted defense.

Threat modeling is the practical backbone of that defense. Grant breaks it down into an approachable four-question framework and explains how AI can help teams explore “what can go wrong” without outsourcing responsibility. We also talk about OWASP Cornucopia, a card-based, gamified approach that helps developers surface security abuse cases and connect them to concrete guidance like OWASP ASVS. Finally, we get blunt about agentic AI: treat agents like employees, scope access tightly, watch for toxic combinations of permissions, and keep a human accountable for anything that ships.

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AI is everywhere right now, but most companies are still stuck in the same place: promising pilots that never turn into real production results. We sit down with Kathryn Harrison, Global Vice President of Strategy and AI Platforms at Concentrix to get practical about what actually scales in enterprise AI, especially when you are responsible for operations and customer experience at global scale. If you are trying to prove AI ROI, reduce cost, grow revenue, or improve customer satisfaction without creating new risk, this conversation is built for you.

We walk through the real “AI gap” between executive expectations and day-to-day delivery, plus a simple maturity model that shows where most organizations sit today: individual copilots, narrow functional pilots, and full workflow reinvention with agentic AI. From there, we dig into orchestration, the make-or-break work of deciding what AI should handle, where humans must stay in the loop, and how to make handoffs seamless so customers do not have to repeat themselves. Catherine also explains how to encourage fast experimentation while keeping security, compliance, and governance solid through role-based access, clear guardrails, and cross-functional oversight.

Then we get into what teams often ignore until the bill arrives: token shock. We talk tokenomics, why bolting AI onto an old process can cost more than the original workflow, and how CFOs and CIOs should treat AI spend as a capital allocation decision. We wrap with what is coming next, including agentic commerce and a glimpse at what a truly agent-driven Black Friday could look like across retail, travel, banking, and healthcare.

If this helped you think more clearly about enterprise AI strategy, agentic workflows, and customer experience automation, subscribe, share with a builder or operator, and leave a review with your biggest takeaway.
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Your AI agent can write SQL in seconds. The hard part is letting it touch real data without turning production into a crime scene.

We sit down with Mike Freedman, co-founder and CTO of Tiger Data (formerly TimescaleDB) and a Princeton computer science professor, to unpack what “real-time data infrastructure” looks like now that AI agents are becoming everyday teammates. We talk about why the Timescale name started to box the company into a narrow “time series database” perception, even though many teams use Postgres as a unified operational database that combines telemetry, events, and business metadata in one place.

From there, we dig into why Postgres is having a second wave with AI: a stable, trusted core plus an extension framework that lets the ecosystem innovate with time series, PostGIS, and vector search. Mike explains why industrial data is growing faster than legacy proprietary “box in a closet” systems can handle, and why modern industrial historians separate compute and protocol layers from scalable storage so factories, power grids, and monitoring dashboards can evolve like modern software.

Then we get concrete on agentic Postgres. Agents move fast, operate in parallel, and are surprisingly strong at SQL, which raises new needs: hybrid search inside the database, safe tool calling (including MCP-style interfaces), and rapid database branching so an agent can explore a fresh snapshot of a multi-terabyte database in seconds, test ideas, and spin it down at near-zero cost. We close with what’s next for Tiger Data across cloud and edge deployments, including the rising importance of AI data centers as industrial-scale control systems, plus Mike’s advice for students building careers in a rapidly shifting AI era.

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Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n
Support the show
More at https://linktr.ee/EvanKirstel