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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.

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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
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Enterprise AI is everywhere, yet most teams still struggle to move beyond pilots. We sit down with Jason Olkowski, Chief Strategy and Transformation Officer at Creatio to unpack what the data actually says about who is succeeding with enterprise AI adoption and why. One surprising insight: the job-cut narrative is overplayed. The strongest programs use AI to redeploy capacity, automate the right work, and help people do more high-value thinking, selling, and serving.

We dig into what separates the top adoption leaders from everyone else, including how they embed AI across the business, automate a larger share of critical workflows, and scale AI agents across departments instead of leaving AI as a personal assistant. Jason explains why governance is not a compliance checkbox, but a practical system for building, deploying, orchestrating, monitoring, and auditing agentic automation safely in production.

You’ll also hear concrete examples of AI agents inside CRM, like morning account briefings, voice-based updates on the go, and automated post-meeting follow-ups that push clean intelligence back into the system of record. We wrap with pragmatic advice for CIOs under board pressure: pick a governed, LLM-agnostic platform, start with repeatable workflows for fast wins, then reinvest the freed capacity into tougher friction points that slow the business down.

If you’re trying to turn enterprise AI strategy into measurable impact, subscribe, share this with a colleague who owns AI governance, and leave a review with your biggest “where do we start?” question.
Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n
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AI gets a lot messier the moment it leaves the chat window and touches real sensors, machines, and buildings. We sit down with Anthony Canterbury, Head of Engineering and Architecture, JIG-SAW US to dig into AIoT, the intersection of AI and industrial IoT, and why reliability and safety matter more than hype when you are dealing with edge computing, automation, and real operational risk.

We talk through what has actually changed over the last couple decades of machine intelligence, from expert systems and early neural nets to today’s transformer models. Then we get practical: device onboarding, decoding weird formats, parsing, and data normalization. Anthony explains why pairing LLMs with deterministic algorithms can remove a huge amount of manual integration work, and why in-house benchmarking beats trusting public leaderboards when your data is specialized and messy.

From there we tackle the uncomfortable part: hallucinations. If you simply bolt a chatbot onto an existing platform, you can see extreme error rates on IoT workflows. The path forward is architectural, not cosmetic: constrain the model, validate outputs, specialize where needed, and keep a human in the loop so alerting systems evolve without becoming a false-alarm factory. We also dig into treating cloud APIs like “devices,” plus when small, custom edge models win on security, cost, and accuracy for sensitive environments.

If you care about industrial AI, IoT security, edge AI, and building systems that work in the real world, this one is for you. Subscribe for more, share this with a CTO or engineer who is building with AI, and leave a review with your biggest question about AIoT reliability.
Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5n
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