How Tiger Data Makes Real-Time Databases Ready For AI Agents

Interested in being a guest? Email us at [email protected] patching feels like progress until you realize it is an endless race you cannot win, especially in critical infrastructure. We talk with Benny Czarny Founder, Chief Executive Officer (CEO) and Chairman of the Board at OPSWAT about what it really takes to protect operational technology (OT) environments where downtime is unacceptable and “mostly secure” is not an option. From water systems to power and manufacturing, we unpack why complex IT/OT networks, specialized devices, and compliance demands require more deterministic cybersecurity than the average enterprise playbook. We dig into what some are calling “Black October” and why the volume of vulnerabilities keeps climbing, with AI making it easier for both researchers and attackers to find and exploit weaknesses. Then we get concrete: what should never be exposed to the internet, how air gapping buys time, and why a data diode can reduce risk compared to relying on firewall rules and perfect patch timing. If you are responsible for critical systems, this is a practical conversation about shrinking attack paths, not just reacting faster. A big focus is file-based intrusion risk and the prevention-first idea behind Deep CDR (Content Disarm and Reconstruction). Instead of trying to detect the next clever payload hidden in a document, image, or USB transfer, we explore the “trust no file” model that regenerates content into a known-safe version. We also talk training, awareness, and why cybersecurity education works better when it is accessible and actually interesting. Subscribe, share this with someone who owns an OT environment, leave a review, and tell us what security assumption you would change first.Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5nSupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] is getting frighteningly good at finding software vulnerabilities and stitching them into workable exploits, and security teams were already underwater long before the latest wave of zero-days. That reality raises an uncomfortable question: if offense moves at machine speed, what does credible defense look like?We sit down with Dr. Neha Rungta, Director of Applied Science, Amazon Web Services (AWS) unpack AWS Continuum for Code Vulnerabilities, now generally available, and the practical mechanics behind “autonomous security.” We walk through the full loop from discovery to validation to prioritization to remediation, including why proving exploitability in your real cloud environment matters so much for reducing false positives. When you can combine code signals with AWS context like network reachability, permissions, encryption, and workload placement, the output stops being a giant list and becomes an actionable plan.We also dig into the CyberGym benchmark and what an 89% success rate actually represents: generating a crashing input, producing a patch, preventing regressions by running tests, and confirming the fix resolves the historical vulnerability, not just one symptom. And we get candid about what keeps AI-generated security fixes safe: “boring” but essential engineering discipline, sandboxing, and rigorous test suites.If you care about secure software development, DevSecOps, vulnerability management, and AI security tooling that engineers will actually use, this conversation delivers real details. Subscribe, share the episode with your security or platform team, and leave a review with the hardest part of vulnerability triage in your org.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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/4iQ9b5nSupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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.If this conversation helps you, subscribe, share it with a builder on your team, and leave a review so more developers can find it.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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.Support the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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.If you got value from this, subscribe, share it with a builder on your team, and leave a review with one question you want agents to answer from your data.Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5nSupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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/4iQ9b5nSupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 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/4iQ9b5nSupport the showMore at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected] 35% of executives say AI consistently delivers the business outcomes they expect, and that single stat explains why “AI everywhere” suddenly feels risky. We sit down with Sushant Tripathi Vice President & North America Lead – AI & Digital Transformation from TCS to talk about what changes when enterprises move beyond experimentation and put autonomous agents into real business operations, where the cost of a bad decision is measured in customer harm, regulatory exposure, and millions in downtime. We dig into why classic IT reliability metrics like uptime and latency are no longer enough. AI reliability now includes service-level experience, trust, and the ability to prove what happened and why it happened. Sushant breaks down how enterprises should think about trustworthiness as an engineering responsibility, not just a compliance checkbox, and why “more model capacity” does not automatically create dependable outcomes without strong data, integrations, evaluation, and observability. We also get practical about controls: why many companies can detect AI failures but far fewer can stop them, what “kill switch” thinking looks like, and how an AI maturity journey changes the right level of autonomy. Finally, we talk governance and ROI: who inside the organization must own the policies that let agents act, and how to turn individual productivity gains into measurable enterprise KPIs like revenue, faster resolution, and better customer experience. If you’re building enterprise AI, agentic AI, or AI governance programs, this is the reliability playbook you’ll want. Subscribe, share this with a leader who owns risk or operations, and leave a review with your take: what is the first guardrail every autonomous agent should have?Learn more about Microsoft's ISOC announcement: https://fandf.co/4iQ9b5nSupport the showMore at https://linktr.ee/EvanKirstel