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Agentic AI is showing up everywhere, and it is not waiting for your security program to catch up. From the VMware Explore floor, we talk with Umesh Mahajan, General Manager for Broadcom’s Application Networking and Security Division, about what changes when AI agents become real workloads that talk constantly, call tools through APIs, and evolve by the week. The headline is uncomfortable but useful: AI helps defenders ship faster, but Frontier AI also helps attackers find vulnerabilities and drive more zero-day behavior than many teams are ready to handle.

We dig into the security model that holds up when agents are dynamic and mistakes travel fast. Umesh explains why you have to solve a dual problem: protecting agents from attacks while also preventing a compromised or misdirected agent from attacking others. That leads to concrete priorities like lateral segmentation, distributed firewalling, IDS/IPS, and NDR-style behavioral detection, plus network-level controls to reduce the risk of sensitive data exfiltration. We also talk about discovery, because shadow AI is already a reality. If you cannot identify which agents, MCP tools, servers, and models are running, you cannot confidently separate authorized usage from risky sprawl.

Finally, we tackle the fear every operator has: security that slows the business down. We cover why performance and latency matter more for AI workloads, how enforcement close to the hypervisor can reduce overhead, and what it takes to deploy protection quickly so “bought but not deployed” does not become your weak spot. If you’re building enterprise AI on VMware infrastructure, this is a practical map for getting safer without stalling momentum.

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Frontier AI is changing the rules of security, and enterprises feel it first. From the floor at VMware Explore, we sit down with Purnima Padmanabhan
Vice President and General Manager, Tanzu Division, to unpack what customers are asking for as AI agents move into real systems and real data. The headline: private AI is getting a serious bump, not as a trend, but as a control strategy for regulated industries and anyone worried about risk, governance, and cost. 

We walk through the “threat side” of AI, where advanced models can amplify attacks and push open source security to a breaking point. Purima explains why so many enterprise applications depend on open source, especially Spring and Java, and what happens when vulnerability reports spike. We also dig into TrueSource by Broadcom, the idea of a secure software supply chain for curated open source packages, libraries, and images that enterprises can trust and operationalize. 

Then we shift to the “opportunity side”: moving beyond chat and RAG into workflow automation with enterprise AI agents. That raises hard questions about sandboxing, limiting autonomy, and keeping agents secure. Just as important, we talk about why data projects slow everything down, and why governed access to the right data is what makes agents useful at scale. 

Finally, we get practical about developer experience and SDLC automation, including Purima’s example of running a swarm of 21+ agents across planning, Jira creation, coding, and testing, plus the caution that not all AI-generated code is equal. If you’re building on VMware Tanzu, thinking about private cloud AI, or trying to balance security, sovereignty, and TCO, this is for you. Subscribe, share with a builder on your team, and leave a review. What would make you trust an AI agent in production?
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AI is making decisions that shape real lives, yet most people cannot see how those decisions get made. We sit down with Scott Zoldi, Chief Analytics Officer at FICO, to unpack what “trustworthy AI” actually requires when the stakes include fraud, credit risk, and customer outcomes in heavily regulated financial services. If you have ever wondered why black box models create so much fear and backlash, this conversation puts clear language around the real issues: data provenance, explainable AI, ethical testing, robustness, and the ability to audit a decision after the fact. 

We go beyond buzzwords and get specific about AI governance. Scott explains why responsible AI starts with a shared model development standard, so a large organization is not running a hundred different approaches that no one can consistently defend. We talk about why monitoring is often the weakest link in real world machine learning, when to retire models that drift, and why enterprises need to stay in control instead of outsourcing critical decisions to models they did not build. 

Then we dig into a practical enforcement mechanism: coupling AI governance with blockchain to create an immutable record of requirements, testing, verification, and release decisions. Think of it as an operating manual that travels with the model and can be inspected years later by regulators, customers, or internal teams. We also look ahead to what changing regulation could mean, including the push toward interpretable models, trust scoring for generative AI, and focused language models or small language models built for narrow tasks with auditable data. 

If you care about responsible AI, AI transparency, and building systems people can actually trust, hit play, then subscribe, share this with a friend who works in AI or compliance, and leave a review with the one governance rule you think every model should follow.
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Drug discovery is one of the hardest engineering problems on Earth, except it has not always been treated like engineering. Costs can hover around $2 billion per successful drug, timelines can run 10+ years, and too many patients still wait without a cure. We sit down with Rory Kelleher, who leads global business development for life sciences at NVIDIA, to talk about what changes when accelerated computing meets foundation models, generative AI, and agentic AI that can actually do work.

We break down how scientific agents differ from chatbots, and why tools matter as much as models. Rory explains NVIDIA’s BioNEMO Agent Toolkit and the idea of turning core life sciences capabilities into “agent skills” so biologists and chemists can run complex workflows through natural language. We talk protein design and protein binder design, co-folding, bioinformatics, target identification, and ADMET prediction for toxicity and safety, plus why this wave can “democratize” computational drug discovery for scientists who were never trained as programmers.

You’ll also hear a real example from Bristol Myers Squibb, where foundation models trained on proprietary sequences and compound libraries helped improve a sickle cell molecule profile until it reached first-in-human testing. We dig into what an “AI factory” looks like inside pharma, why teams want to run open models and local LLMs on secure infrastructure, and why scientific judgment becomes more important, not less, as agents increase throughput.

If you care about biotech, pharma R&D, life sciences AI, and the future of medicine, this conversation is for you. Subscribe, share the episode with a friend in research, and leave a review with the one workflow you want agents to tackle next.
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
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Everyone is obsessed with bigger models and faster GPUs, but the part that decides whether AI actually works in the real world is the data behind it. We talk with Paul Speciale CMO at Scality, about what happens when enterprises try to store, protect, and serve AI data at petabyte and exabyte scale, and why the rise of huge context windows turns “just a chat” into a massive storage and latency problem.

We walk through the full enterprise AI pipeline, from data collection and cleansing to inference and the long archive tail most teams never plan for. Paul breaks down which AI phases truly need the fastest storage, why inference can demand microsecond access, and how tiered designs (hot, warm, cool) align to modern GPU stacks. We also dig into what’s changing in the market right now: flash price spikes, constrained supply, and the reality that power per rack often matters more than raw capacity.

From there, we get practical about operations and risk. We explore autonomous infrastructure as “tell the system what you want, not how to do it,” including human-in-the-loop recommendations that can move data to cheaper tiers and cut power bills. We also cover cyber resilient storage, immutability for ransomware defense, and the growing pressure around data sovereignty and even code transparency. If you’re building an AI infrastructure plan, this is the blueprint mindset that keeps GPUs busy and data trustworthy. Subscribe, share this with your infrastructure team, and leave a review with the biggest AI data challenge you’re facing.
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