The Ravit Show

Ravit Jain

The Ravit Show aims to interview interesting guests, panels, companies and help the community to gain valuable insights and trends in the Data Science and AI space! The show has CEOs, CTOs, Professors, Tech Authors, Data Scientists, Data Engineers, Data Analysts and many more from the industry and academia side. We do live shows on LinkedIn, YouTube, Facebook and other platforms. The motto of The Ravit Show is to the Data Science/AI community grow together!

  1. 13H AGO

    How Cisco Is Preparing for an Agentic Workforce

    “Your next employee might not be human… and your security strategy isn’t ready for it.” At RSAC, I spoke to Tom Gillis, SVP & GM of Infrastructure & Security Group at Cisco on The Ravit Show, and the conversation quickly moved beyond the usual AI hype into something much more real. We talked about agentic AI not just as a tool, but as a system that can act on its own, make decisions, and operate across enterprise data. That shift is forcing a complete rethink of security, because traditional models were built around humans, not autonomous agents. One thing that stood out was how security teams have always played it safe, often defaulting to “no,” but with agentic AI, that mindset becomes a bottleneck. The real challenge now is enabling this new layer of intelligence without losing control. We also unpacked what it really means to secure an “agentic workforce.” If every employee starts running multiple AI agents, each acting independently, the attack surface grows overnight. So do we start treating these agents like endpoints? Do they need identities, permissions, and governance just like humans? And if that’s the case, how do SOC teams even deal with the explosion of alerts and signals? What I found interesting is that this is not some distant future problem, it is already showing up, and companies like Cisco are actively working through how to design security systems that can keep up. This conversation made one thing very clear to me. The AI conversation is no longer about models or capabilities. It is about control, trust, and how we rethink security for a world where humans are no longer the only actors inside the enterprise. #data #ai #rsac #cisco #theravitshow

    9 min

  2. 4D AGO

    From visibility to control. How Commvault is evolving data security

    Most security conversations at RSAC start with visibility. This one did not. I was at the Commvault booth, which by the way is set up like a full wrestling ring, and I sat down with the José Gomez Field CTO Security to talk about something that feels much more real right now. Control. Not dashboards. Not alerts. Actual control over who is accessing data in real time. What stood out to me in this conversation was how much AI is changing the risk surface. It is not just more data. It is more access, more queries, more non human identities touching sensitive systems all the time. And a lot of traditional tools were never designed for this. One point that stuck with me. Structured data is still one of the hardest things to secure properly. We assume it is easier because it is organized. But when access patterns explode, especially with AI, it becomes harder to track who should see what at any given moment. That is where real time access control starts to matter. Not after the fact. Not in a report. Right when the query happens. We also talked about something every team struggles with. How do you enforce governance without slowing people down? Because if security becomes a blocker, people will find a way around it. The interesting shift here is making security part of the flow instead of a checkpoint outside it. And tying that directly back to resilience. Because the more control you have over access, the faster you can respond and recover when something goes wrong. Another great conversation from the Commvault booth. #data #ai #security #rsac #attack #api #commvault #theravitshow

    8 min

  3. 5D AGO

    Commvault + Microsoft: The Future of Cyber Resilience and Clean Recovery

    I did not expect to walk into a wrestling ring at RSAC conference. But that is exactly what Commvault built at their booth. And after my conversation there, it made complete sense. I spoke to Michelle Hartley Graff and Michael Fasulo from Commvault right in the middle of that ring, and we got into what this partnership actually means beyond the announcements with Microsoft. Here is the reality I keep hearing from teams. Detection is not the problem anymore. The real struggle starts after that. You detect something. Then what? That gap between detection and clean recovery is where most teams slow down. What stood out in this conversation was how tightly Microsoft and Commvault are trying to close that gap. With Microsoft Sentinel in the mix, the day to day operations start to feel more connected. Signals are not sitting in silos anymore. Then you bring in Security Copilot. Now you are not just seeing alerts, you are actually understanding them faster and deciding what to do next without digging through ten different tools. And the most interesting part for me was this idea of real signal sharing. Not just integrations on paper, but systems actually talking to each other in a way that helps you move faster when it matters. Because in a real attack, speed is everything. But so is getting back to a clean state you can trust. That is where this partnership is focused #data #ai #security #rsac #attack #api #commvault #theravitshow

    6 min

  4. 6D AGO

    Commvault at RSAC: Clean Recovery, AI Risk, and the New Security Playbook

    Just wrapped up a conversation with Vidya Shankaran, CISSP from Commvault here at RSAC, and honestly, this one made me pause and rethink a few things. We talk a lot about resilience, threat detection, and now AI data. But what stood out to me is how the conversation is shifting from just “can you recover” to “can you recover clean”. That’s a big difference. With Vidya, we went deep into what’s actually broken in traditional recovery models and why “verified clean recovery” is becoming critical. Not just recovering fast, but recovering without bringing the threat back with you. We also got into the real tradeoff teams are dealing with today. Speed vs accuracy in threat detection. Quick scans vs deeper AI inspection. And the answer is not either or, it is how you combine both in practice. Another big takeaway for me was around AI data becoming a new attack surface. Most teams are still thinking about structured data, but AI pipelines, embeddings, and unstructured data are now part of the risk layer. And the blind spots are bigger than most people think. We also touched on something I hear a lot from teams. How do you actually enforce governance without slowing everyone down. There is no perfect answer, but there are better ways to approach it. If you are thinking about resilience, especially in an AI-first world, this conversation is worth your time. Let me know what stood out to you. #data #ai #security #rsac #theravitshow

    9 min

  5. MAY 5

    Why Robotics AI Is Hitting a Data Wall | Steve Xie (Lightwheel)

    Building AI for the real world is a very different problem than building AI for text. I sat down with Steve Xie, Founder & CEO of Lightwheel on The Ravit Show, to break down what it actually takes to train systems that operate in the physical world. Steve’s journey from Peking University to Columbia University, and then into leadership roles at Cruise, NVIDIA, and NIO, gives him a unique lens into where today’s AI systems struggle when they leave controlled environments and face the real world. One of the biggest takeaways from this conversation is that the core bottleneck in AI is no longer models, it is data. While large language models benefited from massive, passive data sources, robotics has no equivalent. There is no scalable way to collect real-world interaction data, no reliable evaluation layer, and very little infrastructure to continuously improve systems once deployed. This is where simulation becomes critical. In autonomous driving, simulation is helpful. In robotics, it is foundational. You cannot run thousands of parallel experiments in the real world, and you cannot reset physical environments at will. Simulation is what makes learning, testing, and iteration possible at scale. But not everything that looks like simulation actually works. As Steve explains, true simulation needs to be physically accurate, reproducible, and capable of generating actionable feedback. Without that, it cannot train real systems. What makes Lightwheel interesting is their approach to solving this problem. Instead of starting with data collection, they start with evaluation. They identify where models fail, generate targeted data to fix those failures, and create a continuous feedback loop. It is a shift from a passive data pipeline to an active data engine built for physical AI. They are already working with teams like DeepMind, ByteDance, and Alibaba, building infrastructure that sits beneath both robotics companies and AI labs. The bigger idea is simple. You cannot scrape your way to physical intelligence. You have to generate, test, and refine data in closed loops. #data #ai #robot #nvidiagtc #lightwheel #api #training #behaviour #theravitshow

    47 min

  6. MAY 2

    Lightwheel Booth Tour at NVIDIA GTC

    Just walked through the Lightwheel booth at #NVIDIAGTC and this is one of those moments where you realize physical AI is moving much faster than most people think. I got a booth tour by Jonathan Stephens, Chief Evangelist at Lightwheel, and what stood out immediately is how deep their approach goes. This is not just simulation for the sake of simulation. This is about building real-world intelligence that actually works outside the lab. Lightwheel is solving a problem most teams underestimate. You cannot scale robotics or physical AI without massive amounts of high-quality, physics-accurate data. Not synthetic guesswork. Real-world grounded data. And they are doing this at a completely different scale. We are talking about hundreds of thousands of hours of simulation data, not a few thousand. What I found most interesting is how they break this down into three layers: First, the world itself. They are literally measuring real-world physics. Using robotic systems to capture forces, movements, and interactions. Then rebuilding those environments in simulation so models learn from something that actually reflects reality. Second, behavior. This is where it gets powerful. Their Auto Data Gen layer uses LLMs to break complex robotic tasks into smaller actions. So instead of manually guiding every step, you can scale learning in a much more automated way. Third, evaluation. Most teams stop at training. Lightwheel goes further. They are benchmarking performance with tools like Isaac Lab Arena and pushing models through real-world scenarios like cleaning a kitchen or navigating a grocery setup. This full stack approach is what makes them stand out. Also worth noting the ecosystem they are already working with. Stanford, MIT, Nvidia, Figure, ByteDance. That tells you where this space is heading. If you are thinking about robotics, embodied AI, or world models, this is a company to watch closely. Physical AI is no longer theoretical. It is becoming operational. And Lightwheel is quietly building the infrastructure behind it. #data #ai #robot #nvidiagtc #lightwheel #api #training #behaviour #theravitshow

    13 min

  7. MAY 1

    Why Arango Thinks Context Will Define the Future of AI

    Everyone is building AI agents… but very few are talking about the biggest blocker: fragmented data. I sat down with Ravi Marwaha, Chief Product & Technology Officer at Arango, on The Ravit Show to unpack what’s really going on beneath the surface. This was not a typical “AI hype” conversation. We went deep into why agents that are supposed to reason, decide, and act are still struggling in real enterprise environments. And it comes down to one thing: context. Here’s what stood out from the conversation: - Ravi’s journey and why he chose to build at the intersection of data and AI - How fast the space has evolved and why most architectures are already outdated - The real problem with fragmented data when it comes to building reliable agents - The rise of the “context layer” and why everyone is suddenly talking about it - Where Arango fits in vs vector databases, graph databases, and data catalogs - Real customer use cases that go beyond theory - And what the next 18 months could look like for this entire space One thing became very clear to me: The definition of a data platform is changing in real time. It is no longer just about storing or querying data. It is about enabling AI systems to actually understand and use that data in context. If you are building anything in AI right now, this is a conversation worth paying attention to. #data #ai #context #arango #theravitshow

    48 min

  8. APR 29

    Inside Arango 4.0: Making AI Simpler, Cheaper, and Production-Ready

    Just wrapped a great conversation with Ravi Marwaha on-site at NVIDIA GTC. We talked about what Arango is building and why it is starting to show up more in serious enterprise conversations. If you have not come across them yet, worth a look: https://arango.ai/ What stood out to me was how they are thinking beyond just being “another database company” and leaning into something much bigger around context and how enterprises actually use data. Next week, I am sitting down with Ravi for a deeper podcast. We are going to unpack some real questions that I think a lot of people in data and AI are already thinking about: • Why move from enterprise roles to Arango • What makes Arango interesting right now • Are they a database company, a context layer, or something else entirely • How they evolved so quickly • Everyone claims to be an AI company, what actually makes them different • Real enterprise use cases • What the next 2–3 years look like This is going to be a candid conversation. Stay tuned. #data #ai #AWSPartner #arango #NVIDIAGTC #theravitshow

    5 min
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Ratings & Reviews

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About

The Ravit Show aims to interview interesting guests, panels, companies and help the community to gain valuable insights and trends in the Data Science and AI space! The show has CEOs, CTOs, Professors, Tech Authors, Data Scientists, Data Engineers, Data Analysts and many more from the industry and academia side. We do live shows on LinkedIn, YouTube, Facebook and other platforms. The motto of The Ravit Show is to the Data Science/AI community grow together!

Information

  • Creator
    Ravit Jain
  • Years Active
    2021 - 2026
  • Episodes
    521
  • Rating
    Explicit
  • Copyright
    © Ravit Jain
  • Show Website
    The Ravit Show