The Ravit Show

Everyone is talking about bigger AI clusters. What happens when those clusters need to span multiple data centers? At Cisco Live, I sat down with Rakesh Chopra, SVP & Fellow, Common Hardware Group at Cisco on The Ravit Show, to discuss one of the less talked about challenges in AI infrastructure: scaling AI beyond a single data center.A few key themes from our conversation:-- The industry is moving from scale-up and scale-out to scale-across architectures-- Connecting GPUs across data centers is becoming a critical challenge as organizations build larger AI environments-- Power efficiency is now as important as raw performance, driving innovation in silicon and optics-- Network reliability and low-latency communication are essential as AI clusters stretch across geographic boundaries-- Co-designing networking, silicon, and optics is becoming a requirement rather than an optimizationThe AI conversation often focuses on models.But the real story may be the infrastructure required to make those models work at scale.#CiscoLive #AI #Networking #Innovation #TheRavitShow

For years, data engineering has been about building pipelines, warehouses, dashboards, and choosing the right tools. But what if we've been solving the wrong problem? I recently sat down with Sai Sundar from WALT, who has spent decades building data platforms at Apple, Yahoo, LinkedIn, Chime, and GEICO. One idea from our conversation really stood out. Companies don't need more data tools. They need better business outcomes.Sai explained how data teams often work in silos. Engineers build pipelines. Business teams ask questions. Analysts sit in the middle translating requirements. The result is slow decisions, duplicated work, and endless back-and-forth.The next evolution isn't another platform.It's creating systems that understand business goals, work with your existing data stack, and help organizations make trusted decisions faster.Some of the topics we covered:* Why data has historically been treated as a second-class citizen* Why business outcomes matter more than adopting the latest technology* How AI is changing the role of data engineering* Why trust and transparency are becoming essential in enterprise AI* What the future of conversational data engineering could look likeThis conversation isn't just about AI.It's about rethinking how data teams create value for the business.#data #ai #dataengineering #walt #theravitshow

Why would someone leave Apple, LinkedIn, and Meta to join an early stage startup? That was the first thing I wanted to ask Ranjith Prabu, CTO when he sat down with me at the WALT AI office in Santa Clara on The Ravit Show.He spent two decades building and scaling data platforms at some of the biggest companies on earth. Now he is the CTO of WALT AI.His answer was simple. Even the best resourced companies on the planet still struggle with data engineering. It is the bottleneck nobody talks about. Engineers build the pipelines but never reach the insight. Analysts have the questions but cannot touch the plumbing. Work gets thrown over the wall, and value leaks at every handoff.Ranjith calls this the chasm. He left to close it.A few things from our conversation that stuck with me.Data engineering used to be locked away. It needed huge teams, huge budgets, and armies of consultants. The way cloud opened up infrastructure, agents are starting to open up data engineering.Determinism matters more than people think. If the CEO asks the same question twice, the answer has to be identical. A model writing fresh SQL every time cannot promise that. That is the line between a demo and production.Tribal knowledge should not live in one person's head. Why you exclude Q2 returns should not walk out the door when an analyst quits. It should live in the system.And data quality is where most data projects quietly die. You can build the most elegant pipeline in the world, but if one number is wrong, trust is gone. Once trust is gone, nobody uses the platform.The part I keep thinking about. Tools give you capability. They do not give you the outcome. The outcome still takes people and months of work. That gap is the real problem, and it is the one Ranjith is now building to solve.Worth your time if you care about where data engineering is heading.#data #ai #dataengineering #walt #theravitshow

PostgreSQL is no longer just a database conversation. It's becoming a platform conversation. I had the opportunity to sit down with Claire Giordano, Principal Group PM Microsoft near Stanford University right after POSETTE: An Event for Postgres to discuss the biggest takeaways from one of the largest PostgreSQL events in the world.A few themes stood out:* PostgreSQL adoption continues to accelerate across organizations of every size* The ecosystem around Postgres keeps expanding, making it easier to build modern data and AI applications* AI was impossible to ignore, but the conversation wasn't about replacing databases. It was about how databases can provide the context, reliability, and foundation AI systems need* The community remains one of PostgreSQL's biggest strengths, with contributors and companies working together to push innovation forwardOne of the most interesting parts of our discussion was where PostgreSQL goes next.As organizations look to build AI-powered applications, support real-time workloads, and simplify their data architectures, PostgreSQL continues to find itself at the center of those conversations.The database landscape keeps evolving, but PostgreSQL's momentum shows no signs of slowing down.In this episode, Claire shares:* Her biggest takeaways from POSETTE 2026* The PostgreSQL trends generating the most excitement* Surprising announcements and discussions from the event* How AI is influencing the PostgreSQL ecosystem* What this year's event tells us about the future of PostgreSQL* What the community should be paying attention to next#data #ai #postgresql #database #opensource #theravitshow

Everyone wants better AI models. A few days back at Data Citizens on the Road by Collibra, I sat down with Reece Griffiths, Field CTO at Collibra on The Ravit Show, to discuss one of the biggest challenges facing enterprise AI today: unstructured data.For years, data governance focused primarily on structured data.But AI is changing the game.Today, enterprise knowledge lives across PDFs, presentations, images, documents, emails, and shared drives. If that content isn't properly governed, AI systems can quickly run into problems:* Generating answers from outdated or draft documents* Exposing sensitive information due to missing confidentiality labels* Missing relevant content because of poor metadata and classificationOne concept from our discussion really stood out:Knowledge decay.Even the most advanced AI models will struggle if the underlying knowledge base is stale, incomplete, or poorly maintained.We also discussed why enterprises are moving toward unified semantic models that connect structured and unstructured data, allowing AI systems to understand business context consistently across the organization.The takeaway?The future of enterprise AI won't be determined solely by model performance.It will be determined by the quality, freshness, and governance of the data behind it.#Data #DataCitizens #Collibra #AI #GenerativeAI #DataGovernance #AIGovernance #EnterpriseAI #Metadata #DataManagement #TheRavitShow

What if the biggest obstacle to AI success isn't the technology? It's the way organizations are structured. At Data Citizens on the Road by Collibra, I sat down with Joyce Snelders Senior Manager at Deloitte on The Ravit Show to discuss what organizations are experiencing as they move from AI experimentation to enterprise-wide adoption.A few key takeaways from our conversation:* Data governance has gone from a "nice to have" to a business priority because AI is only as good as the data behind it.* Many organizations are building AI agents without common standards, creating duplicate efforts and inconsistent outcomes across teams.* Chief Data Officers are increasingly becoming AI leaders, taking responsibility for both data and AI strategies.* The next phase of enterprise AI is not just about technology. It is about governance, operating models, and change management.* Leaders should start preparing for a future where digital FTEs work alongside human employees.One statement from Joyce stood out:Organizations don't have an AI problem. They have a governance and operating model problem.The companies that solve that challenge first will be the ones that scale AI successfully.#DataCitizens #Collibra #AI #DataGovernance #AIGovernance #EnterpriseAI #DataLeadership #TheRavitShow

Everyone is talking about AI governance. Almost nobody is talking about the part that actually decides whether it works. I had a blast chatting with Gaurav Bhandari, AVP and Head of Data and Analytics consulting at Infosys, on The Ravit Show at Data Citizens on the Road by Collibra. One line stuck with me. Roughly 80% of AI governance is just governing the data that feeds your models. We have been here before. Data governance started as a compliance and privacy problem in regulated industries. Then data became the asset everyone wanted to mine for value. Now AI has raised the stakes again, because a model is only as good as the context behind it.Gaurav broke that context down into five things every enterprise has to get right:- Trust. Can you rely on the output.- Ethics. Even when you trust it, is it the right answer to put in front of people.- Regulations. Are you staying compliant as the rules keep shifting.- Privacy. Do people still control their own data.- Security. Is everything safe once it sits inside your workflow.Miss one of these and your AI agents are running on shaky ground.What stood out to me was how the Infosys and Collibra partnership fits this moment. Ten plus years working together, and not just in finance. Retail, manufacturing, life sciences too. Collibra brings the platform. Infosys weaves the policies, controls, and structure into one governance story instead of a pile of disconnected tools.His advice for the next 12 months was refreshingly simple. Stop thinking about data governance. Start building data plus AI governance.The companies that treat these as one problem will move faster than the ones still treating them as two.Full interview is live now.Follow The Ravit Show for more conversations from across the Data and AI world, and subscribe to the newsletter to stay ahead.#data #ai #collibra #governance #infosys #api #datacitizen #theravitshow

Most enterprise conversations around AI start with models, copilots, and agents. This conversation started somewhere else: the data foundation. Last week at Informatica World, I had the opportunity to sit down with Martí Ganduxé Pregona from Schneider Electric and Emilio Valdés from Informatica to discuss what it really takes for enterprises to move from AI experimentation to AI at scale on The Ravit Show!!!!One theme came up repeatedly throughout our discussion:AI is only as good as the data behind it.We explored how the combination of Informatica and Salesforce is expanding the role of data management beyond traditional integration and governance into areas such as agent governance, workflows, and APIs.We also talked about one of the most talked-about announcements from the event: Informatica Headless.The idea is simple but powerful. As enterprises deploy more AI agents, they need a trusted layer that ensures those agents are working with accurate, governed, and compliant data.A few insights from the conversation:* Why trusted data is becoming the foundation of every AI strategy* How enterprises are preparing for an agent-driven future* Why data governance is becoming more important, not less, in the age of AI* The growing need to balance innovation speed with compliance and security requirements* What enterprise leaders are learning from one another as they navigate AI transformationOne thing was clear: the future isn't just about building smarter AI.It's about building an organization that can trust the outputs AI produces.The full interview is below.#data #ai #InformaticaWorld #theravitshow

One of the most interesting conversations I had at Informatica World was with Theodora Bakker, Vice President of Data at Hearst, and Gaurav Pathak, SVP & GM Product Management, DGP and AI at Informatica/Salesforce on The Ravit Show.What stood out to me was how practical this discussion was.We talked about why enterprise leaders continue to bring Informatica IDMC into multiple organizations across industries, what actually makes a company “AI-ready” versus truly “AI-leading,” and how the new Headless announcements could change the way teams think about modern data architectures.Theodora shared a strong perspective from the customer side, especially around scaling data foundations across very different environments. Gaurav also broke down how Informatica is thinking about the next phase of AI and enterprise data management.A few key themes from the conversation:* Why strong data foundations still decide whether AI initiatives succeed or fail* The difference between experimenting with AI and operationalizing it at scale* How enterprises are thinking about flexibility, governance, and modernization with Headless capabilities* What enterprise leaders should prioritize right now to move from AI-ready to AI-leadingIf you’re working in data, AI, analytics, governance, or enterprise architecture, this is a conversation worth watching.#data #ai #InformaticaWorld #theravitshow

Breaking right from Informatica World 2026!!!! Rahul Auradkar, President & GM, Data & Context | AI Foundations,, Salesforce just came off the keynote stage and joined me on The Ravit Show to break down everything that was announced today around:- Headless Data Management- Trusted enterprise context for AI- Agentic AI workflows- Multi-cloud interoperability- The future of enterprise data architectureOne thing that stood out from our conversation:Enterprise AI is no longer just about building models. It is now about building trusted systems that AI agents can actually operate on.We also discussed why metadata, governance, and interoperability are becoming the foundation for the next generation of AI systems across enterprises.A lot of important insights in this one.#data #ai #InformaticaWorld #theravitshow

Are dashboards becoming irrelevant in the age of Agentic AI? I recently sat down with Clarence Rozario from Zoho on The Ravit Show for an in-depth conversation on one of the biggest shifts happening in Data & AI right now: Agentic Analytics!!!!For years, business intelligence has focused on helping people understand what happened. Now we're entering a new era where analytics can help recommend actions, support decisions, and even automate parts of business workflows.In this conversation, we explored:* How BI has evolved from reporting and dashboards to Agentic Analytics* Why enterprises are shifting from insights to outcomes* Whether dashboards still have a role in the AI era* How Agentic AI is changing decision-making inside organizations* Why Context Engineering may become one of the most important capabilities for enterprise AI* The growing importance of semantic layers, business context, and trusted data foundations* Why Data & Analytics platforms must evolve to support agentic systemsOne theme stood out throughout our discussion:AI is only as good as the context and data foundation behind it. Without the trusted business context, even the smartest agents will struggle to deliver reliable decisions.What role do you think dashboards will play in a world increasingly driven by AI agents?#data #ai #agentic #ai #dashboards #api #semanticlayer #theravitshow

Some conversations stay with you because there is no hype in them, just real answers. That is how my interview on The Ravit Show with Thomas Benjamin, SVP of Product Development and Engineering at Boomi, felt at Boomi World 2026.Thomas was clear about why most companies cannot get past their first pilot. They treat scaling as a model problem, when the real issue is everything underneath. The data. The context. The way agents are governed. That is where pilots quietly fall apart.We also talked about what agentic context actually means inside a real enterprise. Thomas explained it in a way that made it obvious why putting AI on top of messy data will keep giving you unreliable answers, no matter how strong the model is.The part I keep thinking about was on partnerships. No single company owns the full stack today. Thomas was honest about what makes a partnership real versus what makes it just a logo on a slide. That difference matters more than most people admit.My takeaway. The winners in this next phase will not be the ones with the flashiest agents. They will be the ones who took the time to get the boring layers right.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

I learn the most from people who can explain hard things simply, and my conversation on The Ravit Show with Patricia Moore, AI Field CTO at Boomi, was one of those at Boomi World 2026.Patricia was direct about why so many AI agent pilots stall. Most teams rush to deployment before doing the work on context, data readiness, and governance. That is the gap between the enterprises getting real value and the ones still running experiments.We spent real time on context engineering. Everyone uses the phrase, but very few can explain it. Patricia made it obvious why context is not just another feature. It is what decides whether an agent can be trusted inside a real business. The same thinking applies to hallucinations. The fix is not bigger models. It is better grounding, cleaner data, and tighter checks around the agent.The part I enjoyed most was her view on the shift happening inside enterprises moving from experiments to real outcomes. The leaders getting it right are not chasing AI for the sake of AI. They are tying every initiative to outcomes that actually matter.My takeaway. The companies winning with agents are treating context, governance, and outcomes as the real product. The model is just one part of the story.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

At Boomi World 2026, I spoke with the amazing Nicole Bradley from Amazon Web Services (AWS) for a conversation on The Ravit Show, and it kept coming back to one idea. Most enterprises are not failing at agentic AI because of the models. They are failing because they are trying to stand up data management and agents without the right partnership underneath!!!!Nicole walked me through the patterns AWS is seeing across customers right now, why Boomi became the partner that made sense, and the use cases where this combination is genuinely hard to beat. We also talked about the roadmap for the next 12 months, and there is a lot coming that customers should be paying attention to.The line that stuck with me. Customers do not need more tools. They need fewer broken seams between them.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

One of the sharpest architecture conversations I had at Boomi World 2026 on The Ravit Show was with Kenneth Maglio, Principal Architect at World Wide Technology. His view on agentic AI was refreshingly honest. Can't wait to do this again!!!!A lot of teams are still debating whether to prioritize data management or agentic AI. Ken's answer was simple. That debate is the problem. If you separate them, you end up with agents that look impressive in a demo and fall apart in production.We talked about what his team was spending too much time on before Boomi, and how much of that work was not moving the business anywhere. The bigger shift was around data freshness. Ken made the case that this is the single biggest factor in whether an agent can actually be trusted. Stale data is not a small issue. It is the difference between a system that scales and one that quietly erodes confidence across the business.We also got into the measurable outcomes WWT has seen since adopting the Boomi architecture, and what would break if that layer was removed. His answer made it clear how foundational this has become.The takeaway for me. Agentic AI in the enterprise will not be won by whoever has the best model. It will be won by whoever has the cleanest, freshest, most governed data feeding those agents in real time.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

Quick conversation on The Ravit Show from Boomi World 2026 with John Baker, CIO and CISO at Lexitas. One of the most grounded customer perspectives I have heard this year. Thanks for the amazing insights, John :)John was clear about why Lexitas refused to treat data management and agentic AI as separate projects, what his team stopped wasting time on after Boomi, and why agent governance is the part most enterprises underestimate. Agents are only predictable when the layer beneath them is.My takeaway. The architecture decision is the AI decision.#data #ai #BoomiWorld #theravitshow #BoomiAmbassador

Enterprise software is changing. I sat down with Brian Landsman, CEO of AgentExchange at Salesforce, to talk about what an agent-first future actually looks like. #salesforcepartnerThis wasn’t a surface-level conversation. We went deep into what’s coming next.Here’s what stood out:* AgentExchange evolved from a marketplace directory into a commerce and discovery layer, is rethinking how enterprises deploy software* Headless architectures could fundamentally reshape how people interact with enterprise systems since traditional UIs matter less* Agents are moving from assistants to becoming the primary interface* Workflows need to be redesigned from the ground up for an agent-first world* Success will be defined by agents executing end-to-end tasks, not just supporting humans* The gap between AI pilots and production is finally starting to closeWe also discussed how individuals can go to market faster with $50M AgentExchange Builders Initiative.Watch the full conversation and let me know what you think.#data #ai #tdx26 #salesforce #workflows #api #headless360 #agentexchange #apps #theravitshow

Some conversations stay with you for days. This one did. Last week at Team '26 in Anaheim, I spoke to my favourite Tamar Yehoshua, Chief Product and AI Officer at Atlassian. A week later, I'm still thinking about three things she said.Here's the thing about Tamar. I always learn something new every time we talk. She's one of those rare leaders who can zoom from a product detail to a 5-year vision in the same breath without missing a beat.What makes her perspective so useful: Tamar has shipped product at Google Search, led product at Slack through their tenfold growth and IPO, and ran product and technology at Glean. Three different eras of how knowledge workers find what they need at work. And now she's leading Atlassian's AI strategy at the moment the entire category is being redefined.Team '26 was her first Team event as CPO and AI Officer. You could feel the weight of that moment in the room.Here's what we got into:- Day one through her eyes. What it actually felt like to walk on stage as the new CPO and announce the biggest set of AI launches in Atlassian's history.- The connective thread. Atlassian covered massive ground in the keynote. AI for developers, service teams, product teams, agents in Jira. I asked Tamar how she wants people to think about Atlassian's AI strategy as one story instead of five. Her answer reframed the whole keynote for me.- How customers are actually using Rovo. Not the marketing version. The real version. What's working, what's surprising, where the patterns are forming.- The shifts that matter. Tamar has lived through search becoming the default interface, then SaaS becoming the default workplace, then chat-based collaboration becoming the default for distributed teams. I asked what excites her most about this moment. Her answer wasn't what I expected.- The next 5 years. How teams will actually work differently. Not predictions. Patterns she's already seeing inside Atlassian's own teams.The throughline across everything she shared: context is the moat. Models will keep getting better and cheaper. What separates the winners is what your AI knows about how your company actually works.Big thank you to Tamar for the time and the candor, and for being so generous with her thinking every time we connect. And to the Atlassian team for hosting me at Team '26.#data #ai #atlassian #team26 #theravitshow

300 to 600 hours reclaimed every single week. Ticket creation cut by 75%.These are not projections. This is what DocuSign is actually seeing from Atlassian's Rovo rollout right now.New episode of The Ravit Show is live with Shivi Singh Verma, MBA, PMP®, CSM®, PMI-ACP®, ITIL®, Senior Manager of Engineering at Docusign, recorded at Team '26 in Anaheim.If you have been waiting for an enterprise AI deployment story that goes past pilots and demos, this is the one to watch.Shivi leads GenAI and AI Agentic strategy at DocuSign. They have actually done the hard work most companies are still talking about. Phased rollout, real guardrails, measured ROI, and a clear plan for what comes next. Their philosophy on this is sharp: adopting AI at scale requires foundational trust, robust governance, and clear guardrails. Not optional, not later, on day one.What we got into:- The tipping point. What finally convinced DocuSign to move forward with Rovo. There is a specific moment Shivi described that I think every engineering leader weighing this decision needs to hear.- The phased rollout. What the pilot looked like, what surprised Shivi as they expanded beyond it, and the guardrails they put in place that they would recommend to other enterprises starting today. This is the playbook section.- How they actually measured ROI. Most companies struggle to prove AI value to leadership. DocuSign did not. I asked Shivi how they measured the 300 to 600 hours weekly and the 75% ticket reduction, and what convinced their leadership these gains were real and sustainable. The answer is more disciplined than I expected.- What comes next. DocuSign is planning to let non-technical teams build their own governed agents through Rovo Studio, and shift from reactive AI to proactive AI. We spent time on what that future looks like, and what they are doing now to prepare for it.- The line from Shivi that stayed with me: AI at enterprise scale is not a model problem. It is a trust problem. Get the governance right first and the productivity gains follow. Skip that step and the project will not survive its first incident.If you are an engineering leader, a CIO, or anyone trying to build the business case for enterprise AI inside your own company, watch this one. Shivi gives you the playbook.Big thank you to Shivi for the openness about what worked and what was harder than expected. And to the Atlassian team for the front-row access at Team '26.#data #ai #atlassian #team26 #theravitshow

I had a blast chatting with Sherif Mansour, Head of AI at Atlassian, at Team '26 in Anaheim. If you want to understand what Atlassian actually shipped this year and why it matters, this is the conversation to watch.Sherif is the person inside Atlassian who has been thinking about AI longest and hardest. He runs Atlassian Intelligence, the generative AI platform that powers Rovo, the Teamwork Graph, and the agent experiences across Jira, Confluence, and Loom. When the entire company stage talks about AI for two hours, Sherif is one of the people who actually built what they are talking about.That made this conversation different from most AI interviews you will hear this year.What we covered:The keynote in his own words. Atlassian announced AI for developers, service teams, product teams, agents in Jira, and a brand new Product Collection. I asked Sherif what excites him most across all of it. His answer surprised me.Teamwork Graph, opened up. The 150 billion connection graph is now accessible to any agent through MCP, CLI, and Forge connectors. I asked Sherif what "opening it up" actually means in practice, and what changes for builders outside Atlassian who want to plug in.Agent orchestration in Jira. What it looks like when an agent is not just answering questions but coordinating work across an entire project. Sherif walked through how Atlassian thinks about keeping humans in the loop where it matters, and where to get out of the way.AI mythbusting. Sherif came in with strong opinions on the myths he is tired of hearing. We spent real time here. If you work in or around enterprise AI, this section alone is worth the watch.The line that stayed with me: the hardest problem in enterprise AI is not making models smarter. It is making them aware of how your company actually works. Everything Atlassian shipped at Team '26 traces back to that one bet.Big thank you to Sherif for the depth, the candor, and the patience with my follow-up questions. And to the Atlassian team for the front-row access at Team '26.#data #ai #atlassian #team26 #theravitshow

What happens when the database becomes an active participant in AI applications instead of just a place to store data? In this session of The Ravit Show, I sat down with Jay Gordon and Patty Chow to unpack the biggest announcements and takeaways from CosmosDB Conf!!!!One theme stood out throughout the conference:AI is not just changing applications. It's changing the database itself.We discussed:- How OpenAI scales from zero to millions of queries per second- Why Walmart relies on globally distributed architectures to keep checkout systems running during failures- How vector search, full-text search, and hybrid search are becoming native database capabilities- The rise of agent memory architectures and AI-native applications- Why developers need real-time visibility into query costs- How to think about CosmosDB vs Azure DocumentDB based on workload requirements- What the Azure CosmosDB Agent Kit means for developers building AI-powered systemsOne of my biggest takeaways was that retrieval is increasingly moving into the database layer itself. Instead of stitching together multiple services, developers can now work with a more unified approach to search, AI, and data.If you're building AI applications, working with data infrastructure, or trying to understand where databases are headed next, this conversation is worth watching.The full interview is now live.What was your biggest takeaway from CosmosDB Conf this year?#data #ai #azure #cosmosDB #microsoft #api #microservices #theravitshow

BREAKING from Rubrik!!!! They just made the most aggressive bet I have seen on where enterprise security is heading. I interviewed Anneka Gupta, their Chief Product Officer, right as it all went public at Rubrik FORWARD on The Ravit Show.Two announcements came out of Las Vegas this week.First, Rubrik AI. The platform itself is now an agent. You define the outcome, recover clean, contain the blast radius, restore the business, and Rubrik AI reasons over your data, identities, and deployed agents to deliver it. Recovery sequences that took human teams weeks now finish in minutes. Every action stays auditable, attributable, and reversible.Second, Rubrik Agent Cloud for Anthropic's Claude Code and Claude Cowork. Claude is being adopted faster than any agentic technology Rubrik has seen. These agents write, push, and deploy code on their own, while enterprise security was built assuming a human stays in the loop. RAC closes that gap with real-time governance through SAGE and the industry's only Agent Rewind, which reverses an agent's actions and recovers the codebase even when a mistake outruns version control.Here is why these two launches are really one story.Rubrik's Zero Labs research found 86 percent of firms expect AI agents to outpace their existing security capabilities. Most vendors respond to that stat by selling more visibility. Rubrik's answer is different: if threats and agents move at machine speed, defense and recovery have to move at machine speed too. So they built an agent to protect you from agents.That framing is what I pushed Anneka on in our conversation.We got into what a runaway AI risk actually looks like inside a security environment, and how Agentic Guardrails stop one before it spreads. Which parts of a multi-week recovery workflow are genuinely automated and which still need a human call. How one agent reasons across Rubrik Security Cloud and Rubrik Agent Cloud at the same time, spanning data, identity, and third-party agents. The role of identity in agentic resilience. And why Databricks Unity Catalog was chosen as the first native lakehouse integration for RAC, with more connectors coming.My take after 750 plus interviews in this space: every enterprise I talk to is racing to deploy agents, and almost none of them can answer one question. What happens when an agent does something wrong?Observability tells you what happened. Rewind lets you undo it. That difference is going to define the next phase of enterprise AI, because the companies that win with agents will not be the ones that deployed fastest. They will be the ones that stayed in control.#data #ai #cybersecurity #theravitshow

Most teams think they have an AI strategy. But what they actually have… is fragmentation. At Atlassian Team ‘26, I sat down with Molly Sands, PhD, Head of Teamwork Lab on The Ravit Show to talk about what’s really happening inside teams today.Her work at the Teamwork Lab is different. They’re not just studying the future of work. They’re actively testing how AI changes the way teams operate.A few takeaways that stood out:– The biggest problem isn’t lack of AI tools. It’s the “AI fragmentation tax.”Too many tools, not enough alignment.– Top teams are not just adopting AI.They’re redesigning how they set goals, collaborate, and make decisions.– A lot of “work about work” still exists.Status updates, coordination, chasing context. This is where AI should be making the biggest impact.– The best approach to AI adoption is not top-down mandates.It’s embedding AI into everyday workflows so teams naturally use it.One thing I appreciated… Even at Atlassian, there isn’t a perfect answer yet.And that’s the point. This is still being figured out in real time.If you’re thinking about AI for your team, don’t start with tools. Start with how your team actually works.#data #ai #team26 #atlassian #theravitshow

Can Postgres become the foundation for the next generation of AI applications? As we get closer to POSETTE 2026 by Microsoft in partnership with AMD, I sat down with Charles Feddersen from Microsoft for a curtain raiser conversation about one of the most important events in the Postgres community.Over three days, POSETTE 2026 will bring together 50 speakers and 44 sessions, covering everything from the future of Postgres to its growing role in AI, vector search, and modern application development.In our conversation, we discussed:* Why Postgres continues to gain momentum across the industry* What many people still underestimate about Postgres and AI* The evolution beyond pgvector and RAG* The sessions and speakers Charler is most excited about* How POSETTE creates value for both beginners and Postgres expertsIf you work with data, AI, analytics, or application development, this is a conversation you won't want to miss.#Data #AI #Postgres #POSETTE2026 #AI #DataEngineering #Database #OpenSource #TheRavitShow

Most AI tools are still just indexing documents. The Teamwork Graph has 150 billion connections. That difference is the whole game. I sat down with Jamil Valliani, the Head of AI Product at Atlassian, during Team '26 on The Ravit Show to understand why they are betting the next decade on this approach. Twenty years in Search before this role. Long before vector databases were trendy. Long before RAG was an acronym.A few things we got into:What the Teamwork Graph actually is. Why this architectural choice separates Atlassian's AI from everything else in the market.150 billion connections vs document indexing. Most enterprise AI tools search your text. This connects people, work, decisions, and outcomes across systems. The gap is bigger than I realized.Why connected data wins on accuracy. Atlassian's internal benchmark: 44% more accurate results using 48% fewer tokens. We broke down what is actually happening under the hood.A Search veteran's read on this moment. What makes this AI shift different from every other one. The most grounded take I heard at any conference this year.The line that stayed with me: in the next era of work, the company with the best context will win. Not the company with the best model. Models are getting commoditized. Context is not.If you work on retrieval, RAG, or graph-based AI inside an enterprise, this one is for you.#data #ai #atlassian #team26 #theravitshow

At SAS Innovate, I had the chance to speak with Reggie Townsend about one of the most important topics in AI right now.Trust. What stood out from our conversation is how the market is evolving. Enterprises are moving quickly with AI, but the real challenge is no longer building models. It is ensuring visibility, transparency, and control as these systems start influencing real decisions.We also discussed SAS’ new AI Navigator and why it matters. It is not just another layer. It is a way for organizations to understand where they are in their AI journey and how to move forward in a structured, governed way. This becomes critical, especially in industries where accountability is not optional.My biggest takeaway.If trust does not scale, AI will not scale.Learn more from the interview below!!!!#data #ai #SASInnovate #SASVisionary #theravitshow

From SAS Innovate, continuing conversations with leaders shaping how enterprise AI actually gets deployed. I spoke to Marinela Profi from SAS and this one cut straight to where the industry really is right now.There’s a lot of excitement around AI. But most enterprises are still not ready to scale it. We talked about why. The gap is no longer about models. It’s about systems, governance, and how AI fits into real enterprise workflows.One of the most interesting parts of the discussion was around MCP (Model Context Protocol) inside SAS Viya. This is about giving AI systems the right context, control, and structure so they can operate reliably in production environments.Because without that, AI stays stuck in experimentation. We also went deep into why SAS is building dedicated agent infrastructure instead of just layering AI on top of existing tools. That decision matters.It allows enterprises to move faster, while still maintaining control, auditability, and trust. That balance is what most organizations are struggling with today.My biggest takeaway. The industry is moving from generative AI experimentsTo governed, production-ready intelligence. And that shift requires a completely different approach to architecture.#data #ai #SASInnovate #SASVisionary #theravitshow

Last week at SAS Innovate, I spoke with Dan Soceanu about something every enterprise is talking about, but very few have solved. AI-ready data.The conversation was very practical. AI needs data, but not just more data. It needs data that is trusted, governed, and fit for purpose, especially as automation and agents start making decisions. We also went into digital sovereignty, which is becoming a key concern. Organizations are thinking deeply about where their data lives, how it is controlled, and how it aligns with regulations across regions.What stood out is that this is not a future problem. It is a current one. And looking ahead, the focus is shifting from collecting data to making it usable and reliable for AI systems. My biggest takeaway.AI success will depend more on data discipline than model sophistication.#data #ai #SASInnovate #SASVisionary #theravitshow

I’m here at SAS Innovate, continuing conversations on what’s next for enterprise AI. I had a blast chatting with Amy Stout, and this one was focused on something many enterprises are curious about but not fully ready for yet. Quantum AI.The discussion was very grounded.We talked about the real barriers enterprises face today:- Access to quantum systems- Lack of expertise- And uncertainty on where it actually fits in business problemsWhat SAS is doing with Quantum Lab is interesting because it is trying to remove that friction. Making quantum more accessible, more practical, and connected to real use cases.The key takeaway for me was this. Quantum is not about replacing AI. It is about expanding what problems we can solve.And while it may still be early, the groundwork being laid now will define who is ready when it scales.That’s the kind of long-term thinking I’m seeing here at SAS Innovate.More content coming from SAS Innovate on The Ravit Show.#data #ai #SASInnovate #SASVisionary #theravitshow

I’m here at SAS Innovate, speaking with leaders who are shaping how enterprise AI actually gets deployed. Just spoke to Alyssa Farrell from SAS on The Ravit Show, focused on how SAS is accelerating AI across industries like financial services, public sector, and life sciences.What stood out was how clearly this is not about generic AI anymore. We talked about pre-packaged agents and industry-specific models, and why they matter. Most enterprises don’t struggle to build models. They struggle to make them work in real environments.Regulation, workflows, and domain complexity are not edge cases. They are the system.SAS is leaning into this by embedding that context directly into AI systems, which is what makes agentic AI actually usable at scale.This is the shift I’m seeing here. From building AI. To deploying AI that understands the businessStay tuned for more content on The Ravit Show.#data #ai #SASInnovate #SASVisionary #theravitshow

AI conversations are everywhere, but what stood out to me in my chat with Harmeen Mehta from Equinix at Google Cloud Next '26 was how grounded their approach is. They did not start with big external announcements. They started inside.Harmeen shared a simple idea. If AI is going to change how a company works, it has to show up in how employees work first. Not as a side experiment, but as part of daily workflows. That shift is what moved AI from a pilot to something core to the business.At Equinix, AI is not sitting on the edges. It is being used to remove real friction from day-to-day work. Helping teams move faster, reduce repetitive tasks, and focus on higher value problems. That is where the impact starts to become real.But what stood out even more was how they approached trust.Employee hesitation is real. Questions around accuracy, reliability, and job impact come up quickly. Instead of ignoring that, they leaned into it. Clear use cases, transparency, and gradual rollout made a big difference in adoption.The biggest takeaway from this conversation was simple.Do not try to scale AI before you make it work internally.If your own teams are not using it, trusting it, and seeing value from it, scaling it across the business will not work.And looking ahead, the shift is already happening. Not years from now, but right now. AI is starting to change how work gets done inside enterprises, one workflow at a time.#data #ai #equinix #security #googlecloudnext #api #google #theravitshow

Just wrapped a great conversation with Woon Ho Jung, CTO - Cloud Native, Commvault, at Google Cloud Next 2026 and this one hit a nerve. Everyone is talking about multi-cloud, AI pipelines, and scaling data.But almost no one is talking about what’s quietly breaking underneath it all. Data protection. We got into what’s really happening inside enterprises today.Teams assume replication and retention policies are enough. They’re not.At scale, across billions of objects, things get messy fast. Gaps show up where you least expect them.That’s where the big announcement comes in. Clumio is going deeper with Google Cloud. Clumio for GCP is not just another backup solution. It’s a rethink of how you protect cloud-native data, especially inside Google Cloud Storage where most AI and analytics pipelines live today.What stood out to me:- Protecting data at massive scale is still an unsolved problem for many teamsNative tools give a false sense of security- Resilience in the AI era needs a completely different approachIf you’re building on Google Cloud right now, this is something you need to pay attention to. This is not about backup. This is about trust in your data layer.#data #ai #commvault #security #googlecloudnext #api #google #theravitshow

AI sounds exciting… until you actually try to use it inside a company. That was my biggest takeaway from my conversation with Michael Fasulo from Commvault at Google Cloud Next '26 on The Ravit Show.Everyone wants AI, but when it comes to real deployment, things break. Data is messy, systems are disconnected, and trust is missing. The gap is not ambition, it is readiness.One line that stayed with me. If your data is compromised, your AI is compromised.And with agentic AI, it gets even more real. These systems are not just answering anymore, they are taking actions. That means mistakes can have real impact.My takeaway is simple.The companies that win will not be the ones trying the most AI. They will be the ones fixing their data and putting the right guardrails in place first.#data #ai #commvault #security #googlecloudnext #api #google #theravitshow

AI agents sound exciting. But my conversation with A. Ravi M., CIO at Box at Google Cloud Next '26 on The Ravit Show was not about excitement.It was about risk. We are moving from AI that answers to AI that acts. And that shift introduces a completely new set of challenges. Not just accuracy, but control, access, and accountability. Ravi pointed out that most enterprises are not struggling with AI capability. They are struggling with governance. Who has access to what data, what an agent is allowed to do, and how you track those actions. Those gaps become very real once agents start operating on sensitive enterprise content.And that is where security needs to evolve. It is no longer enough to protect data at rest. You have to think about how AI agents interact with that data in real time, and what guardrails are in place when they take action.The partnership with Google Cloud plays a big role here. With platforms like Vertex AI and BigQuery, the focus is not just on building agents, but on building them with the right controls and visibility from day one.The biggest takeaway for me was simple. If you are a CIO thinking about AI agents, do not start with deployment. Start with trust. Because without that, none of this scales.#data #ai #box #security #googlecloudnext #api #google #theravitshow

Everyone is talking about AI agents, but after my conversation with Ben Kus, CTO at Box at Google Cloud Next 2026 on The Ravit Show, one thing became very clear. Agents are useless without "context". #boxpartnerBen kept coming back to that word. Not just data, not just models, but context. In an enterprise setting, context means understanding the full picture around your data. Who created it, where it lives, who can access it, and how it should be used. Most companies already have massive amounts of content, but it is fragmented and static, and that is the real problem.What stood out is how Box is approaching this. They are not just storing enterprise content, they are structuring it in a way that AI agents can actually use, turning content into something agents can reason on, not just retrieve. And this is where the partnership with Google Cloud comes in. With models like Gemini and platforms like Vertex AI, they are able to operationalize that context at scale in real workflows.The biggest takeaway for me was simple. If you want to become AI-first with agents, do not start with the agent. Start with your data. Structure it, govern it, and make it usable. That is what actually makes AI work.#data #ai #box #security #googlecloudnext #api #google #theravitshow

BREAKING: Kore.ai launches Artemis — a new generation Agent Platform for enterprise AII just sat down with Prasanna Arikala at their San Francisco office right after this launch.And here’s what stood out.For years, most enterprises have been stuck in the same loop:-- Build AI pilots-- Struggle to productionize-- Lose control over governance-- Start overArtemis is Kore.ai’s answer to that problem.This is not just another AI platform.It is a ground-up rebuild focused on one idea:AI should not just assist. It should build, govern, and optimize itself.Prasanna shared something interesting during the conversation.They didn’t evolve the platform.They rebuilt it from scratch around what enterprise AI actually needs in 2026:-- AI building AI-- Built-in governance, not bolted on-- Optimization as a continuous loop, not an afterthought-- Designed for regulated industries from Day 1And this is where it gets real.Most enterprises today already have Amazon Web Services or Microsoft.But the gap is not infrastructure.The gap is:How do you go from AI experiments to reliable, governed, production systems at scale?That’s the layer Kore.ai is going after.Also, one insight from Prasanna that stayed with me:The biggest mistake is thinking AI is a model problem. It is actually a systems problem.This launch is a signal.We are moving from:“Let’s try AI”To:“Let’s run the business on AI systems we can trust”I’ll be dropping the full interview soon on The Ravit Show where we go deeper into:-- Why they rebuilt everything-- What “AI building AI” actually means-- Where enterprise AI is headed in the next 18 monthsThis one is worth paying attention to.#data #ai #koreai #agents #theravitshow

AI infrastructure conversations usually stay very technical. But my chat with , Santosh Erram, VP Partnerships DDN at Google Cloud Next '26 on The Ravit Show went in a different direction.He kept bringing it back to one thing. Business value. Yes, compute is growing. Yes, GPUs are everywhere. But that is not the real bottleneck anymore. Data is. If you cannot move it fast, access it easily, and actually use it, your AI investment does not translate into outcomes.What stood out was how fast things are moving. Their partnership with Google Cloud went from idea to launch in under six months. And now they are pushing things like 10 terabytes per second performance and hybrid tiering to meet real enterprise demands.But the real proof was in the use cases.- Salesforce pushing GPU utilization from around 48% to over 90%.- Resemble AI driving cost savings.- Sony Honda Mobility using it for autonomous driving.Even financial firms bursting massive workloads into the cloud, hitting petabyte scale in a single day. This is not experimentation anymore. We are moving from AI pilots to real production. And the shift from training to inferencing is going to define the next phase. My biggest takeaway. AI is no longer limited by models. It is limited by how fast and how well you can work with your data.#data #ai #ddn #infrastructure #googlecloudnext #api #google #theravitshow

AI is moving fast, but after my conversation with Alex Bouzari, Co-Founder and CEO at DDN, at Google Cloud Next '26, one thing became clear.The bottleneck is no longer the model.It is the infrastructure behind it. Alex broke it down in a very real way. Today’s AI systems are powerful, but the way data moves through them is still inefficient. You train these large models, but when it comes to actually running them at scale, things slow down. Latency increases, costs go up, and performance becomes unpredictable.That is what is broken.He shared how this shows up in real scenarios. When enterprises deploy AI, especially with large models, they struggle with speed and consistency. It is not that the model cannot perform, it is that the infrastructure cannot keep up with the demand.At Next, DDN focused on solving exactly this. Building what Alex called a new foundation for AI, designed for high-performance workloads where data access and speed matter just as much as the model itself.One concept that stood out was KV cache.It sounds technical, but the idea is simple. Instead of recomputing everything every time a model runs, you reuse key pieces of information. That reduces latency and makes systems faster and more efficient. In large-scale AI systems, that becomes a big deal.The bigger shift here is clear.We are moving from experimenting with AI to operationalizing it at scale. And that means infrastructure is becoming the deciding factor.What makes DDN different is their focus on this layer. Not just enabling AI, but making sure it actually performs in real-world environments.My takeaway. The future of AI will not just be defined by better models. It will be defined by better infrastructure.#data #ai #ddn #infrastructure #googlecloudnext #api #google #theravitshow

The man, the legend, CTO of Qlik, Sam Pierson. Always love chatting with him and this time I asked him some hard questions. I like the depth of this conversation. Thanks Sam for always being such a great sport and sharing some enterprise gaps in the Data & AI World :)#data #qlik #ai #qlikconnect #theravitshow

I had the chance to discuss key takeaways with Mike Capone, CEO of Qlik at Qlik Connect 2026 on The Ravit Show. What made this conversation stand out was how real it felt. No hype, no buzzwords. Just a clear view of where things are actually going. The shift is happening fast. We are moving from AI that gives answers to AI that takes action. And that sounds simple, but when you unpack it, it changes everything. It changes how data is prepared, how systems are designed, and how much trust you need before letting AI operate inside real workflows.We talked about what is making this possible now, and why bringing analytics, data engineering, and trust together is no longer optional. It was also interesting to hear how companies like UPS, Schneider Electric, and HelloFresh are already moving from insights to execution.One point that really stayed with me was this. Perfect models are not the goal. Impact is. And the teams that understand this are the ones moving faster.We also spoke about trust, which is becoming the foundation for everything. Because once AI starts taking actions, you cannot afford to get it wrong.And I ended with a simple question. What is Qlik’s role in the AI stack today. The answer was sharp and very telling..#data #ai #qlikconnect #qlik #daredevil #api #trust #dataquality #agentic #agents #theravitshow

Spent time at Qlik Connect this week and one thing became very clear to me. Everyone is talking about AI, but very few are talking about what actually makes AI work. I had a great conversation with Sean Stauth and Kyle Jourdan from Qlik, on The Ravit Show and we went beyond the usual AI hype. What stood out to me is that most teams are not failing at AI because of models. They are getting stuck on data. Not because they don’t have data, but because they don’t trust it, can’t access it easily, or simply can’t operationalize it fast enough.That gap between “we have data” and “we can actually use it for AI” is where most projects slow down. We also spoke about the constant tension between speed and foundations. Everyone wants to move fast with GenAI, but if your data layer is weak, you are just scaling confusion. The real challenge is not choosing between speed or building the right foundation. It is figuring out how to do both at the same time.Another point that stayed with me was around agentic AI. Grounding LLMs in enterprise data is no longer optional. It is the difference between something that looks good in a demo and something that actually works in production. And again, it all comes back to data quality, governance, and accessibility.My biggest takeaway from this conversation is simple. AI is no longer the hard part. Data is. The teams that figure this out will move ahead very quickly. The rest will keep experimenting without real impact.Conversations like this are exactly why I enjoy being on the ground at events like Qlik Connect. Learn from them below!!!! #data #qlik #ai #qlikconnect #theravitshow

AI is not the problem. The foundation is. That’s exactly what we unpacked in this conversation with Bruno BILLY and Rahul Bakhshi at APGAR On Air. Here’s what stood out for me. Most organizations are pushing hard on AI. But the data underneath is still fragmented, inconsistent, and not ready for scaleAnd that’s where things breakIn this conversation, we go deep on- What a real data foundation actually looks like- Why most AI initiatives fail before they even start- Where things break at scale across governance, ownership, and trust- What teams can realistically do in the next 90 daysIf you are building AI that needs to run in production, this is worth your timeWatch the full conversation here: https://lnkd.in/g-VSBqat#data #ai #apgar #theravitshow

Another solid conversation from Qlik Connect 2026. This time with Gregory Pierce, MBA from Amazon Web Services (AWS), and we went deep into what it actually looks like to build data and AI systems at scale.What I liked about this discussion was how practical it was. There is always a lot of talk about cloud and AI, but this was more about how teams are actually making it work.We talked about the partnership between AWS and Qlik, and how it is helping customers bring everything together. Data integration, analytics, governance, all running on a scalable foundation. Not as separate pieces, but as something that needs to work end to end.One point that really stood out was around growth. Data volumes are increasing fast, AI workloads are getting heavier, and most teams are still dealing with legacy systems. The question is not just how to move to the cloud, but how to do it in a way that sets you up for what comes next.We also got into AI and GenAI in modernization. Where does it actually help? Things like speeding up migrations, reducing manual effort, and making systems easier to understand. But at the same time, Greg was clear about being careful. If your data is not reliable, adding AI on top just increases risk.And that led to another important point. Accuracy and trust. As teams use AI to transform legacy systems, they need strong validation, governance, and a clear understanding of what is happening behind the scenes.The last part of the conversation was about flexibility. This space is changing fast. New tools, new architectures, new expectations. The teams that win are the ones that stay adaptable and do not lock themselves into one way of doing things too early.Overall, this was a very grounded conversation on how cloud, data, and AI actually come together.#data #ai #qlikconnect #qlik #daredevil #api #trust #dataquality #agentic #agents #theravitshow

Another great conversation from Qlik Connect 2026. I sat down with Christopher Powell, and this one was all about customers. Not in a generic way, but what it actually means when you see real use cases in action.What stood out was how much focus Qlik is putting on customer stories. When you hear how teams are actually using data in their day to day work, it just clicks. It is not theory anymore. It becomes something you can relate to and apply.We talked about examples across regions, including teams in places like Japan solving very specific problems, and even sports organizations using data to compete with limited budgets. Those stories make everything feel a lot more real.He also walked through some of the key announcements.The Data Impact Awards stood out. Six customers from around the world being recognized not just for using data, but for actually driving measurable impact.Then the push around agentic AI. You can see where things are going. Less about static insights, more about systems that actually help move things forward.The ServiceNow partnership was another big one. Bringing trusted data into a system of action instead of keeping it separate. That shift is important.And there were updates on the engineering side as well, which felt like a direct response to what customers have been asking for.Looking ahead, there is a lot coming. More agentic AI releases through the year, built by teams across Sweden, India, the US, and Canada. And a continued focus on sharing more real customer stories.Overall, this conversation made one thing clear.Technology matters, but what really brings it to life is how customers are using it.#data #ai #qlikconnect #qlik #daredevil #api #trust #dataquality #agentic #agents #theravitshow

I got a chance to sit with Charlie Farah at Qlik Connect on The Ravit Show and this was one of those conversations that makes you pause and rethink how we look at data and analytics today. We often talk about tools, platforms, and the next big thing in AI. But what stood out to me in this conversation was how different regions are evolving at different speeds, especially across APAC versus the US and Europe. The ambition is the same, but the maturity, priorities, and constraints vary a lot more than we usually acknowledge.We also discussed Qlik’s analytics roadmap, and it is interesting how fast things are moving. It feels like Qlik Answers just launched, and now the conversation is already shifting toward how these capabilities actually get used in real business workflows. Not just dashboards or insights, but decisions.And that is where I think the biggest gap still exists. Not between data and analytics, but between analytics and actual business value. Many teams are still very good at generating insights, but not as strong at embedding those insights into day to day operations where decisions are made. That last mile is still broken in many organizations.Another point that stayed with me was how easy it is to overlook the hidden challenges. It is not always about technology. It is about alignment, ownership, and making sure the right people trust and act on the data.Looking ahead, the next few years will not just be about better AI or faster analytics. It will be about making these systems actually usable and reliable in real environments. Less experimentation, more execution.What I liked most about Charlie’s perspective is that despite all the changes, the core excitement around analytics has not changed. The opportunity to turn data into something meaningful for the business is still huge.And we are just getting started.#data #qlik #ai #qlikconnect #theravitshow

Most teams do not lose because of lack of talent. They lose because of decisions. At Qlik Connect, last week, I had a great conversation with Andreas Hadelöv, Assistant General Manager and Lead Analyst at Malmö Redhawks on The Ravit Show, and what stood out was how disciplined their approach to data is. This is a team competing in Sweden’s top ice hockey league with one of the lowest budgets, so they cannot afford guesswork.Everything they do is grounded in data, but in a very practical way. They bring together data from wearables, game events, and training into a single view using Qlik, and use it to plan practices, give player feedback, prevent injuries, and shape game-day strategy. No overcomplication, just focusing on where data actually impacts performance.Where it gets even more interesting is recruitment. They rely on a few trusted metrics like ice time and performance to make hiring decisions. That clarity helps them stay competitive without overspending.Their approach to AI was also refreshing. They are testing it, but only using it where the data is highly reliable, mainly in scouting. Everything else can wait until the foundation is strong.The biggest takeaway for me was simple. Start small, stay focused, and align data with your strategy. That is what makes data actually useful.#data #qlik #ai #qlikconnect #theravitshow

What if the biggest shift in data security is happening where most teams aren’t even looking? I was at the RSA Conference at the NetApp booth, and had a great conversation with Gagan Gulati, SVP/GM of Data Services at NetApp, that really got me thinking. For years, we’ve focused on visibility like dashboards, alerts, and detection. But the real shift is moving from detecting risk to actually blocking it at the data I/O layer, right where data is accessed. In a world where AI systems are interacting with data at massive scale, this becomes critical. We spoke about how concepts like a Security Knowledge Graph can help govern not just human users but nonhuman identities by understanding relationships between data, systems, and access in real time without slowing things down. Another important point was around AI training. It is no longer just about protecting data, but about knowing if your data is even ready by scoring it early and preventing risks before they show up in model outputs. And with most enterprise data being unstructured, the storage layer itself is evolving into a place where context and control come together. This conversation made me realize that security is no longer just another layer in the stack, it is moving closer to the data itself.Are we ready to rethink where security should actually live?Explore NetApp's CyRes capabilities -- https://www.netapp.com/cyber-resilience/?utm_campaign=cross-cyre-multi-all-ww-digi-ravit_show_influencer_video_interview_at_rsac_2026-1775578050296&utm_source=linkedin&utm_medium=social&utm_content=video&utm_segment=1j_cyre#data #ai #security #storage #agents #api #netapp #theravitshow

“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

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

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