JetStream Security

AI is ready. Trust isn’t. Today, we’re making something official. JetStream Security has raised $34M, led by @Redpoint Ventures with participation from the @CrowdStrike Falcon Fund and industry leaders, to solve a critical enterprise problem: how to trust the AI systems already operating in production. AI now calls APIs, accesses data, inherits permissions, and consumes budget. But when boards ask simple questions like where it is running, what it is doing, who owns it, and what it costs, many organizations cannot answer with confidence. The gap isn’t model performance. It’s governance. AI has moved from experimentation to infrastructure. It acts under credentials and makes decisions with real financial and operational impact. Without visibility and accountability at runtime, trust breaks down. When trust breaks down, adoption slows. We built JetStream around a simple belief: Governance enables trust. Trust unlocks advantage. Governance is the control layer that makes AI visible, attributable, and defensible in production. Our founding team—Raj Rajamani (CEO), Jared Phipps (COO), Jatheen Anand (CTO), and Venu Vissamsetty (Chief Architect)—has led product, engineering, and go-to-market at some of the most influential security companies of the last decade. We’ve built platforms that protect the world’s largest enterprises, scaled organizations through hypergrowth, and navigated the security challenges that emerge with every new computing paradigm. AI is ready for takeoff. Now it needs governance-grade trust. Read the release: bit.ly/46X2urt Learn more: bit.ly/4u7cTup

We’re hearing a different kind of question lately. Not “Should we use AI?” More like: “Where is it already making decisions?” “Who actually owns what it’s doing?” “How far does its authority reach?” AI isn’t sitting in pilots anymore. It’s moving through real workflows. And once it’s operational, clarity becomes more valuable than capability. Learn more: jetstream.security/?utm_source=tw… Chat with us at RSA: jetstream.security/event-rsa-2026… #aigovernance

Heading into RSAC, there’s a lot of optimism around AI. Demos will be slick. Roadmaps will be ambitious. Everyone will say adoption is accelerating. But here’s the uncomfortable question: Adoption is accelerating. Is confidence in how these systems behave keeping pace? That gap is becoming harder to ignore. It’s the conversation we’re having in San Francisco. AI is ready. Trust isn’t. Let's talk: jetstream.security/event-rsa-2026… #rsac2026 #aigovernance

JetStream was built around a simple reality we kept seeing in the field. AI adoption is already happening at scale. But accountability, visibility, and trust haven’t kept pace. As systems move from pilot to production, governance can’t live outside the system anymore. It has to operate at runtime across identity, behavior, and cost as AI moves through real workflows. In this clip, Raj shares how that belief shaped the way we structured the JetStream platform. Learn more: jetstream.security/?utm_medium=so… #ai #aigovernance #jetstream

AI scales decisions. But who owns the outcome? The biggest impact of AI adoption may not be technical. It may be organizational. As AI systems move deeper into enterprise operations, something subtle begins to change. Responsibility starts to spread. Decisions that once sat clearly with teams or individuals become embedded in automated workflows. Authority gets delegated through configurations, permissions, and integrations. Outcomes are still produced. But it becomes harder to trace who was accountable for them. This isn’t a technology problem. It’s an organizational one. AI is not just introducing new tools. It is reshaping how responsibility is distributed across the enterprise. And most governance models weren’t designed for that shift. Learn more: jetstream.security/platform/?utm_… #aigovernance #ai

AI adoption is accelerating fast. The harder question many teams are now facing isn’t whether the technology works. It’s whether they can clearly explain what their AI systems are doing as they scale. That’s a conversation we’re having with a lot of leaders right now. If you’re heading to RSA, let’s talk. We’ll be in San Francisco March 23–25. #rsac2026

Most companies don’t have an AI strategy problem. They have an AI accountability problem. It’s a pattern JetStream co-founder Jared Phipps has been seeing repeatedly in conversations with enterprise leaders navigating real AI deployments. The data reinforces it. Gartner reports that 69% of security leaders are already seeing unapproved generative AI use inside their organizations. At the same time, IBM research shows only about 1 in 5 organizations have mature AI governance in place. AI capability is moving faster than the ability to understand and control how it operates. Today, AI systems call APIs, inherit permissions, trigger workflows, and consume budget across SaaS environments. But basic questions are still surprisingly hard to answer: Who owns this system? What authority is it operating under? What is it actually costing? How do we know when behavior changes? This is why governance is becoming less about policy and more about operational visibility and accountability. AI is no longer experimental. It’s becoming infrastructure. And infrastructure requires a control layer. That realization is a big part of why we created JetStream. Learn more: jetstream.security/?utm_campaign=… #aigovernance #enterpriseai #agenticai

Surely organizations didn’t fall behind on purpose. They applied the same governance models that worked for traditional software. Systems were designed. Policies were documented. Approvals were granted. Then the system went into production. For a long time, that approach worked because software behaved predictably. AI systems operate differently. Once deployed, they continue interacting with APIs, data, and other systems across the environment. And they keep evolving as prompts, integrations, and permissions change. Which means something subtle but important has changed. Trust can’t be established once and assumed forever. It has to be built at runtime. Not just when the system is designed. But while it’s operating. That shift is why trust and governance haven’t kept pace with how quickly AI is being deployed. Learn more: bit.ly/3MUVy7p

AI isn’t failing. Most companies just can’t explain what their AI is doing. Over the past few days, as we’ve started talking more publicly about JetStream, one thing has stood out. The conversations with enterprise teams all sound remarkably similar. The technology works. But when leadership asks a few simple questions, the answers get murky. Where is this AI running? What data can it access? Who owns it? What is it costing the business? Those questions sound basic. But once AI connects to APIs, SaaS apps, models, and internal data sources, the answers aren’t always obvious. And when organizations can’t clearly explain what these systems are doing, trust starts to erode. Not because the technology failed. Because visibility and governance haven’t kept pace with how quickly AI is being deployed. AI capability is moving incredibly fast. Trust and accountability need to catch up. That gap is exactly what we built JetStream to solve. Visit us on the web: sbrew.link/fAzUHms0 #aigovernance #enterpriseai #cybersecurity #ai

Always fun when the conversation about AI governance includes Ferraris, Priuses, and Le Mans prototypes. There’s got to be a connection in there somewhere ;) Huge thanks to the team at TBPN for having Raj on the show to talk about what JetStream is building and why this moment in AI matters. The discussion touched on a few things we’re hearing constantly from enterprises. AI capability is moving incredibly fast. But the ability to understand, govern, and trust these systems hasn’t kept pace. That gap is where adoption slows. JetStream exists to close that gap, helping organizations bring visibility, accountability, and control to AI systems already operating inside their environments. Appreciate the time and the laughs. Watch the interview: youtube.com/watch?v=2CTpnO… Read the Fortune coverage: bit.ly/3NjDALP Learn more about JetStream: bit.ly/4boqhmw #aigovernance #enterpriseai #cybersecurity #jetstreamsecurity

JetStream Security @jetstream_sec Raises $34M in Seed Round #AI #AIGovernance #EnterpriseAI #AIVisibility #AIPlatform #AISecurity #SaaS #SeedRound #Funding thesaasnews.com/news/jetstream…

@jordan_segall thank you @jordan_segall !!

So excited to be working with this incredible team who have led some of the largest security companies in the world, working on the most pressing issue in cyber. Congrats @jetstream_sec !

@Redpoint @George_Kurtz @assaf_rappaport @fkerrest Thank you @Redpoint!

We're thrilled to be leading @jetstream_sec's $34M Seed alongside industry leaders including @George_Kurtz , @assaf_rappaport and @fkerrest.  JetStream is an AI Governance company helping enterprises safely adopt AI at scale. Enterprises are sitting on massive AI potential, but many remain stuck in pilot purgatory because they can't answer basic questions about model behavior, data lineage, and compliance. Jetstream is already helping some of the world's largest companies navigate this without governance becoming a roadblock. Led by Raj Rajamani, former CPO of CrowdStrike and SentinelOne, the founding team is comprised of veteran security operators from SentinelOne, Dazz, Cohesity, McAfee, and Attivo Networks. Together, they built an AI blueprint that synthesizes the interdependencies and touchpoints across systems, giving enterprises the visibility and control needed to move fast. It's rare to find a team that truly knows how to build iconic companies, at exactly the moment when enterprises most need help adopting AI safely. We couldn't be more excited for what's ahead. Congratulations to Raj Rajamani, Jared Phipps, Venu Vissamsetty, Jatheen Anand, and the entire Jetstream team!

Over the last decade, every major technology shift has followed the same pattern. It begins as experimentation. Lives in pockets. Feels optional. Then, quietly, it becomes embedded in the operating model. Cloud did this. SaaS did this. Identity did this. AI is doing it now. The difference is speed. AI systems are already participating in workflows that touch revenue, customer data, regulated information, and financial spend. They are not waiting to be formally categorized before influencing outcomes. And yet, in many enterprises, AI is still treated like an initiative. Something to pilot. Something to evaluate. Something to “roll out” later. But systems that act, consume budget, and inherit authority are not initiatives. They are operating components. Operating components require oversight by default, not by exception. Not because something has gone wrong. Because once something becomes embedded in the way work gets done, ambiguity becomes expensive. The real shift underway isn’t technical. It’s organizational. AI is moving from project to participant. From experiment to embedded. And until leadership treats it accordingly, adoption will continue to move faster than confidence. #aigovernance #enterpriseai #agenticsystems #enterprisetech

Speed without control creates friction. Friction within systems erodes trust. Enterprises have optimized for acceleration. Cloud deploys in minutes. APIs connect instantly. AI executes in milliseconds. On paper, everything moves faster. But speed alone doesn’t produce advantage. When authority is unclear, when oversight lags autonomy, when accountability requires reconstruction instead of visibility, friction appears. More reviews. More escalations. More hesitation under scrutiny. That’s not discipline. That’s confidence degrading in real time. AI doesn’t fail because it’s slow. It fails because trust thins under pressure. Once trust erodes, scale slows. Not because the system can’t run, but because leadership won’t let it. AI doesn’t need to move slower. It needs to move with control. Sustainable speed is a byproduct of trust — not the substitute for it. #aigovernance #enterpriseai #agenticsystems

Infrastructure is not measured by adoption. It is measured by control. Every material enterprise asset answers four questions: ・ Is it inventoried? ・ Is ownership defined? ・ Is usage attributable? ・ Is cost measurable? If AI cannot answer those questions, it is not governed. It is tolerated. That distinction matters. Because tolerated systems eventually collide with scale. The blocker isn’t intelligence. It’s operational readiness. Production systems require: Enumerability. Identity-bound ownership. Runtime visibility. Cost attribution. Without those primitives, AI remains unmanaged. And unmanaged infrastructure does not scale. #aigovernance #enterpriseai #enterprisetech #aicontrol

For years, AI was discussed as a capability. Now it operates as part of the enterprise system itself. When something begins executing actions, inheriting authority, and consuming budget automatically, the conversation changes. This is no longer about experimentation. It’s about operating discipline. Every mature technology eventually moves from novelty → necessity → infrastructure. AI has crossed that line. #aigovernance #enterpriseai #enterprisetech

As AI systems move into production, the governance discussion matures. Early-stage conversations focus on capability and performance. Production environments require something different: structured oversight. Systems that influence decisions, spend, and regulated data are expected to be observable, attributable, and auditable by design. AI now meets that threshold. The relevant question is not whether it functions. It is whether it can be accounted for. #aigovernance #enterpriseai

How would you reconstruct a material AI-driven action inside your enterprise today? Not the output. The action. Which system it touched. Which identity it operated under. Which data it accessed. Which workflow it triggered. Which budget it consumed. Most critical enterprise systems produce durable records. Finance generates ledgers. Identity systems log access. Infrastructure emits telemetry. These records make oversight — and accountability — possible. Yet 69% of security leaders report prohibited generative AI use inside their organizations. AI is already embedded across SaaS platforms and automation layers. It can initiate actions, transform data, influence decisions, and generate real-time cost. In many environments, the visible artifact is only the outcome — not the operational path behind it. That creates a structural gap. Not a model problem. An accountability problem. As AI activity becomes materially significant, the standard should be reconstructability. Anything that can affect revenue, cost, or customer outcomes must withstand scrutiny after the fact. That is an operating model decision. #aigovernance #enterpriseai #boardrisk