JetStream Raises $34M to Solve Enterprise AI's Governance Crisis
Enterprise AI adoption has hit a governance wall. 93% of executives report challenges implementing AI governance and security guardrails, according to new research, creating a trust crisis that’s blocking the transition from pilot programs to production systems at scale.
JetStream, founded by CrowdStrike’s former Chief Product Officer Raj Rajamani and other cybersecurity veterans, has raised $34 million in seed funding to tackle what the company calls “the trust problem with AI.” The Redpoint Ventures-led round includes backing from CrowdStrike CEO George Kurtz, Wiz CEO Assaf Rappaport, and Okta co-founder Frederic Kerrest.
The Governance Accountability Gap
The primary barrier to scaling AI isn’t model performance or infrastructure limitations—it’s organizational discomfort with AI’s black box nature, Rajamani argues. Companies deploying autonomous agents often cannot answer basic operational questions: which data an agent accessed, what model produced a decision, who approved the workflow, or how much a deployment actually costs.
This visibility deficit becomes acute when CISOs and CIOs face compliance accountability for AI systems exhibiting non-deterministic behavior. Identical prompts producing different outputs reinforces skepticism among leaders responsible for operational risk management.
“Senior leaders, whether it’s a CISO or CIO, do not have enough visibility and controls to get a full, thorough handle of everything that is AI happening within their enterprise,” Rajamani told Information Security Media Group. “Companies have a trust problem with AI.”
Many enterprises believe they’ve standardized on a single AI provider like Microsoft Copilot or Google Gemini. In practice, “it’s a very, very fragmented landscape,” with shadow AI proliferation creating governance blind spots across environments.
Blueprint-Based Control Architecture
JetStream’s approach centers on what it calls AI Blueprints—structured representations mapping how AI systems operate in real time. Each Blueprint tracks relationships between agents, models, data sources, tools, and identities behind every action, whether human or machine.
The platform focuses on observed runtime behavior rather than static architecture diagrams, flagging activity that drifts from approved purposes. Blueprints define authorized operational parameters and enable real-time detection when autonomous systems stray outside intended boundaries.
“Not only are we giving you those 5,000 jigsaw pieces, we also put them together into 50 blueprints, which you look at and say, ‘Oh yeah, I totally understand what this is trying to do,’” Rajamani explained. “We also have enforcement capabilities which help you control MCP proliferation or key sprawl for MCP.”
The system addresses two of the most pressing enterprise concerns: Model Context Protocol (MCP) server sprawl and key management. Developers can download and install MCP servers locally, introducing supply chain risks and unvetted integrations. Related key sprawl occurs when credentials accidentally committed to repositories get exploited for unauthorized usage or economic abuse.
Enterprise Validation and Market Timing
JetStream is already working with Fortune 500 customers, with nearly 80% of existing enterprise clients driving expansion into AI governance capabilities. The company has grown to 40 employees since founding in 2025, hiring across engineering, product, and go-to-market roles.
The timing reflects broader enterprise pressure around AI oversight. While 80% of CEOs remain optimistic about AI returns, half acknowledge their roles could be at risk if those investments fall short. This tension creates demand for infrastructure that can accelerate AI adoption with auditable control frameworks.
“The number one issue I’m hearing from our customers is, ‘How do we control the proliferation and sprawl of MCP servers?’” Rajamani noted. The company has built a verified MCP catalog with deep security scans, addressing supply chain concerns around locally installed servers.
Infrastructure Consolidation Play
JetStream differentiates itself through unified governance rather than point solutions. Many vendors address narrow slices like MCP governance, shadow AI detection, or cost management in isolation. When governance is fragmented across consoles, attackers or insiders can exploit gaps between tools.
“Most large customers will not put three or five different solutions to solve for AI governance,” Rajamani said. “They will want one AI governance platform that covers visibility, design, control, financial operations.”
The platform includes financial accountability features, tracking AI workflow costs at the agent level. Many enterprises receive monthly invoices without detailed breakdowns of token usage or cost attribution across autonomous systems.
Looking Forward: Governance as Infrastructure Layer
JetStream’s $34 million seed round—which closed within weeks—signals strong investor appetite for tools bringing order to AI deployments. The company is positioning governance as a fundamental infrastructure layer, not an afterthought to AI development.
“There is also a huge question around providing a kill switch to customers in case something goes out of hand,” Rajamani noted, highlighting the need for emergency controls over autonomous systems.
The broader shift reflects AI adoption moving beyond model capability constraints toward trust, visibility, and accountability requirements. As organizations prepare to move AI projects from experimental programs into production environments, governance infrastructure becomes the critical enablement layer.
Over the next 12-18 months, expect governance platforms to become standard infrastructure for any serious AI deployment, with real-time visibility and control mechanisms separating successful enterprise AI programs from those stuck in perpetual pilot mode.
JetStream’s governance-first approach to AI infrastructure addresses a critical deployment bottleneck. For organizations looking to orchestrate AI agents across complex enterprise environments, Overclock provides complementary workflow automation and execution infrastructure to bridge the gap between AI strategy and operational reality.