Snowflake announced Cortex AI Gateway on Tuesday, a centralized control layer designed to govern how AI agents — including those built by competitors like Anthropic’s Claude Code and Cursor — access enterprise data, tools, and models. Alongside the gateway, the company unveiled a first wave of security integrations with 1Password, Aembit, Linx Security, SailPoint, and Saviynt, an unusual coalition of identity vendors who often compete with one another, now aligned around a shared trust model for autonomous agents.
The announcement, made from the company’s no-headquarters base in Bozeman, Montana, is Snowflake’s most aggressive move yet to position itself not merely as the place where enterprise data lives, but as the control plane that decides what AI agents are allowed to do with it.
«The next era of AI won’t be built through more walled gardens. It will be built through secure agent interoperability,» Mayank Upadhyay, Snowflake’s chief security and trust officer, told VentureBeat in an exclusive interview. «If every vendor builds a closed ecosystem of agents, enterprises simply recreate the fragmentation they’ve spent years trying to solve. Instead of breaking down silos, they create a new generation of AI silos that limit innovation and make it harder to scale AI across the business.»
Why decades-old enterprise security models break when AI agents become the actors
The core argument animating today’s announcement is that decades of enterprise security architecture rests on an assumption that no longer holds — that the actor behind every access request is a person.
«Traditional security was built for a world where humans were the actors. AI agents change that completely. For decades, security models assumed people would access one application at a time, operating at human speed and within relatively defined boundaries,» Upadhyay said. The deeper issue, he argued, is not novelty but exposure: «The challenge isn’t that AI creates entirely new security problems. It’s that AI exposes the blind spots we’ve always had.»
Organizations have never had perfect visibility into every API, dataset, and workflow, Upadhyay noted, and at human speed those gaps were manageable. Agents operating at machine speed can «combine access across systems and act on permissions that were never intended to be exercised together, amplifying those longstanding risks.» His conclusion: «In the agentic era, trust can’t be a one-time decision made at login. It has to be continuously verified through every agent, every action, and every interaction across the enterprise.»
Nancy Wang, chief technology officer of 1Password, described the failure mode in more visceral terms. When agents first arrived, she told VentureBeat, the default pattern was dangerously simple: «Let me just give the agent my credentials and it can just act as me… let’s imagine you’re the head of security or the head of IT, and you have access, especially admin access, to all of the systems. Well, now suddenly your agent now has admin access to all of the systems, and so it could exfil data… if it’s subject to a prompt injection, for example.»
The audit trail becomes equally useless, she added: «Imagine the audit logs show that Michael sent a couple million dollars to an offshore account… It raises eyebrows when, in fact, it could just be an agent going off the rails and doing things that you never authorized.» Her prescription, and the premise of 1Password’s integration with Snowflake, is blunt: «Agents need their own identity.»
Inside Cortex AI Gateway: how Snowflake plans to govern agent access and rein in runaway AI costs
Cortex AI Gateway, which will enter public preview soon, functions as a connective layer for what Snowflake calls «all trusted agent activity.» It governs both first-party agents built inside Snowflake, such as Snowflake CoWork and CoCo, and third-party agents built on external platforms. With support for more than 100 MCP servers — the Model Context Protocol connectors that have become the de facto standard for wiring agents to enterprise tools — the gateway centralizes access policies, authentication, permissions, and audit logging in a single place.
The gateway also addresses a less glamorous but increasingly urgent problem: runaway AI spending. It gives IT and finance teams a unified view of AI consumption, attributes costs to the specific teams, agents, or workloads driving them, and enforces spending limits before bills spiral.
Upadhyay described how those costs compound in practice. «AI is dynamic. Agents can invoke multiple models, call different tools, and execute multi-step workflows, creating consumption patterns that can change from one task to the next. For example, an enterprise may deploy an AI assistant to help employees answer internal questions. A simple request that only requires retrieving a document could unintentionally be routed through a more expensive reasoning model, trigger additional searches across multiple systems, or invoke unnecessary workflows.» At scale, with thousands of employees and hundreds of agents, small inefficiencies become significant line items.
The gateway builds directly on Snowflake’s May 2026 acquisition of Natoma, a 27-person startup whose centralized MCP gateway enforced identity, policy, and audit at the tool-call level. Forbes reported at the time that the deal — announced the same day as Snowflake’s $1.33 billion quarterly product revenue report and a $6 billion AWS compute commitment — was the smallest of the day’s three announcements by dollar value but the most revealing about where Snowflake believes the next platform fight sits: not in the data warehouse, but in the layer that decides what an agent may touch and records what it did.
Dual attribution and task-scoped access: the technical blueprint for trusting autonomous agents
The technical centerpiece of the partner integrations is what Snowflake calls dual attribution. «By logging both the verified non-human identity of the agent and the specific human who authorized the task, we ensure task-scoped access and complete auditability for every action taken across the enterprise,» Upadhyay said. That answers a question that has stumped security teams: when an agent takes an action, whose action is it? The Snowflake model says the answer is both — the agent’s, and the human’s who delegated the task — and both must be recorded.
Task-scoped access is the companion principle. Rather than inheriting a user’s full standing permissions, an agent gets access only to what a specific task requires. Upadhyay acknowledged the obvious objection — agents are dynamic and their next step often isn’t known in advance. «The goal isn’t to predict every action an agent will take. It’s to ensure that every action an agent takes is evaluated in real time against the appropriate policies, scope, contextual signals, and the original intent of the user,» he said.
Wang explained how 1Password’s piece works at the protocol level, pointing to emerging standards like OIDC-A: «the human, for example, first authorizes the agent to do a specific task, and then what that means is the agent will then receive sort of the delegated task specific token… as part of that token, that is where you learn of the original sort of delegator identity and also the intent behind the task.»
The intent-preservation problem is subtle, she noted, because enterprise tasks decompose into enormous chains of individual operations. «When they’re accessing a table, you know that it’s acting on behalf of the original intent that you gave that agent… a task might be a compilation of hundreds, maybe even thousands, individual actions.» Keeping that intent intact across every step in the chain — and flagging the moment an agent deviates from it — is what the coalition is ultimately trying to standardize.
SailPoint’s field report: the three ways enterprise identity systems fail against AI agents
Chandra Gnanasambandam, SailPoint’s EVP of product and chief technology officer, brought the perspective of a vendor that has watched enterprises break their identity stacks against this problem for more than a year. SailPoint has been in the machine and agent security market for roughly 18 months, he said, with more than 100 customers on its agent identity product — enough of a sample to catalog the recurring failures.
The first is scale-driven shallowness. An average Fortune 500 company has roughly 16,000 employees, and SailPoint is seeing human-to-non-human identity ratios of at least 10 to 1 — before counting the tools and APIs each agent touches, which multiply the count again. «You will get into a million plus non-human identities. Mapping the permissions that each of them get to the 16,000 humans is a completely non-trivial task,» he said. Most companies punt, mapping agents to humans at the directory-group level. «That is grossly insufficient. You want to have fine grain context. Like I said, it’s not access to Snowflake. It’s access to what column and what data inside Snowflake you need.»
The second failure mode is drift. Modern models are relentless goal-seekers, and that persistence cuts both ways. «When you tell them get this done, the underlying models are so powerful now. Even the weaker models are so powerful. They will go find a way to get it done… They will go find the vulnerabilities to bypass the permission to get it done,» Gnanasambandam warned. The answer, he argued, is runtime monitoring of the entire interaction chain, compared continuously against policy, with automatic intervention when an agent escalates beyond what its human delegator authorized.
The third is missing data context. Many vendors, he argued, announce splashy integrations with big application platforms while ignoring where the actual risk concentrates. «That’s not where the risk lies. Risk lies in sensitive data, so the details matter here… Can you map specific columns and rows in Databricks, Snowflake, Redshift, Oracle… into the agent context and the human context? And if you can’t do that, you are going to have gaps and holes.»
SailPoint’s answer required tearing out two decades of architecture. «We rewrote our underlying data and object model to treat AI identity as a first-class object, because for 20 years, SailPoint had a data model and object model that supported the human identity, and AI identities are fundamentally different,» Gnanasambandam said, describing 12 to 18 months of deep engineering work. The result is what he calls a unified lineage: «From human to master agent to sub agent to tool to application to data. That’s what I call the steel chain. That is in one data model, one platform.»
Why rival identity vendors joined Snowflake’s coalition — and what each side gets out of it
Perhaps the most striking aspect of today’s announcement is the roster. 1Password, SailPoint, Saviynt, Okta, and Aembit compete for overlapping identity and access budgets. Snowflake convinced them to build against a common trust framework anyway.
«The reason we brought together leaders across the security ecosystem is because no single company can solve the agent security challenge alone. AI agents can’t deliver real value if they only operate within the boundaries of one platform,» Upadhyay said. His broader thesis frames the whole strategy: «Nobody wants to replace data silos with AI silos.»
Wang offered a pragmatic division of labor: «We bring the trust, and Snowflake brings a system of record.» She framed the collaboration as classic defense in depth — «there are data level controls, and there are identity level controls, and so together we can create a much stronger ecosystem play.»
There is self-interest in the openness, of course. Snowflake sits atop an enormous concentration of sensitive enterprise data — more than 13,900 customers, by the company’s count — and every third-party agent that touches that data through a governed Snowflake gateway deepens the platform’s gravitational pull.
As Constellation Research analyst Michael Ni put it when the Natoma deal was announced, in comments reported by CIO.com: data platforms won the analytics era, and whoever governs agents, context, and autonomous actions wins the agentic one. A Forbes analysis of the same acquisition flagged the tension directly, noting that a governance layer living inside Snowflake risks pulling MCP’s openness back toward a single vendor’s control plane — attractive for Snowflake-standardized shops, more awkward for genuinely multi-vendor agent stacks.
Analyst forecasts show agent governance is now a trillion-dollar race against the clock
The urgency behind today’s announcement is not manufactured. Gartner predicts that by 2027, governance gaps discovered only after production incidents will force 40% of enterprises to demote or decommission autonomous AI agents — with analysts there warning that the greatest risk an agent poses often lies not in its output but in the actions it is empowered to take. IDC, meanwhile, expects more than 1 billion actively deployed AI agents by 2029, executing roughly 217 billion actions per day, and forecasts agentic AI will exceed $1.3 trillion in worldwide IT spending that year. The research firm’s analysts now argue agentic platforms should be treated as decision infrastructure, not productivity software.
Against that backdrop, the identity layer is becoming the contested ground, and every major vendor — Salesforce, ServiceNow, Microsoft, Google, Okta — is racing toward the same runtime-governance chokepoint. Snowflake’s differentiator is proximity to the data itself. As Upadhyay put it, security «can’t just be an API proxy sitting in front of an LLM. It has to anchor all the way down into the underlying data layer, enforcing zero-copy boundaries, dynamic data masking, and real-time exfiltration safeguards before an agent ever touches a row of data.»
The rollout now moves to proving ground. Cortex AI Gateway enters public preview soon, and the five partner integrations enter private preview, a phase Wang described as a deliberate feedback loop — customers on day one get an agent-access broker plus «a full audit log that will show you, for example, what that agent is actually doing,» even when an agent deviates from its intent. Gnanasambandam, characteristically, wants enterprises to skip the easy demos entirely, urging customers to bring loan-origination workflows spanning three clouds and ten applications, half of them mainframes: «Give us that complex use case and bring anyone on and do it in your context, and we will take the challenge with anyone in the world.»
That confidence — from a coalition of rivals, no less — captures what makes this moment unusual. The companies that spent the last decade fighting over who verifies human identity have concluded, more or less simultaneously, that the next decade belongs to whoever can verify the machines acting on our behalf. Upadhyay distilled the wager into a single line: «The future of AI won’t be won by the organizations with the most agents, but by the organizations that can govern those agents with the most trust, visibility, and control.» In the agentic enterprise, it turns out, trust isn’t the guardrail. It’s the product.
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