Palo Alto Networks has released Prisma AIRS AI Gateway, an enterprise control platform that manages AI security and governance across the organization.
With MCP activity surging from 11% to 41.4% and monthly AI transaction volume growing twelve-fold in six months, enterprises face unprecedented risks as sensitive data flows to AI models without oversight.
The gateway sits between AI interactions and backend systems to enforce policies, track costs by team, verify agent identities, and inspect prompts and responses inline—processing 68 Trillion+ tokens monthly at sub-millisecond latency with 99.999% availability.
What happened
Palo Alto Networks announced general availability of Prisma AIRS AI Gateway, a control plane built six weeks after closing the Portkey acquisition. The gateway sits between every AI interaction and backend systems to enforce security, governance, and identity controls across coding assistants, enterprise agents, and copilots.
Why it matters
Enterprise AI adoption is outpacing security infrastructure. MCP activity jumped from 11% late last year to 41.4% by mid-2026, and monthly AI transaction volume grew twelve-fold in six months—some sessions moved hundreds of megabytes of data outbound. Without centralized control, coding agents expose source code and credentials, enterprise agents overstep their scope risking data breaches, and copilots surface data employees should not see. The gateway lets organizations see AI usage, enforce policies, and control agents in real time instead of blocking traffic entirely or staying permissive.
What to watch
The platform processes 68 Trillion+ tokens monthly, delivers sub-millisecond routing latency with inline inspection, and maintains 99.999% availability. Teams can now enforce budgets, verify agent identities with just-in-time least-privilege access, and inspect prompts and responses inline to prevent data leaks—all from a single unified view.
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Palo Alto Networks has positioned the Prisma AIRS AI Gateway as foundational infrastructure for the enterprise shift from software-driven organizations to AI-orchestrated ones. The timing underscores genuine urgency: MCP (Model Context Protocol) activity surged from 11% late last year to 41.4% by mid-2026, and monthly AI transaction volume grew twelve-fold over the same six months—metrics that reveal both rapid adoption and lack of visibility. The company's decision to bring innovations from the Portkey acquisition to market within six weeks of closing suggests confidence that the problem is both acute and addressable at the infrastructure layer rather than at the application level.
The architecture addresses three distinct failure modes in current enterprise AI adoption. Coding assistants expose source code and secrets when context is sent to frontier models; enterprise agents with broad standing privileges and shared keys create accountability gaps; copilots embedded in SaaS tools surface data to employees outside their scope. Traditional point solutions—text filters or single-API routers—cannot handle the scale: hundreds of megabytes of data moving outbound in individual sessions. By sitting inline as a unified gateway for LLMs, Model Context Protocol tools, and agent-to-agent interactions, the platform enables real-time inspection and enforcement without requiring teams to abandon their existing tools.
The scale metrics (68 Trillion+ tokens monthly, sub-millisecond latency, 99.999% availability) are designed to address the operational risk that brought many enterprises to either blanket blocking or permissive approaches. Neither works—blocking stalls AI velocity, permissiveness defers governance to a later date that never comes. The gateway's unified observability (tracking usage, users, projects, token counts, latency, and cost in a single view) is pitched as the mechanism to retire shadow AI infrastructure immediately, while central policy enforcement at the infrastructure layer moves security ownership away from individual developers to the platform team.
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