Continuous Adaptive Authorization for Humans and AI
The first Authorization Management Platform (AMP) that enforces, governs, and adapts access across AI agents, cloud-native apps, and data.
Measurable Impact with Reva.AI
faster application development with AI-powered policy design
reduction in standing privileges with Access Clipping
fewer items to certify through Policy-Based Access Reviews
A New Era of Security for AI-First and Cloud-Native Enterprises
AI-driven and cloud-native environments have outgrown traditional access control. Fragmented policies, static roles, and inconsistent enforcement create blind spots and risk. Reva provides a unified control plane for authorization across AI, applications, and infrastructure — integrating policy design, governance, enforcement and observability.
Strategic Control. Architectural Freedom. Developer Velocity.
Why Reva Is Fundamentally Different?
Every Workload.
Consistent authorization, governance, and control - tailored to each architecture.
Reva for Agentic AI
Adaptive, runtime authorization for AI agents - controlling what they can access, invoke, and execute, with guardrails and human-in-the-loop enforcement
Reva for Cloud-Native Applications
Static roles can’t secure dynamic systems. Centralize and enforce policy-driven runtime decisions across cloud-native applications.
Reva for SaaS & Data Platforms
Unify and govern fragmented SaaS and data authorization models with automated policy discovery, access graphs, and continuous policy certification
Reva for Cloud-Native Authorization
Unify authorization across APIs, Kubernetes, microservices, and cloud IAM.Enforce runtime, policy-driven access from a single control plane.
Built on Open Standards, Designed for Zero Lock-In
- Adopt open standards like Cedar, OPA and AuthZEN to keep policies portable and avoid proprietary authorization dependencies
- Native support for Shared Signals / CAEP to ingest real-time risk signals as decision context for adaptive authorization
- Enterprise-grade performance with P90 sub-10ms latency, 99.9% availability, and 50–80% faster decisions than XACML-based systems

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