Comparison
Both address policy-based authorization for AI agents and MCP tool access. The real decision is architectural: how deeply authorization is enforced along the transaction and data path.
Both vendors govern whether an agent may invoke a tool. PlainID also controls which data is retrieved and what is ultimately exposed.
Full coverage: Whole stack
Humans, NHIs, and agents across applications, APIs, service-to-service, AI/MCP, and data platforms.
Full coverage: Broad
Humans, applications, and agents, with MCP and SaaS/data workloads.
Full coverage: Discovery to response
Static and dynamic MCP discovery, tools registry, tool list filtering, parameter and payload restrictions, payload modification, and masking of returned responses.
Partial coverage: Gateway or framework enforcement
MCP server discovery, invocation checks, parameter validation, and behavioral monitoring.
Full coverage: Composite chain
Evaluates the initiating human plus the executing agent, purpose, action, resource, and runtime context, and bounds delegated authority.
Full coverage: IBAC
Explicit intent-based positioning with delegation patterns.
Full coverage: Registry and graph
Static and dynamic agent discovery, agent registry, profile enrichment, access graph, and agent-to-tool relationships feeding policy construction.
Full coverage: Shadow-agent discovery
Shadow-agent and workload discovery, access graph, classification, and permission visibility.
Full coverage: Before the query runs
Policy-derived data filters, SQL/RAG query transformation, and row and column restrictions for application- and agent-initiated access.
Gap / limitation: Not supported
No pre-retrieval data filtering described.
Full coverage: Query and response layer
Column-level masking at the data layer, plus API, AI, and MCP gateway Authorizers that filter, mask, or redact sensitive fields before delivery.
Gap / limitation: Not supported
No data masking or response protection described.
Full coverage: Snowflake, Databricks, BigQuery, Power BI
Governs policies centrally and orchestrates native row-filtering and masking, protecting direct SQL access and access mediated by apps or agents.
Gap / limitation: Not supported
No native data-platform policy orchestration described.
Full coverage: One decision, many signals
Human, NHI, workload, and AI agent in one decision, with OBO chains. The PIP enriches from IdPs, enterprise data, classification, and risk sources.
Gap / limitation: Not supported
No composite identity or external-context enrichment described.
Full coverage: Ready to use
Enforcement and policy-orchestration patterns for external and internal policy engines across gateways, apps, microservices, agents, and data.
Gap / limitation: Not supported
No prebuilt Authorizer model described.
Full coverage: Full lifecycle
Build, test, simulate, approve, promote, and monitor, with management APIs, ownership, audit, and rollback playbooks across environments.
Full coverage: Included
Simulation, approvals, version history, certification, promotion, and governance.
Full coverage: Central control, distributed runtime
SaaS administration is separated from distributed or hybrid PDP and Authorizers near protected transactions. The management plane stays off the runtime path.
Partial coverage: VPC, hybrid, on-prem
Publishes VPC, hybrid, and on-prem options through a unified policy and Trust Gateway offering.
Full coverage: Production-proven
Trusted by Fortune 500 organizations in production, with documented latency, throughput, and resilience benchmarks.
Gap / limitation: Not verified
Scale and production maturity could not be verified from public information.
The right choice depends on whether you need to govern the action, or the action and the data behind it.
Intent and behavior-based protection
Runtime authorization for agents and data
A regional support rep asks an approved agent to investigate a complaint. They may see customer records only in their assigned region, and payment and personal fields must stay hidden. Here is where PlainID acts.
Confirm the human–agent relationship, task purpose, and regional scope.
Show only permitted tools, then re-check the selected CRM tool operation at call time.
Limit the region or customer account identifiers before the request proceeds, adapting the payload where needed.
Apply user- and agent-aware row filters, column restrictions, and masking through SQL/RAG transformation or native Snowflake and Databricks policies.
Independently inspect the response and remove, filter, mask, or redact restricted fields before it reaches the agent or user.
Record identities, action, policy decision, and restrictions for investigation.
50%+
Of successful cybersecurity attacks against AI agents will exploit access control issues through 2029 — Gartner, "How to Secure Custom-Built AI Agents," 11 June 2025
2B+
Authorization decisions processed annually, built for high-scale, real-time enforcement across complex environments
F500
Fortune 500 enterprises trust PlainID to secure millions of identities, recognized by Gartner and KuppingerCole in authorization and security platforms
Authorization that governs not only whether an action proceeds, but precisely which data is retrieved and exposed.
Policies, ownership, testing, approval, and promotion are managed centrally, while decisions run close to applications, gateways, microservices, agent frameworks, and data systems. The SaaS management plane is not in every runtime transaction.
Humans, partners, customers, workloads, and AI agents share one model. The PIP enriches each decision with identity, business, classification, ownership, location, and risk signals at request time.
Authorizers connect central policy to API/AI/MCP gateways, agent frameworks, apps, service-mesh sidecars, and data systems, and can orchestrate native engines such as Snowflake and Databricks rather than replacing them.
Administration is decoupled from the production decision path, supporting local or distributed decisioning for low-latency, high-volume enterprise transactions, trusted today by Fortune 500 organizations.
Reva's capabilities are vendor claims from public descriptions. Confirm feature availability and integration coverage for each target technology.
Check that restrictions are actually enforced on the data, not only that the call is permitted or denied.
Verify that sensitive fields in the response are masked, removed, or redacted before they reach the agent or user.
Fine-grained outcomes such as constrained tool lists, parameter restrictions, query filters, and masks depend on the integration. Validate each one you plan to use.
Comparison
Standards-based ABAC vs. runtime authorization for agents and data.
Comparison
Identity governance vs. runtime authorization, compared.
Platform
See the full platform overview.
They overlap. Both address modern policy-based authorization, including AI agents and MCP tool access. Reva emphasizes adaptive intent and behavioral protection. PlainID is a mature, enterprise-wide authorization layer that also governs the data an agent can retrieve and expose.
PlainID applies pre-retrieval filters, SQL/RAG query transformation, row and column restrictions, and response masking, and orchestrates native policies in Snowflake, Databricks, and BigQuery. Reva describes permission-aware retrieval and data authorization publicly, but equivalent pre-retrieval transformation and native policy orchestration should be proven for each proposed integration.
Yes. Depending on the Authorizer and integration, PlainID can produce constrained tool lists, permitted operations, parameter restrictions, enriched tokens, query filters, masks, redactions, and approval obligations. Confirm which outcomes are supported for each target technology.
It compares documented capabilities and public vendor descriptions, not independently verified product equivalence. Reva descriptions are vendor claims. Integration coverage and feature availability should be confirmed in a proof of concept.
30 minutes, your environment, no generic slide deck.
