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EnforceAuth
Authorization Context Intelligence

You have policies.
But can you trust the context those policies use?

EnforceAuth makes the context your authorization decisions depend on visible, trustworthy, and governable.

Good policy + bad or missing context = a bad authorization decision.

Identity and policy engines still matter. The residual risk is whether the context those controls use is complete, correct, current, and governed.

AI agents made this impossible to ignore. The same gap already sits in every app, API, and policy engine you run.

Operational authorization, open to every team — built on the OPA runtime your fleet already operates.

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mock mcp exampleCompliance

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Speaks to the agents you already use

ClaudeCursorVS CodeChatGPTGeminiMistral Le ChatClineContinueMicrosoft Copilot StudioZedGitHub Copilot CLI
Assess · Govern · Evaluate · Defend

From context to decisions — the EnforceAuth suite

Zift, Writ, Herald, and Verdict each see, fix, or prove authorization context. Customer-deployed OPA remains the decision engine at the edge. EnforceAuth answers: can you trust the context that policy is using?

console.enforceauth.com/dashboard
EnforceAuth dashboard — real-time overview of active policies and decision volumeEnforceAuth decision log — real-time policy evaluation across all entitiesEnforceAuth decision detail — full input, results, and policy path for a single evaluationEnforceAuth deployments — policy deployment history across all entities
Dashboard

The Authorization Platform Flow

01

Assess

Zift Migration Tool

Open source

Sift embedded authz. Extract to PaC. Rego or Cedar. Find where authorization context and logic are scattered.

02

Govern

Writ

Author Rego. Ship bundles. Govern entities, drift, and audit through 100+ MCP tools. Govern the context and policy your decisions depend on.

03

Evaluate

Herald MCP

Bring your AI agents. Cloud or air-gapped. Compose Writ data with your own for compliance, audit, risk. Put trustworthy context in reach of agents and operators.

04

Defend

Verdict Agentic Firewall

Coming soon

Human approvals for AI-driven actions. Bound actions when context or risk requires a human gate.

Decision engine

Open Policy Agent (OPA)

Deployed at the edge.

The differentiator

Your AI agent operates the entire platform.

Most authorization vendors stop at "an MCP server can call our PDP." EnforceAuth ships the inverse: the platform is itself an MCP server. Your AI agent authors Rego, deploys bundles, queries decision logs, and renders the result as charts, tables, or code. All through 100+ scoped, RBAC'd, audit-logged tools. Same surface as the UI, fewer clicks.

mock mcp exampleCompliance

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Wire-transfer denials · last 2 hours
preparing visualization

Examples shown for demonstration purposes only. Actual agentic functionality depends on your local agent's capabilities and the MCP tools you have configured.

Three on-ramps

Where to start.

Recommended

Scan your code

Find where authorization context and logic are scattered in your existing app. zift is open source; install in 30 seconds.

Try zift
In-market

Migrating from Styra

Same OPA. Same Rego. Keep the policy engine. OPA stays open under CNCF, so the runtime is unchanged. The question we add: can you trust the context those policies use?

Migration guide
On this page

See the agent examples

Watch an agent author Rego, deploy bundles, and read decision logs. Connect the agents you already use through MCP.

See the examples

Common questions

What buyers ask first

An auditor will eventually ask why an agent was allowed to act. A login line is a thin answer. Read what AI agent authorization evidence should contain.

Authorization Context Intelligence is the work of checking the facts a policy uses, not just the policy text. Identity can be right, and the rule can be right, and the decision can still be wrong if ownership, classification, or delegation was missing or out of date. That is the problem EnforceAuth is built for. It shows up first with AI agent authorization, and it was already there for apps, APIs, and human access.

AI agent authorization evidence is a record of one access decision that someone else can check later. A useful record says who or what asked, on whose behalf, what data or tool was involved, which policy answered, and why. A login record only has the first of those. Evidence IQ is designed to keep the five in one place. It is not available to pilots until the end of Q4 2026.

EnforceAuth is the Authorization Context Intelligence company. We make the context your authorization decisions depend on visible, trustworthy, and governable. It works with the policy engines and identities you already run.

No. Policy engines decide allow or deny. Customer-deployed OPA runtimes remain the decision engines. EnforceAuth helps you trust — and evidence — the context those policies use, and governs authoring, bundles, entities, and agent tooling around them.

Most organizations have identity and policy. They still cannot answer whether the context those policies use is complete, correct, current, and governed. That gap is what Authorization Context Intelligence exists to close.

No. AI agents are where the context problem becomes obvious. The same discipline applies to applications, APIs, Kubernetes, and human access. We started from authorization context, not from an AI-only product.

No. Policy engines decide allow or deny. IAM and CIEM tell you what access exists. We make the context those decisions depend on trustworthy — and the “why allowed” explainable.

Trust the context your policies use.

From scattered if (...) checks to decisions you can trust and evidence. Customer-deployed OPA remains the decision engine. Same Rego, same audit trail.

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