Policy - Data
Summary
A to B: Entitlement definitions and runtime policy decisions consumed by data enforcement points, including policy for read, transform, and reveal rules plus clearance tiers.
B to A: Retrieval coverage gaps, prompt-injection-induced bypasses, ungoverned access paths, abstraction tier compliance gaps, and seal-break events identified during Pillar B operations, surfaced back to Pillar A for policy correction, control-plane hardening, and tier model evolution.
Standards and Specifications
- OAuth 2.0 RAR
- OpenID Connect
- OPA/Rego
- OAuth 2.0 Token Exchange
- SPIFFE/SPIRE
This interface binds identity-aware authorization policy to every point where an AI system touches an enterprise's data -- both what it reads and what it is allowed to reveal. Retrieval and abstraction operations depend on Pillar A to expose a consistently evaluable authorization surface -- via tokens, APIs, or sidecars -- so that each query is filtered to the caller's effective access rights, and so that clearance tiers and redaction rules govern what a data operation may disclose in its output. In the opposite direction, retrieval logs, detected bypass patterns, abstraction tier-compliance gaps, and seal-break events must all flow back into Pillar A so that policy authors can close gaps such as over-broad scopes, misaligned group mappings, missing constraints on cross-tenant sources, or drifting tier models -- reinforcing Pillar A's role as the shared policy authority across the full data lifecycle, not just at the point of read. When this loop is implemented well, AI data access and disclosure become extensions of enterprise authorization rather than a parallel, less-governed channel.
Variants
IGA API call at request time
The retrieval enforcement point calls the IGA or identity governance platform synchronously on each retrieval to resolve the current entitlements and access scopes for the requesting identity, then constructs the query filter from that response.
Maximizes alignment between retrieval behavior and authoritative entitlements but introduces latency and dependency on IGA availability; requires stable, documented IGA APIs and shared identifiers between IGA accounts and retrieval principals.
JWT / token claims
The upstream IdP issues a token that includes roles, groups, and sensitivity clearances as claims; the retrieval layer parses the token and maps claims to filter clauses and allowed corpus sets for each request.
Reduces runtime coupling to IGA by relying on the IdP as policy distributor, but creates staleness risk if token lifetimes or claim-refresh logic are not tuned to entitlement lifecycle speed; requires agreement on claim schemas and audience across IdP, IGA, and retrieval systems.
Policy engine sidecar (OPA, Cerbos, Cedar)
The retrieval service calls a local or network-near policy engine with identity, resource, and environment attributes; the engine evaluates authorization rules and returns allow/deny plus filter parameters used to constrain the retrieval query.
Enables low-latency, portable authorization decisions across heterogeneous retrieval backends, provided that policy bundles are synchronized from Pillar A and resource attributes are modeled consistently; cache invalidation and bundle distribution pipelines become critical interoperability concerns.
Pre-computed metadata filter
At session establishment, Pillar A computes an entitlement-derived filter expression or set of corpus IDs for the identity and hands it to the retrieval client or gateway, which reuses this filter on subsequent retrieval calls.
Simplifies enforcement and reduces per-call overhead but risks over-permissioned access when entitlements change mid-session; requires explicit rules for session duration, mid-session refresh, and revocation handling to stay aligned with Pillar A governance.
SPIFFE SVID + mTLS
Retrieval clients authenticate using SPIFFE-issued identities over mutual TLS; the retrieval layer and attached policy engine treat the SPIFFE ID as the principal and evaluate authorization and scope rules for each retrieval based on that identity and associated attributes.
Supports secure, workload-centric identity and retrieval across multi-cloud and mesh environments, removing static secrets but requiring SPIFFE/SPIRE infrastructure and consistent SPIFFE ID mapping to enterprise identities and policies in Pillar A.
MCP Enterprise Authorization
AI clients call retrieval-capable MCP servers whose methods are fronted by enterprise authorization; OAuth-scoped tokens, claims, or sidecar policy checks ensure that each MCP tool invocation only exposes data consistent with Pillar A policy for the requesting identity.
Turns MCP into a governed retrieval surface rather than an open tool channel, but demands that MCP servers adopt a standard enterprise authorization pattern and propagate identity context and policy decisions to underlying retrieval systems.
IGA-managed role and clearance attributes
Clearance tiers and output entitlements are modeled as attributes and roles in the IGA system; abstraction services or attached policy engines query these attributes at output generation time to decide which fields, sections, or explanation depth to include.
Aligns output governance with enterprise access governance but requires IGA to support clearance as a first-class, lifecycle-managed attribute and for abstraction components to consume that data via standardized identity and attribute schemas.
Purview or catalog-driven sensitivity labels
Abstraction components inspect data lineage and Microsoft Purview or similar catalog labels to infer the sensitivity of input sources and then apply Pillar-A-defined rules for what each recipient clearance tier may see.
Tightly integrates with ecosystems that already label content, reducing duplication of classification logic, but depends on consistent labeling practices and cross-system understanding of label semantics between the catalog, policy engine, and abstraction layer.
Policy engine with identity and context-aware rules
An external policy engine evaluates rules that combine recipient identity, purpose, channel, and data classification to determine whether to redact, summarize, or fully reveal each portion of the AI output.
Offers high flexibility and portability across abstraction frameworks but requires a shared vocabulary for classifications, tiers, and purposes, as well as reliable propagation of identity and context signals into the policy decision point.
Structured output schema with field-level access control
The abstraction layer emits structured responses where each field is mapped to a required clearance tier; before serialization or rendering, Pillar C drops or redacts fields the caller is not entitled to see under Pillar A rules.
Simplifies enforcement and auditing for structured responses but is harder to apply to free-form text; requires schema governance so that new fields are consistently classified and enforcement behavior remains predictable across services.
Tiered explanation and rationale control
Abstraction logic applies Pillar-A policies to control how much reasoning, provenance detail, or model rationale is exposed at each clearance tier, limiting sensitive operational or security details to higher tiers.
Extends abstraction beyond data redaction into explanation control, reducing leakage of sensitive methods or internal topology; depends on standardized tier definitions shared between Pillar A policies and Pillar C implementation teams.
Participating Vendors
Okta
Okta serves as an enterprise IdP in Pillar A, issuing verifiable identity tokens consumed by Pillar B and C enforcement contexts. Okta's Cross-App Access (XAA) extension brings MCP server authorization under enterprise identity governance, enabling centralized policy enforcement for AI tool access in Pillar B.
Azure AI Search
Open Policy Agent
OPA (Open Policy Agent) is a CNCF policy-as-code engine serving as a core component of Pillar A's shared policy authority, called at runtime by Pillar B retrieval filters, Pillar C output orchestration, and Pillar D monitoring workflows. OPA evaluates identity context and policy rules to produce authorization decisions across AI pipelines, microservices, and API gateways.
Linked Evidence
No public evidence links have been attached directly to this interface yet.
Assertions
Open Policy Agent compiles authorization decisions into Weaviate query filters
Open Policy Agent can be used in a filtered-retrieval runbook where policy decisions are evaluated or compiled into constraints that an application maps onto Weaviate's native query filters before vector or hybrid search, helping enforce identity-aware retrieval boundaries.
Open Policy Agent compiles authorization decisions into Pinecone metadata filters
Open Policy Agent can be used in a filtered-retrieval runbook where policy decisions are evaluated or compiled into constraints that an application maps onto Pinecone's native metadata filters before search, helping enforce identity-aware retrieval boundaries.
Cerbos translates authorization policies into native Weaviate query filters
Cerbos documents that its query plan API can translate authorization policy into Weaviate-compatible query filters before retrieval, allowing Pillar A identity-aware authorization to constrain which objects and chunks Weaviate returns in a RAG workflow.
Cerbos translates authorization policies into native Pinecone query filters
Cerbos documents that its query plan API can translate authorization policy into Pinecone-compatible metadata filters before retrieval, allowing Pillar A identity-aware authorization to constrain which vectors and document chunks Pinecone returns in a RAG workflow.