Data Access Request with Privacy and Security Validation

The problem

Data access requests from employees or AI agents are evaluated before provisioning. Sensitivity classification, business purpose documentation, need-to-know validation, and requestor identity checks run before access is granted. Sensitive data access always escalates for privacy review. All decisions log requestor, purpose, classification, and policy version.

Without deterministic enforcement, AI agents either block every edge case (adding manual overhead) or silently approve decisions that violate policy — with no audit trail to show auditors or regulators.

How Corules solves it

Corules sits between your AI agent and the action it wants to take. When the agent proposes a decision, Corules evaluates the full context against your compiled policy set in a single deterministic pass — no LLM, no ambiguity.

The result is a structured outcome: ESCALATE — data_classification = 'PII_SENSITIVE' requires privacy team approval regardless of role.

Policy example

Corules policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated deterministically at request time — no LLM, no variability.

// Data access policy (CEL)
context.data_classification in params.requestor_allowed_classifications[context.requestor_role]
  && context.business_purpose.size() >= params.min_purpose_length
  && context.data_scope <= params.max_records_per_request[context.requestor_role]

This expression is evaluated against the structured context your agent sends in the /v1/validate request.

Integration options

Corules integrates with the tools your teams already use. All integrations call the same REST API or MCP server — your policy logic stays in one place.

Frequently Asked Questions