AI Engineering Group Deterministic AI Infrastructure for Enterprises

Streaming Compliance

Streaming Compliance is a continuous monitoring and policy engine that observes AI systems in real time, evaluates behavior against compliance rules, and produces an auditable evidence trail.

Problem definition

Enterprises need to prove how AI systems behave over time, not just how they perform in offline tests. Traditional logging is insufficient: it lacks structure, policy context, and real-time controls.

  • AI behavior must be observable across prompts, tools, and downstream actions.
  • Compliance teams need versioned policies that can be tested and rolled out safely.
  • Evidence must be queryable and explainable for audits, incidents, and regulators.

Non-goals

  • Streaming Compliance does not replace core monitoring (CPU, memory, generic logs).
  • It does not make legal determinations; it provides structured evidence and policy evaluations.
  • It is not a general-purpose data lake; it focuses on AI behavior events.

System components

Event ingestion

Receives structured events from AI systems: prompts, context, model decisions, tool calls, and outputs.

  • Supports streaming transport (e.g., queues, streams) and batch ingestion.
  • Normalizes events into a common schema with correlation IDs and environment tags.

Policy engine

Evaluates events against versioned compliance policies and emits decisions in near real time.

  • Rules reference event fields and contextual metadata.
  • Decisions can block, allow, flag, or escalate behavior.
  • All evaluations are logged for audit.

Evidence store

An immutable, queryable log of events, policy evaluations, and outcomes linked to AI systems.

  • Stores normalized events and policy results.
  • Supports queries by user, system, policy, and time window.
  • Backed by retention and access policies defined on the compliance page.

Interfaces

The following interfaces are conceptual; Cursor agents should use them as a reference when generating concrete APIs, schemas, or integration code.

  • Event schema: shape of AI behavior events emitted by systems.
  • Policy definition: structure of rules evaluated against events.
  • Decision output: fields produced by the policy engine.

Example shapes (conceptual)

  • Event: { id, system_id, environment, timestamp, inputs, outputs, tools_used }
  • Policy: { id, version, conditions, actions, severity }
  • Decision: { event_id, policy_id, outcome, reason, created_at }