Runtime enforcement for AI that can act.
Not just logs.
Probabilistic models inside deterministic platforms create a new failure class: confidently wrong behavior at scale. RegulatedRuntime is the Runtime Control Plane and Policy Enforcement Point (PEP) that keeps execution bounded, evidence-complete, replayable, and incident-ready.
What it does: Enforces decision-time authorization, provenance gates, and idempotent execution, producing deterministic evidence that can be reconstructed and replayed.
Why it matters: In regulated systems, logs describe outcomes, not causality. Auditors and incident responders need proof of what was allowed, under which policy, with which inputs, and what executed.
Technical Resources
V1.0.4 SPECIFICATIONSThe Determinism Gap
Why probabilistic agents fail production audits in deterministic, regulated environments — and why runtime control must sit in the execution path.
The “Confidently Wrong” Trap
Uptime dashboards stay green while behavior drifts. Standard logs capture outputs, not the state that created them. We bind policy, identity, and retrieval state at the decision moment.
Retrieval Is a Security Boundary
RAG can ingest stale, poisoned, or unauthorized content. We enforce provenance, allowlists, and staleness budgets before inference and before actions.
Tool Calls Turn Mistakes Into Incidents
Retries duplicate side effects (double billing, duplicate tickets, repeated emails). We enforce idempotency keys, rate limits, blast-radius caps, and a global kill switch.
The AI Control Plane
Governance is not a document. It is a runtime capability. RegulatedRuntime sits in the execution path to bind policy, identity, retrieval, and tool intent into a single immutable decision record.
Every sensitive read and every tool call is a privilege boundary. We evaluate who is calling, what operation, on which scope, under which policy version, with which risk state.
We validate inputs, enforce operation-scoped permissions (not “whole tool allowed”), rate-limit actions, and require approvals for high-risk operations.
Provenance-tagged chunks, allowlisted sources per task, freshness checks, and redaction before prompt assembly. If evidence is missing, the system downgrades to “explain-only”.
When injection signals spike or anomalies rise, globally degrade agents to read-only or approval-required without redeploying application code.
> Policy: EU_DATA_RESIDENCY_V4.1
> Obligation: PII_MASK_AT_INGRESS
> Capability: jira.create_ticket (scoped)
> Idempotency Key: req_99a_safe
Integration Specs
Designed as a substrate that does not replace your models, governance tooling, or identity stack. It adds decision-time control points so regulated execution is auditable, bounded, and incident-ready.
Deployment Model
Sidecar / middleware in front of model calls, retrieval, and tools. Works for agent frameworks and custom orchestrators.
• Integrates with established data, identity, and execution planes already present in large enterprises
• Operates across heterogeneous inference runtimes and orchestration models without architectural coupling
• Leverages existing authorization, audit, and observability investments rather than duplicating them
• Provides a consistent enforcement and evidence layer across tools, workflows, and regulated operations
Authorization Semantics
Policy answers “allowed?”; execution answers “safe to run?”. We implement runtime checks in the order that matters.
2) Delegation: can it run on behalf of this user / tenant / environment?
3) Capability scope: which operations exactly (create vs delete)?
4) Data scope: row/column filters, classification, purpose-limits.
5) Risk gates: downgrade to read-only, require approval, or block.
Safe Execution Patterns (Non-Negotiables)
The Evidence Artifact
A reconstructible ledger of what happened at execution time: which policy version was active, what was retrieved, what was redacted, what was attempted, what was allowed, and what was blocked.
The Challenge
An EU regulator asks for proof that German customer data was processed on EU-based nodes and that PII was masked before entering inference. Standard logs miss the “pre-process” state and policy version binding.
Runtime Guarantees
- Policy binding, including version and obligations
- Geofencing verification
- Pre-inference PII masking proof
- Provenance + staleness checks for retrieval
The Challenge
A customer disputes a decision from months ago. The model, prompts, and retrieval corpus have changed. You must prove what the system saw then, under which policy, and which tool operations were executed.
Runtime Guarantees
- Policy version binding + obligations applied
- RAG snapshot (chunk IDs + provenance)
- Deterministic replay inputs (hashes)
- Tool call chain with idempotency keys
What You Actually Get
If you are accountable for regulated AI, you need three things that most stacks don’t provide:
Operating Model
Production AI isn’t a diagram. It’s a living runtime. These are the few SLOs and runbooks that make it manageable.
SLOs That Matter
Not “model accuracy.” Real survivability metrics:
Policy coverage: % of sensitive reads/writes gated by PEP with recorded decision.
Time to contain: how fast can we disable tools / downgrade runtime?
Drift detection time: detect behavior drift before users do.
Runbooks You Must Have
When bad days happen, response must be boring and predictable:
Retrieval poisoning: restrict sources. Tighten freshness. Require citations.
Retry storm: enforce idempotency. Circuit-break writes. Rate-limit tools.
Tool instability: fail closed for writes. Degrade to explain-only.
Strategic Rationale
This is a missing runtime layer that mature teams repeatedly rebuild (inconsistently) inside each agent workflow. When enforcement becomes a platform primitive, it compounds across every product surface: retrieval, tools, and regulated execution.
Once this layer exists, regulated execution becomes a property of the platform, not a burden on individual teams.