Continuous Observability + Continuous Assurance. The Trust Layer for Enterprise AI.
Your AI is now a model risk problem.
Continuous Observability + Continuous Assurance. The Trust Layer for Enterprise AI.
Every model call, every agent invocation, every prompt revision is a model-risk event. Your existing MRM framework wasn't built for AI velocity. Reign extends MRM to the agentic era.
The MRM problem
Annual cycles. Weekly model changes. Daily agent invocations.
Traditional MRM was designed for models that changed quarterly and were independently validated annually. AI changes the cadence. Foundation models update weekly. Agents invoke tools thousands of times a day. Prompts evolve continuously. The CRO carries the risk — but the framework wasn't built for the velocity.
The cycle mismatch.Annual validation can't keep pace with weekly model churn. Without continuous monitoring, the CRO is signing off on a snapshot — not the system in production.
Agentic AI breaks the model.Tool calls, MCP servers, and chained agents create surface area that traditional MRM frameworks don't cover. Effective challenge needs telemetry that traditional model platforms don't produce.
Concentration risk is invisible.Most enterprises can't answer simple questions: which models are in production, who validated them, what providers are we depending on, what's our exposure if a provider degrades or fails?
Examination scope is expanding. SR 11-7 was always implicit for AI. Now examiners are explicit. OSFI E-23, the EU AI Act, and DORA are all in scope at once — and the audit committee is asking whether the framework holds.
MRM frameworks, mapped to Reign
Every framework citation, every component, every artifact.
Reign's four-component spine maps directly to the MRM frameworks regulators expect. Each row below is a real risk question — and the Reign component that delivers the evidence.
| Framework / Citation | Risk Question | Reign Component | Evidence Reign Delivers |
|---|---|---|---|
| SR 11-7 — Federal Reserve Model Risk Management Guidance | Are we managing AI models with the rigor SR 11-7 expects? | Model Risk Validation | Approved-model registry with effective challenge tracking. Independent validation evidence. Documented model assumptions and limitations. Tied to the model owner, validator, and risk classification — every change logged with reviewer attribution. |
| OSFI E-23 — Enterprise-Wide Model Risk Management | Is enterprise-wide model risk in scope and continuously assessed? | Model Risk Validation | Enterprise model inventory with risk-tiering and change control. Continuous validation harness. Material model changes flagged for second-line review before deployment. |
| DORA — Digital Operational Resilience Act | Are AI-dependent operations resilient and observable end-to-end? | AI Gateway + Audit Ledger (CAVR) | Every AI call gated, logged, and identity-bound. Continuous audit chain across LLM, agent, and tool layers. ICT-third-party concentration risk visible at the gateway. Incident classification feeds resilience reporting. |
| NIST AI RMF — Govern · Map · Measure · Manage | Do we have a structured AI risk program mapped to NIST functions? | Four-Component Spine | Govern: AI Gateway policy enforcement. Map: Model Risk Validation inventory and risk classification. Measure: Audit Ledger (CAVR) continuous monitoring. Manage: Assurance Packs and incident response evidence. |
| SR 11-7 §V — Ongoing Monitoring | Are deployed models continuously monitored for drift and decay? | Model Risk Validation drift detection | Statistical drift monitors on every approved model. Performance benchmarks tracked against validation baseline. Alert thresholds tied to materiality. Out-of-tolerance events trigger second-line review. |
The four pillars of AI MRM
Governance · Validation · Monitoring · Change Control.
The four pillars of model risk management — adapted for AI velocity. Reign is built so each pillar is enforced by design, not asserted on a slide.
Governance
Who owns each model — and who challenges it?
Model Risk Validation binds every model to a documented owner, validator, and risk tier. Effective challenge is enforced — the same person cannot both develop and approve a model change. Roles, approvals, and exceptions are part of the audit chain.
Validation
Has every approved model been independently validated?
Independent validation evidence is attached to every approved model. Validation harnesses run on demand and on schedule. Re-validation triggers fire on material change. Validators have read-only evidence access without depending on model owners.
Monitoring
Are we detecting drift, decay, and material change in time?
Continuous monitoring at the Gateway and Model Risk Validation layer. Drift detection with materiality thresholds. Performance, fairness, and stability benchmarks. Alerts route to the model owner, validator, and second line when thresholds are crossed.
Change Control
Is every model change tracked, reviewed, and reversible?
Cryptographically versioned change packets. PCCP-aligned predetermined change control. Material changes are flagged for second-line review before deployment. Every change is logged, attributed, and reversible.
For your role on the risk chain
Built for the four hands that touch AI risk.
Chief Risk Officer (CRO)
An enterprise-wide picture of AI model risk that you can defend to the board, the regulator, and the audit committee — without depending on engineering for every data point.
MRM Director
Your existing MRM playbook — extended for AI velocity. Reign delivers SR 11-7 / E-23-aligned evidence on every approved model, every change, every drift event.
Model Risk Validation Lead
Independent challenge with read-only evidence access. Validation harnesses, drift telemetry, and change packets in the formats your team already uses for traditional models.
Internal Audit
Population-level AI evidence for the third line. Test SR 11-7 controls without sampling. Walkthrough-ready packets for examination prep and quarterly Risk Committee reviews.
Mapped to your risk cadence
Continuous evidence, on every risk timeline.
Quarterly Risk Committee
Live MRM dashboard from Audit Ledger (CAVR)Real-time view of approved-model inventory, drift events, validation status, material model changes, and exception activity — pulled directly from the audit chain. No manual prep window.
Annual MRM Cycle
Independent validation packetsValidation evidence assembled per SR 11-7 / E-23. Performance benchmarks, ongoing-monitoring summaries, change-control records — all framework-mapped and submission-ready.
Examination Prep
Submission-ready Assurance PacksVerifiable, framework-mapped artifacts in the formats examiners expect. SR 11-7, OSFI E-23, DORA, EU AI Act, NIST AI RMF — assembled on demand for examination request lists.
Real-Time Risk Alerts
AI Gateway escalation triggersWhen drift, exception, or material change crosses a threshold, the AI Gateway alerts the model owner, validator, MRM director, and CRO — with full context and the policy that fired.
Explore the four components
The four components that make AI risk manageable.
AI Gateway
Policy at the edge. Every model call identity-bound and logged. The DORA-aligned control point.
ExploreModel Risk Validation
Approved-model registry. Validation harnesses. Drift detection. The SR 11-7 layer.
ExploreAudit Ledger (CAVR)
Continuous audit chain. Monitoring evidence for the second line on demand.
ExploreAssurance Packs
Framework-mapped artifacts. SR 11-7, E-23, DORA, EU AI Act — submission-ready.
ExploreFrequently asked