One governance register for every model, including your AI.
Model inventory, validation, backtesting and drift under SR 11-7 — extended to AI systems with bias, ethics and explainability for the EU AI Act, NIST AI RMF and ISO 42001.
Model Risk Register
LiveModels registered
318
High-risk AI
22
Validations current
100%
Drift breaches
4
↓-3Model performance index, last 12 months
GeneSecure Model Risk & AI Governance is a unified register for governing both statistical models and AI systems. It covers model inventory, independent validation, backtesting and drift monitoring under SR 11-7, and extends to AI-specific controls — bias and fairness testing, explainability, ethics review and whistleblower intake — aligned to the EU AI Act, NIST AI RMF and ISO 42001.
Illustrative outcome. We will map Model Risk & AI Governance to your own data, frameworks and targets in a working demo.
The case for Model Risk & AI Governance
Govern every model — statistical or AI — from one register.
One register, every model
Statistical models and AI systems live in one inventory with tiering, ownership and lifecycle state.
SR 11-7 by design
Independent validation, backtesting and ongoing monitoring follow the three-lines model out of the box.
AI Act ready
Risk classification, bias/fairness testing and explainability map directly to EU AI Act and NIST AI RMF expectations.
Drift caught early
Continuous drift and actual-vs-expected monitoring with breach alerts before performance degrades materially.
Transparent by construction
Model scores carry transparent weights and validation metrics — AUROC, PSI drift, backtest results — so reviewers see why a number is what it is, not a black box.
The platform governs itself
GeneSecure's own risk engine ships a model inventory whose "validated" status is computed by executing checks in-process, with each engine's assumptions stated in words — the standard we sell is the standard we run.
Capabilities that ship on day one
Every capability runs on the shared data fabric, the governed Cortex brain and the evidence ledger — so Model Risk & AI Governance compounds with the rest of the platform.
Model inventory & tiering
Central register with materiality tiering, ownership and lifecycle state.
Independent validation
Validation workflow with findings, conditions and revalidation scheduling.
Backtesting & performance
Backtests, actual-vs-expected and performance benchmarks over time.
Drift monitoring
Continuous drift detection with breach alerts and time-to-breach.
Model Risk Register
LiveModels registered
318
High-risk AI
22
Validations current
100%
Drift breaches
4
↓-3Model performance index, last 12 months
Bias & fairness evidence
Verifies that bias, fairness and evaluation controls are recorded against each AI asset, surfacing gaps as findings.
Transparent scoring & metrics
Feature-level weight transparency plus tie-aware AUROC, PSI drift and Basel traffic-light backtests attached to each model.
AI ethics & risk classification
EU AI Act risk tiering, ethics review and human-oversight controls.
Whistleblower & incident intake
Channels to raise model and AI concerns with case tracking.
Interactive walkthrough
Model risk and AI governance, from a shadow inventory to an examined register
Five scenes: what is actually running, SR 11-7 validation as a workflow, why the AI Act class depends on the use and not the model, drift caught between validations, and what an examiner receives.
Platform walkthroughs for this module
Real scenarios, real outcomes
Where Model Risk & AI Governance changes the day-to-day — the situation teams start from, and the outcome they get.
Every model with a current status
The challenge
Pricing, reserving and a growing roster of AI/ML models are tracked across spreadsheets, so validation status is hard to evidence for examiners.
The outcome
One register shows every model — statistical and AI alike — with a current, evidenced validation status and clear ownership.
AI Act-ready in the same register
The challenge
New AI systems need bias, explainability and risk-classification evidence that a traditional model-risk tool simply doesn't capture.
The outcome
AI models carry fairness evidence, transparent scoring and EU AI Act risk tiering alongside statistical models, under one governance workflow.
Drift caught before it costs
The challenge
Model degradation is noticed only at the annual review, after it has already skewed pricing or capital decisions.
The outcome
Continuous drift and actual-vs-expected monitoring raises a breach alert before performance slips materially.
Made for your team, aligned to your frameworks.
Cortex skill-agents draft the work, cite their sources and write every action to the evidence ledger — so Model Risk & AI Governance accelerates the people accountable for it without putting your audit posture at risk.
Who it serves
- Model risk managers
- Validators
- AI / ML governance leads
- Chief Data / AI Officers
- Regulators & auditors
Aligned to
- SR 11-7
- EU AI Act
- NIST AI RMF
- ISO 42001
- OCC model risk guidance
Pairs with the rest of the platform
Model Risk & AI Governance shares one core with every other module — combine it to extend coverage without adding integration debt.
Enterprise GRC
One command center for risk, resilience, third-party and privacy.
Financial Risk
VaR, liquidity stress and credit exposure in one command center.
GeneSecure Cortex
One governed AI control plane across every module.
Risk Management
An AI-native ERM loop that detects, predicts and auto-remediates.
Model Risk & AI Governance FAQ
What evaluation teams want to know before a demo — answered plainly.
See Model Risk & AI Governance on your data
Book a working session and we will map your sources, workflows and frameworks onto Model Risk & AI Governance — and show Cortex reasoning over them live.