Rivac AI LabsAI Risk Assessment & Model Risk Management
When an AI system is making or influencing consequential decisions (credit, hiring, medical triage, safety) an informal sense that 'it seems to work fine' isn't risk management, it's exposure waiting to surface. We build structured risk registers and model-risk governance processes specifically for AI systems, identifying failure modes, quantifying their business and legal impact, and defining ongoing monitoring and controls. This draws on established model risk management discipline from regulated industries like banking, adapted for the realities of LLM and ML-based systems. Clients use this to know, in specific and documented terms, where their AI systems could fail and what's being done about it, before a regulator or a plaintiff's attorney asks the same question.
This is a capability we deliver for you as a service, not a role we’re hiring for.
How We’d Approach This
A clear, staged plan, not a black box
These are the service-specific steps inside our six-step process.
- 1
Inventory AI systems currently deployed or planned, and identify which are making or influencing consequential decisions
- 2
Assess failure modes for each system and quantify the business, legal, and reputational impact
- 3
Build a structured risk register with defined owners, monitoring triggers, and mitigation controls per risk
- 4
Establish an ongoing model risk review cadence so the register stays current as systems change
What You Get
Deliverables from this engagement
- Structured AI risk register scored by likelihood and impact
- Model risk governance process with defined ownership and review cadence
- Monitoring and control recommendations per identified risk
- Documentation package suitable for regulatory or audit review
Six Ways We Could Architect This
Different engagement, different build, pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Read all 6 architectures as text
Lean Fast-Start Architecture
Intake
- AI System Inventory
- Consequential Decision Use Cases Flagged
- Initial Risk Interview
Assessment
- Failure Mode Brainstorm
- Impact And Likelihood Scoring
- Draft Risk Register
Controls
- Existing Controls Mapped
- Gap List Identified
Delivery
- Client Review Session
- Finalized Risk Register
- Priority Remediation List
- Monitoring Cadence Agreed
Cloud-Native / Managed Services Architecture
Data Layer
- Model Inventory API
- Managed Risk Register Platform
- Model Performance Data Warehouse
Assessment Operations
- Managed Model Monitoring Service
- Failure Mode Library
- Impact Quantification Engine
Review Workflow
- Cloud Based Risk Scoring Dashboard
- Stakeholder Sign Off Workflow
Delivery
- Published Risk Register Portal
- Automated Threshold Alerts
- Executive Risk Dashboard
- Quarterly Report Generator
Event-Driven & Automation-First Architecture
Triggers
- New Model Deployed
- Automated Risk Intake Scan
- Model Performance Drift Detected
- Auto Risk Score Recalculation
Automation Layer
- Failure Mode Auto Classifier
- Auto Updated Risk Register Entry
- Auto Routed To Risk Owner
Review Gate
- Risk Score Above Threshold
- Escalated To Model Risk Committee
- Logged As Accepted Risk
Delivery
- Notification To Model Owner
- Scheduled Risk Digest
- Risk Register Updated And Timestamped
Enterprise-Grade & Compliance-Ready Architecture
Governance Layer
- Model Risk Policy
- Three Lines Of Defense Structure
- Regulatory Mapping Basel Style MRM
Secure Register
- Access Controlled Risk Register
- Role Based Model Owner Access
- Versioned Assessment Records
Auditable Review
- Independent Model Validation
- Risk Committee Review
- Documented Mitigation Plan
Compliance Delivery
- Audit Trail Export
- Redundant Risk Data Backup
- Signed Off Risk Assessment
- Annual Revalidation Schedule
AI-Augmented, Human-in-the-Loop Architecture
Data Ingestion
- Model Documentation And Logs
- Historical Incident Data
AI Analysis Layer
- AI Failure Mode Discovery Engine
- Draft Risk Scenarios
- Draft Impact Quantification
Human in the Loop
- Model Risk Analyst Review
- Assessment Complete And Defensible
- Flag Gaps Back To AI Engine
- Human Approved Risk Register
Delivery
- Published Risk Register
- Mitigation Plan Assigned
- Ongoing Monitoring Dashboard
- Quarterly Reassessment Trigger
Integration-Heavy / Ecosystem-Connected Architecture
Client Systems
- Model Registry And MLOps Platform
- Enterprise GRC System
- Incident Management Tool
Risk Core
- Risk Assessment Engine
- Risk Register Model
- Mitigation Tracking Output
Two Way Sync Layer
- GRC System Sync
- MLOps Model Tagging Sync
- Compliance Team Dashboard
- Data Science Team Dashboard
Shared Governance
- Joint Review With Risk Committee
- Published Enterprise Risk Register
More in AI Strategy, Readiness & Governance
- AI Center of Excellence Setup
Stands up the internal structure, champions, and playbooks that let an organization keep adopting AI after the initial engagement ends.
- AI/LLM Security Red-Teaming
Adversarial testing of deployed models and agents for prompt-injection, jailbreak, and data-exfiltration vulnerabilities.
- AI Opportunity Discovery & Use-Case Prioritization
Facilitated workshops that surface the highest-ROI AI use cases across your business and rank them by impact and feasibility.
- AI Maturity & Readiness Assessment
A structured audit of your data, infrastructure, talent, and process readiness for AI adoption, with a scored gap report.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.
Talk to us about AI Risk Assessment & Model Risk ManagementMost engagements like this start as a $500–$2,500 pilot, see full pricing.