Turn AI risk from a blocker into a competitive advantage
A structured approach to identifying, assessing, and mitigating AI risks across your organization—with full traceability to compliance frameworks.
The challenge
AI risk management is harder than traditional IT risk
AI systems introduce risks that evolve over time—bias that emerges from data drift, performance degradation in production, third-party model dependencies, and regulatory requirements that change faster than your documentation. Traditional risk management tools weren't built for this complexity.
Risks are scattered across spreadsheets, emails, and different team silos
No standardized way to quantify and prioritize AI-specific risks
Difficult to trace risks back to regulatory requirements during audits
Mitigation efforts lack visibility, ownership, and accountability
Risk posture changes over time but historical trends are lost
Benefits
Why use Risk management?
Key advantages for your AI governance program
Identify risks across the entire AI lifecycle
Prioritize with severity-weighted risk scoring
Track mitigation progress with clear ownership
Demonstrate due diligence to regulators and auditors
Capabilities
What you can do
Core functionality of Risk management
Unified risk register
Consolidate project, vendor, and model risks in one place with specialized tracking for each risk type.
Quantified risk assessment
Calculate risk levels using a severity-weighted formula that prioritizes impact over probability.
Mitigation workflows
Assign owners, set deadlines, and move risks through a 7-stage workflow from identification to resolution with full audit trail.
Framework traceability
Link risks to EU AI Act controls, ISO subclauses, and NIST AI RMF subcategories.
Risk analytics
Track how your risk posture changes over time with severity trends across your entire AI portfolio.
Enterprise example
How a global organization gained control over distributed AI risks
See how organizations use this capability in practice
The challenge
An organization with multiple AI initiatives across different business units had no unified view of AI risks. Each team tracked risks differently—some in spreadsheets, some in project management tools, some not at all. When preparing for an external audit, they spent weeks gathering risk information from various sources.
The solution
They implemented a centralized risk management system with three separate registers for project, vendor, and model risks. Each risk was linked to the relevant compliance framework controls and assigned clear ownership with deadlines.
The outcome
The organization reduced audit preparation time significantly. They could demonstrate exactly which risks mapped to which regulatory requirements, show historical risk trends, and prove that mitigation activities were tracked and completed. Leadership gained real-time visibility into organizational risk posture across all AI initiatives.
Why VerifyWise
Purpose-built for AI risk complexity
What makes our approach different
Three-dimensional risk tracking
Separate registers for project risks, vendor risks, and model risks—each with specialized fields and workflows tailored to that risk category.
Severity-weighted scoring
Our risk formula weights severity 3x higher than likelihood, ensuring high-impact risks get prioritized even when they seem unlikely.
Built-in framework mapping
Link any risk directly to EU AI Act controls, ISO 42001/27001 subclauses, or NIST AI RMF subcategories. When auditors ask how you manage a specific risk, you have the answer.
Historical risk analytics
Time-series snapshots track how your risk posture changes over time. See trends across severity, likelihood, and mitigation status from 7 days to 1 year.
Regulatory context
Meeting regulatory risk requirements
AI regulations worldwide require organizations to identify, document, and mitigate risks throughout the AI lifecycle. A structured risk management approach helps demonstrate compliance with these requirements.
Article 9 requires high-risk AI systems to have a risk management system that identifies, analyzes, and addresses risks throughout the system lifecycle.
Clause 6.1 requires organizations to determine risks and opportunities related to AI management systems and plan actions to address them.
The MANAGE function requires organizations to prioritize, respond to, and monitor identified AI risks based on projected impact.
Technical details
How it works
Implementation details and technical capabilities
Three risk types: Project risks, vendor risks, and model risks with specialized tracking for each
Risk formula: Score = (Likelihood Ă— 1) + (Severity Ă— 3) with severity weighted 3x heavier
6-level risk classification: No risk, Very low, Low, Medium, High, Very high based on calculated scores
7-stage mitigation workflow: Not Started, In Progress, Completed, On Hold, Deferred, Canceled, Requires Review
AI lifecycle phase tracking: Problem definition through Decommissioning for comprehensive risk coverage
Time-series risk history with snapshots tracking severity, likelihood, and mitigation status over time
Change history tracking for all risk modifications with full audit trail
Supported frameworks
Integrations
FAQ
Common questions
Frequently asked questions about Risk management
Risk Score = (Likelihood Value × 1) + (Severity Value × 3). Severity is weighted 3x heavier than likelihood because high-impact risks deserve attention even when probability is low. Based on the total score: ≤4 = Very low, 5-8 = Low, 9-12 = Medium, 13-16 = High, ≥17 = Very high risk.
Project risks cover organizational and project-level concerns with full mitigation workflows. Vendor risks track third-party AI provider exposure with contract and compliance context. Model risks focus on AI/ML-specific issues like performance degradation, bias, security vulnerabilities, and data quality problems.
Yes. Risk history tracking captures periodic snapshots of risk distribution across severity, likelihood, mitigation status, and overall risk level. You can analyze trends over configurable timeframes from 7 days to 1 year to demonstrate continuous improvement.
Every risk can be linked to specific framework controls through dedicated mapping tables. This creates bidirectional traceability—view all risks affecting a particular EU AI Act control, or see all framework requirements a specific risk relates to.
More from Govern
Related capabilities
Other features in the Govern pillar
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See how VerifyWise can help you govern AI with confidence.