Maintain a central registry for all your AI and ML models
Track every model from development to deployment with approval workflows, risk assessments, and compliance documentation.
The challenge
Shadow AI is your biggest compliance risk
Most organizations don't know what AI models are running in their environment, who deployed them, or what data they access.
Teams deploy AI models without governance oversight, creating compliance blind spots that auditors will find
No single source of truth means duplicate models, conflicting versions, and wasted resources across departments
When regulators ask 'what AI do you have?', you can't answer with confidence or provide documentation
Model risks like bias, security vulnerabilities, and performance issues go untracked until they cause incidents
MLOps teams and governance teams work in silos, leading to outdated records and manual reconciliation
Audit failures and fines increase as regulations like EU AI Act require comprehensive AI inventories
Benefits
Why use Model inventory?
Key advantages for your AI governance program
Register models with provider, version, and capabilities
Track approval status (Approved, Pending, Restricted, Blocked)
Sync automatically with MLFlow pipelines every hour
Manage model-specific risks across 5 categories
Capabilities
What you can do
Core functionality of Model inventory
Model registry with metadata
Track every model with provider, version, and deployment details, maintaining a complete inventory of your AI assets.
4-stage approval workflow
Route models through Pending, In Review, Approved, Rejected gates with designated reviewers at each stage.
MLFlow integration sync
Connect to MLFlow experiment tracking to automatically import model metadata, metrics, and lineage information into your governance registry.
Model risk categorization
Classify models by risk category based on use case, data sensitivity, and deployment scope for targeted governance.
Model analytics
Visualize model distribution by provider and risk level with approval pipeline throughput metrics.
Why VerifyWise
Built for real-world AI governance
What makes our approach different
MLFlow-native integration
Unlike spreadsheet-based approaches, VerifyWise syncs directly with your ML pipelines every hour. No manual data entry, no stale records, no reconciliation headaches.
Compliance-ready from day one
Model approval statuses, risk categories, and documentation requirements are designed around regulatory expectations. When auditors arrive, you're prepared.
Risk-aware by design
Each model has a dedicated risk register with categories that matter for AI: bias, performance, security, data quality, and compliance. Not generic enterprise risk templates.
Regulatory context
What regulations require
Multiple frameworks now mandate AI inventories and documentation. Here's what you need to know.
Article 9 requires providers of high-risk AI systems to establish a risk management system. Article 11 mandates technical documentation including system description, design specifications, and monitoring capabilities.
Clause 6.1.2 requires organizations to identify AI system risks. Clause 8.4 mandates documentation of AI system specifications, including model versions, training data, and performance metrics.
The GOVERN function requires organizations to establish policies and procedures for AI system documentation. The MAP function mandates inventory of AI systems and their purposes.
Technical details
How it works
Implementation details and technical capabilities
4 approval statuses: Approved (production-ready), Restricted (limited use), Pending (awaiting review), Blocked (prohibited)
5 model risk categories: Performance, Bias & Fairness, Security, Data Quality, and Compliance
4 risk levels: Low, Medium, High, Critical with status tracking (Open, In Progress, Resolved, Accepted)
MLFlow integration with hourly sync via BullMQ cron job, max 3 retries with exponential backoff (1s, 2s, 4s)
MLFlow auth options: None, Basic (username/password), or Token-based with optional SSL verification
Security assessment documentation with file uploads and structured assessment data (JSONB)
Model-to-project and model-to-framework linking for complete traceability
Field-level change history tracking with old/new values, user attribution, and timestamps
Supported frameworks
Integrations
FAQ
Common questions
Frequently asked questions about Model inventory
VerifyWise connects to your MLFlow tracking server and syncs automatically every hour. It imports model name, version, description, lifecycle stage, run ID, tags, metrics, and parameters. Authentication supports none, basic (username/password), or token-based with configurable SSL verification.
Each model record includes: provider (e.g., OpenAI, Anthropic), model name, version, approver, capabilities, security assessment flag and data, approval status, biases, limitations, hosting provider, reference link, and linked projects/frameworks. Full change history is maintained.
Model risks are categorized into 5 types: Performance (accuracy, latency), Bias & Fairness (discrimination, representation), Security (vulnerabilities, attacks), Data Quality (training data issues), and Compliance (regulatory violations). Each risk has Low/Medium/High/Critical severity levels.
Models have 4 statuses: Approved (cleared for production use), Restricted (limited use with conditions), Pending (awaiting review), and Blocked (prohibited from use). Each model has a designated approver and status date for audit tracking.
More from Discover
Related capabilities
Other features in the Discover pillar
Ready to get started?
See how VerifyWise can help you govern AI with confidence.