Connect Your Existing Tools
Oak Cover integrates seamlessly with your development and data infrastructure to automatically track risk factors, monitor changes, and provide real-time insights—no manual data entry required.
Automatic Risk Tracking
Our platform connects directly to your version control systems and data catalogs to automatically identify and track risk factors without requiring manual updates or configuration.
Real-Time Monitoring
Changes in your codebase and data are automatically detected and reflected in your risk score.
Full Traceability
Link models to their source code, training data, and deployments for complete audit trails.
Risky Data Detection
Automatically identify when PII, financial data, or other sensitive information is added to training datasets.
Connect Your Tools
Link your GitHub, GitLab, or Bitbucket repositories and data catalogs with a few clicks.
Automatic Discovery
Our platform automatically discovers your models, deployments, and training data sources.
Continuous Monitoring
We continuously monitor changes and update your risk score in real-time.
Version Control Integrations
Connect your code repositories to automatically track deployments, test runs, and code changes that impact your AI risk profile.
Features:
- Link models to repository branches
- Track deployment commits
- Monitor test run commits
- View commit history and changes
Use Case: Track which code version is deployed in production and link incidents back to specific commits for faster debugging and risk assessment.
Features:
- Monitor CI/CD pipeline runs
- Track release deployments
- Link models to GitLab projects
- View merge requests and changes
Use Case: Automatically track when new model versions are released through your GitLab CI/CD pipeline and assess risk changes.
Features:
- Link deployments to code changes
- Track commit history
- Monitor branch merges
- View pull request changes
Use Case: Maintain full traceability from code commits to production deployments, enabling quick identification of risky changes.
Data Catalog Integrations
Connect to your data catalog to automatically detect risky data additions, track data lineage, and monitor training data changes that impact your AI risk profile.
Features:
- Track training data versions and schema changes
- Detect PII, financial, and health data additions
- Monitor data refresh schedules
- Link models to their training datasets
- Alert on risky data schema changes
Automatic Risk Detection
When new fields containing PII, financial data, or health information are added to your datasets, we automatically flag them and update your risk score.
Use Case: Automatically detect when customer email addresses or phone numbers are added to a training dataset, alerting you to increased PII risk before the model is retrained.
Features:
- Monitor data lineage and dependencies
- Track metadata and schema changes
- Identify data sources and transformations
- Link training data to upstream sources
- Track data quality metrics
Use Case: Understand the complete data lineage of your training datasets, identifying all upstream sources and transformations that could impact your AI risk profile.