InterVision AI Sovereignty and Data Lineage Service

Key Features:

1. Data Sovereignty Compliance: Implement policies and controls to ensure all data used in AI/ML processes remains within specified geographic boundaries, adhering to local data residency laws. Utilize on-premises infrastructure to ensure data stays within designated regions.
2. Secure AI/ML Pipelines: Develop secure, compliant AI/ML pipelines, ensuring sovereignty over the data lifecycle. Integrate data masking and encryption techniques to protect sensitive information during processing.
3. Data Lineage Visualization: Leverage data management tools to capture and visualize data lineage, providing a clear view of data movement and transformations from source to consumption. Utilize compatible APIs to capture lineage events from various data sources and processing jobs.
4. Ethical AI Usage: Develop AI governance policies that ensure data privacy, bias mitigation, and transparency in AI-driven decisions. Implement continuous monitoring and reporting tools to ensure adherence to ethical standards.
5. Compliance Monitoring: Set up automated tools for monitoring data movement, storage, and compliance, ensuring continuous adherence to sovereignty and privacy standards. Generate regular audit and compliance reports to maintain transparency and accountability.

Deliverables:

• Data Sovereignty Policy for AI: A comprehensive set of policies defining how data can be collected, processed, stored, and used for AI/ML purposes, ensuring full compliance with data sovereignty regulations.
• AI/ML Data Lifecycle Management: Tools and procedures for managing the full lifecycle of AI data, from collection to inference, ensuring data remains secure and sovereign throughout.
• Data Masking & Encryption: Integration of data masking, encryption, and anonymization tools to safeguard sensitive data during AI processes.
• Audit & Compliance Reports: Regular reports on data usage, access, and compliance, including insights into AI-driven decisions to ensure transparency and accountability.
• AI Model Governance Framework: Guidelines and tools for auditing AI model outcomes to ensure fairness, transparency, and ethical decision-making.
• Ongoing Monitoring & Automation: Automated tools for monitoring data movement, storage, and compliance, ensuring continuous adherence to sovereignty and privacy standards.