BerriAI / litellm
github.com·3d ago
Developer Tools
Databricks Blog·Sep 3·5 min read
Governance beyond security: knowledge, context & ontology on the lakehouse
TL;DR
Data governance for AI should focus on knowledge, context, and ontology rather than just security measures. By treating governance artifacts as foundational elements, organizations can enhance AI trust and efficiency.
✦ Why It Matters
Start integrating governance artifacts into your AI strategy to enhance data trust and model efficiency today.
Key Takeaways
- Implement governance artifacts as part of your data strategy.
- Utilize build agents to automate data product delivery.
- Focus on context-centric engineering for AI applications.
- Establish clear accountability for data product ownership.
How It Works
DEP utilizes 'build agents' to automate the data product lifecycle, ensuring that every step is governed by structured metadata. These agents read from a catalog of governance artifacts, perform tasks like data de-identification, and write back results, creating a continuous feedback loop.
This process shifts the focus from merely moving data to making it understandable and actionable for AI.