HAIAMM vs Databricks DASF
Databricks AI Security Framework
A framework mapping roughly 60 AI risks to about 50 controls across the components of an AI system, with strong coverage of the data and model lifecycle.
✓ Pros
- Excellent breadth, maps a large risk set to concrete controls across AI system components.
- Strong data-and-model-lifecycle coverage from a major data-platform vendor.
- Practical, implementation-oriented control text.
- Free and public.
⚠ Cons / Gaps
- A control catalog, not a maturity model, flat, with no tiers and no score.
- Anchored to the Databricks/lakehouse architecture and worldview.
- Less coverage of organizational governance, endpoints, and third-party AI.
- No assessment workbook producing a measurable program result.
Why HAIAMM is a strong choice
- HAIAMM adds the maturity gradient and quantitative workbook on top of comparable control coverage.
- HAIAMM is platform-agnostic across clouds, model providers, and deployment patterns.
- HAIAMM covers governance, vendors, and endpoints more fully as first-class domains.
How they work together
Use DASF as a deep control catalog (especially for data/model-lifecycle controls); use HAIAMM to add maturity tiers, scoring, and vendor-neutral breadth across the rest of the AI estate.
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