AI disclosure structured by feature, model, data, and controls
Write AI trust-center disclosures in the buyer's review order: what the feature does, which model it uses, what data touches it, and what controls sit around it.
Why this can grow a startup
AI trust pages fail when they sound like marketing copy instead of review material. Drata says its AI Feature Items are organized around feature, model, data, and controls. That structure works because it matches the questions buyers already ask in procurement and security review, which makes the answer easier to scan, easier to compare internally, and easier to reuse across deals.
Company example
Drata says its AI Feature Items use a structured narrative organized around feature, model, data, and controls.
Source and metric
Source: Drata · Browse Drata tactics
Drata built its AI disclosure format around the review sequence of feature, model, data, and controls.
Source discovered: May 28, 2026
When to use it
Use this when Website, Security, AI Discovery is relevant to consideration, security review, ai transparency and you can run a bounded test with a free budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
Founder checklist
- Read Drata and identify what is directly supported.
- Choose one channel context: Website, Security, AI Discovery.
- Define the test around Drata built its AI disclosure format around the review sequence of feature, model, data, and controls..
- Set an owner, evidence window, and stop condition before launch.
Explore the context
Apply this with an operator
Choose product surfaces that compound distribution without hiding weak activation or retention.