What we explored
Solutions Lab · Testing
AI Information Stewardship
Practical patterns for using AI while preserving appropriate control over client information, access, retention, validation, and human accountability.
Why it matters
Organizations need usable rules for real work. A generic policy does not answer which information can move through which environment, who can authorize access, or how outputs should be reviewed.
What B2 built or tested
- Risk-classification patterns
- Approved-environment decision rules
- Least-privilege access concepts
- Human-review and validation checkpoints
Technical context
- Identity and access management
- External AI environments
- Connectors and agent permissions
- Logging, retention, and review
What B2 learned
- A vendor statement that data is not used for training does not by itself answer every stewardship question
- Risk should determine the level of control and review
- Client alignment should precede sensitive external AI use
Where it could apply
- Enterprise AI adoption
- Consulting delivery
- Knowledge-work automation
- AI-enabled application design
Solutions Lab projects document active research and learning. They do not imply that an experimental capability is a mature client product.
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