Assemble Data Sources, Architecture Diagrams, and Vendor Contracts
The founder gathers all underlying technical documentation, data architecture schematics, model training logs, and third-party vendor agreements. They extract key terms regarding data privacy, IP ownership, sub-processing, and algorithmic liability. This establishes the factual foundation needed to build a credible compliance framework.
Completing this action compiles the raw legal, technical, and operational inputs required for a thorough compliance assessment. It guarantees that the venture's AI strategy rests on verified technical reality rather than optimistic assumptions.
A centralised repository containing system design documents, API data-sharing agreements, privacy policies, and model performance logs. The founder must demonstrate complete visibility over data lineage and legal terms for all AI components.
Five questions an expert would ask when reviewing your output
Use these to challenge assumptions, pressure-test your logic, and check the quality of this action's output in the context of the parent task and wider venture development.
- 1
Which clauses in your software vendor contracts explicitly assign liability for algorithmic errors or regulatory breaches?
- 2
How complete are your data lineage logs from raw ingestion to model inference?
- 3
What documentation proves that training data consent covers your current commercial deployment model?
- 4
How do your technical architecture diagrams account for data residency and cross-border transfer restrictions?
- 5
Why are you confident that your internal engineering teams have logged all shadow AI or unvetted open-source models?