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Guides/AI governance & data lifecycle

AI governance & data lifecycle

Security basics cover threats; governance covers how data moves, how long it lives, and who answers for vendors. Product owns crisp flows — legal stamps controls — before deals stall on questionnaire number fourteen.

Lifecycle map

Use with privacy counsel — not a substitute for jurisdictional analysis.

Stages from collection through retention and audit for AI workloadsData lifecycle checkpointsCollectNotice &lawful basisMinimizeRedact /truncateRouteRegion /processorProcessLogs &training flagsRetain& auditProcurement maps subprocessors & DPAs to each box —PMs supply flows + data categories, legal stamps approval
Review with counsel — labels here align cross-functional conversations, not statutes.

Artifacts that unblock reviews

TopicPM-ready artifactTypical buyer question
Data categories in promptsMatrix: user content, account metadata, third-party docs, derived embeddingsWhat leaves our VPC? What is ephemeral vs stored?
Retention & deletionTTL defaults per surface (logs, traces, vector rows) + customer-initiated deleteRight-to-erasure latency; backup carve-outs
Subprocessors & regionsArchitecture diagram with inference, logging, storage regions labeledData residency; failover geography; cross-border transfers
Human review & trainingOpt-in language; whether prompts improve vendor models; enterprise zero-retain SKUsModel improvement clauses; audit rights on vendor change notices

Enterprise vs SMB emphasis

Procurement depth by theme (schematic)

Enterprise security reviews concentrate on provenance and subprocessors — SMBs often want plain-language promises first.

Reuse artifacts across sales — the same data map feeds security questionnaires with tweaks.