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This guide focuses on AI training data compliance for production systems, with practical trade-offs for reliability, security, and cost.
Training on the wrong corpus is a legal and trust problem, not an ML flex. Treat dataset intake like dependency licensing. Day 48 enforces manifests, secret scans, and counsel-approved sources.
⚡ TL;DR: Allowlist sources with license/ToS/PII fields. Block ToS-forbidden scrapes and unauthorized customer data. Fail CI without approved_by. Keep a purge path.
yaml
source: internal-sdk-git
license: proprietary-internal
tos_ok: true
pii_risk: low
customer_data: false
approved_by: legal-2026-08-01
text
Typical hard nos (confirm with counsel): sites that ban ML training in ToS; customer repos without contractual rights; secrets in samples; copyleft into proprietary weights without review. Citation-friendly RAG (Day 26) also reduces “invented copyrighted docs” product risk.
❌ "It's on GitHub so we can train"
AI training data compliance: production guidance
- [ ] Dataset manifest with legal fields
- [ ] Automated secret scan
- [ ] Customer-data path reviewed
- [ ] Counsel contact on runbook
- [ ] Purge procedure tested
- [ ] Training job refuses unsigned manifests
Series navigation
← Day 47 · Day 49 → Last updated September 11, 2026 Related: Day 42: Preference Data Without Vanity Accept Rates; Day 46: Synthetic Data That Doesn’t Clone GitHub Noise; Day 76: p99 of Agents: Queueing, Not Just Model Latency.Watch: 100 Days of AI on YouTube
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