Derived-Data & AI Reach Map
Consent State Propagation across Feature Stores, Vector Embeddings, and ML Model Weights
When personal data feeds machine learning and AI pipelines, the legal obligations of DPDP propagate through all derived data layers. Under Section 2(t), any derived data from which a natural person can be directly or indirectly identified remains personal data. If a principal withdraws consent under Section 6(4), the enterprise must evaluate the impact across: (1) Raw Feature Stores -> (2) Aggregated Metrics -> (3) Vector Embeddings -> (4) Model Training Checkpoints -> (5) Inference Logs.
Derived-Data & AI Reach Map
Stage 1: Raw Ingestion
Immediate deletion upon s.6(4) revocation.
Stage 2: Feature Store
Prune user row from offline and online feature tables.
Stage 3: Vector Embeddings
Delete user vector ID from Pinecone / Milvus / pgvector.
Stage 4: Model Weights
Machine unlearning or scheduled model retraining if personal data memorized.
Stage 5: Inference Logs
Scrub user identity and prompt history from telemetry archives.
💡 Core Architectural Insights & Takeaways
Key technical and regulatory takeaways established by this architecture diagram.
📜 Statutory Grounding & Legal Perimeter
Primary Act and subordinate Rule provisions establishing the enforceable legal mandate for this diagram.
🏗️ Target Architecture & Impacted Systems
Enterprise nodes and store topologies impacted by this architectural pattern.
Domestic production database and primary system of record for loan servicing
Purpose-partitioned analytical warehouse staging operational reporting
Immutable consent event store and Policy Decision Point issuing authority tokens
⚙️ Associated Operational Controls
Control Master Matrix obligations enforcing the standards illustrated in this infographic.
🛠️ Implementation Guidance & Traps
Recommended technical sequencing and operational failure modes to avoid.
Recommended Implementation Sequence
- Maintain a Model Data Provenance Registry linking training datasets to consent version IDs.
- Build automated vector deletion pipelines on RAG knowledge bases.
- Implement differential privacy or strict anonymization prior to feature engineering.
Enterprise Traps & Failure Modes
- ⚠️ Assuming mathematical embeddings or derived scores are automatically non-personal data.
- ⚠️ Training foundational models on unconsented user data without retention controls.
📁 Verifiable Evidence Artifacts Vault
Required evidentiary workpapers for regulatory inspections and SDF audits.