Implementing DSAR Orchestration at Scale

Data Subject Access Request (DSAR) orchestration is the technical foundation for compliance at scale. Build an orchestrator that accepts access and deletion requests, deduplicates them, authenticates the requester, and fans out to registered systems. Each system must implement identifier based fetch and delete with identifier mapping for internal keys. Track per system completion and retries, aiming for 95 percent completion within 7 days and long tail within statutory limits: 30 days for GDPR with allowed extensions, 45 days typical for CCPA.

For deletion propagation, use soft delete plus compaction to propagate tombstones across replicas and caches. For feature stores, build versioned snapshots and a fast purge API that removes keys and invalidates caches. For models, maintain training data manifests that map subject identifiers to training shards. Two patterns help significantly: SISA training that shards and isolates segments to enable selective retraining within hours, and influence estimation to identify parameters most affected by a subject, then apply fine tuning to remove influence.

At high scale, the orchestrator must handle 1,000 to 10,000 DSARs per day, pushing 10 to 50 requests per second sustained with bursts to 200 per second. Monitor data egress, access patterns, and unusual joins that could leak personal data. Establish breach detection with clear triage to meet the GDPR 72 hour reporting clock. Run continuous compliance checks that alert on untagged datasets, missing lineage, or features used outside approved purposes. Companies like Meta handle millions of DSARs annually across thousands of data systems with dedicated teams and custom tooling.

💡 Key Takeaways

•DSAR orchestrators must handle 1,000 to 10,000 requests per day, sustaining 10 to 50 requests per second with bursts to 200 per second at scale

•Target 95 percent completion within 7 days, with long tail meeting statutory limits of 30 days for GDPR extensions and 45 days typical for CCPA

•Deletion propagation times vary: minutes for online stores using soft delete, 24 hours for warehouse tables, weekly cycles for model retraining

•SISA training shards data to enable selective retraining within hours, while influence estimation identifies affected parameters for fine tuning removal

•Continuous compliance checks alert on untagged datasets, missing lineage, or features used outside approved purposes to prevent violations before audits

📌 Examples

Meta handles millions of DSARs annually across thousands of data systems using custom orchestration with dedicated compliance engineering teams

A large ecommerce platform implemented SISA training for recommendation models, reducing unlearning time from 48 hours full retrain to 4 hours selective retrain

Google maintains training data manifests mapping user identifiers to training shards, enabling targeted deletion and retraining for affected model segments

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