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DSAR Automation Agent

Privacy & Legal Engineering Task Force

Project Scope & Objective

Current Data Subject Access Request (DSAR) fulfillment is a bottleneck. It involves high-cost legal experts manually reviewing databases to distinguish between PII (Personally Identifiable Information) that must be deleted versus data that must be retained for legal compliance (e.g., tax records, fraud logs).

This research proposes an AI Agent architecture to automate the triage process. We analyzed three tiers of sophistication: 1. Raw LLM 2. Guidance RAG 3. Legal Precedent Agent.

Current Cycle Time
14 Days
Manual Review
Target Cycle Time
< 24 Hours
AI Augmented
Est. Cost Reduction
68%
Per Request

Workflow Transformation

Ingestion

User submits deletion request via portal.

AI Triage Agent (New)

  • Retrieve Policy (RAG)
  • Check Prior Decisions
  • Classify Fields

Human Audit

Legal only reviews "Low Confidence" flags.

Execution

Automated deletion of approved fields.

Projected Cost Efficiency

Comparison of legal counsel billable hours vs. AI infrastructure costs per 100 requests.

Select Architecture

Click to simulate performance metrics.

Legal Precedent Agent

This architecture retrieves context from two vector stores: the static Company Privacy Policy and a dynamic database of historical DSAR decisions made by the legal team. This serves as "Few-Shot" prompting, drastically reducing hallucinations by mimicking verified legal logic.

High Accuracy Audit Trail

Capability Scoring

Comparing the selected architecture against 5 critical success factors.

Auditability Ability to explain *why* a field was retained.
Privacy Safety Risk of accidentally leaking PII in reasoning.
Nuance Handling Understanding "Legal Hold" vs "Marketing Data".

Failure Mode Analysis

For DSAR automation, the cost of error is asymmetric. Deleting data required by law results in regulatory fines. Keeping data requested for deletion results in GDPR/CCPA violations. The Legal Precedent Agent minimizes these risks by anchoring decisions in past human judgments.

⚠️ Primary Risks Identified

  • 1. Hallucinated Legal Obligation: Raw LLMs may invent a law to justify retaining user data (e.g., citing a non-existent tax code).
  • 2. Context Loss: Without specific company guidance, an LLM cannot know that "User ID 555" is linked to an active litigation hold.

🛡️ Mitigation Strategy (Precedent Agent)

  • RAG Grounding: Every decision must cite a specific paragraph from the internal "Data Retention Policy" document.
  • Human-in-the-Loop Threshold: Any request with a confidence score < 85% is automatically routed to human legal counsel.