AI Data Remediation

Operate a self-healing remediation layer for mission-critical data pipelines: intercept corrupt or anomalous data, semantically cluster pattern families, generate deterministic fix logic via local or sandboxed language models, and guarantee zero data loss.

Core Principle

AI generates verifiable transformation logic — never touch or mutate production data directly.

Core Invariants

1. AI Logic Over Raw Data Mutation: The model generates pure, deterministic transformation functions that can be tested, reviewed, and versioned. Raw model outputs are never piped directly into tables.
2. Mandatory AST Static Validation: Every generated lambda or transformation must pass Abstract Syntax Tree (AST) validation against restricted namespaces before evaluation.
3. Strict Zero-Loss Accounting: Total input records must exactly equal successful records plus quarantined records ($\text{Source} = \text{Success} + \text{Quarantine}$). Any non-zero delta immediately halts processing.
4. Air-Gapped / Privacy-Preserved Execution: Sensitive or PII data must never egress to external cloud APIs without prior tokenization or local execution.
5. Human Review Quarantine: Clusters with low transformation confidence ($< 0.75$) or failed AST checks route to an isolated quarantine table with full lineage.