LLMs and AI agents have fundamentally changed data risk from economic to catastrophic. Four real incidents illustrate how AI agents can delete production databases, install malware through hallucinated packages, steal credentials via invisible prompts, and leak trade secrets. The solution requires shifting left with five data quality pillars: structural integrity (schema management), semantic validity (domain bounds), uniqueness and relations (referential integrity), privacy and governance (PII handling), and operational health (SLA monitoring). Data teams must adopt software engineering discipline with transient test environments and rigorous controls at the ingestion layer.
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