Prevent accidental data leaks from spreadsheets and email with inline DLP checks

Executive Summary

Employees accidentally exfiltrate sensitive data more often than hackers. Build an inline email and file DLP that detects sensitive content in spreadsheets and prompts or blocks risky shares, with audit trails for compliance. Higher regulatory risk and remote work have increased spreadsheet sharing frequency across SaaS apps, making accidental leaks more common as illustrated by the source incident. Recent advances in ML for structured data extraction and pattern recognition let products reliably detect sensitive columns, account numbers, and PII inside attachments, enabling inline prevention without heavy rule engineering. Meanwhile major platforms expose richer integration points and APIs for email and file stores, making low-latency inline checks feasible for the first time. Focus on automatic detection of sensitive structured data inside spreadsheets and common attachments, offering low-friction inline warnings, one-click remediation, and immutable audit trails. The source example of a single misforwarded spreadsheet creating a regulatory nightmare shows the core workflow - frequent spreadsheet sharing plus high compliance cost - which an AI model trained on structured data patterns can detect more accurately than generic DLP heuristics. Combine this with integration into email providers and SaaS file stores to create a data moat of incident and labeling signals that improves models over time.