A global insurance operator's automated sensitive data detection tool had generated 19,000 field classifications — 78% of which were false positives. CALIGO conducted a systematic field-by-field review across 3 databases, validating 4,000 genuinely sensitive fields.
The company operated an automated sensitive data detection tool across its databases, but the tool's classification logic was overly broad — flagging far more fields as sensitive than genuinely warranted. With no systematic review process in place, these false positives accumulated over time, resulting in unnecessary masking of non-sensitive fields, operational inefficiencies for teams relying on that data, and a classification inventory that could not be trusted as an accurate reflection of the company's true privacy exposure.
CALIGO conducted a field-by-field review across all flagged data assets in 3 databases, individually evaluating each classification against defined sensitivity criteria and marking fields as either genuinely sensitive or incorrectly flagged. The review was structured to be systematic and auditable, ensuring that every decision was traceable and that the resulting classification inventory could serve as a reliable baseline for ongoing privacy governance — rather than simply correcting the immediate backlog.
Of the 19,000 fields originally flagged as sensitive across the 3 databases, only 4,000 were confirmed as genuinely sensitive — meaning approximately 78% of classifications were false positives. The remediation eliminated unnecessary masking on 15,000 fields, restoring operational access where it had been incorrectly restricted and significantly reducing the overhead imposed by the prior classification model. The company now holds a validated, accurate sensitive data inventory that forms a credible foundation for data privacy compliance.