One signal

AWS has published a walkthrough for redacting personal information from scanned documents at scale with Amazon Bedrock Data Automation. A custom blueprint declares which fields are sensitive in plain language, and the service returns each match with a confidence score and bounding box coordinates for black-box redaction.

Why it matters

The useful number is recall. Across 12 test documents (47 pages) running from clean typed forms to 100 DPI scans, blueprint extraction alone scored 97.0% precision but 89.3% recall, missing repeated names in narrative text. A second pass token-matching blueprint output against BDA's word-level standard output lifted recall to 95.2% at a small precision cost, all from a single API call. The demo case is Attending Physician Statements at roughly 25,000 pages nightly, orchestrated by Step Functions and Lambda. For teams handling scanned medical, insurance or financial paperwork, the recipe is concrete: scope sensitivity per use case, then measure against human-redacted ground truth before trusting the machine.

Discuss: Would 95.2% recall clear the bar for compliance redaction in your world, or does the missing 4.8% mean a human reviewer stays in the loop for good?

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