An AI tool that automatically scans documents and images to find and redact personally identifiable information (PII) like names, addresses, social security numbers, and credit card details, ensuring GDPR and CCPA compliance.
Law firms, government agencies, insurers and many other businesses regularly share documents that contain personal information about people who are not party to the purpose, such as discovery bundles, freedom of information releases, complaint files and data sets for vendors. Manual redaction is slow and error-prone: a missed tax file number or home address can cause real harm, and a redaction drawn over text without removing the underlying text layer can be undone easily.
AI PII redaction scans documents and images to find personal information and removes it properly. OCR reads scanned pages and images, named entity recognition finds names, addresses, dates of birth and contact details, and pattern checks detect identifiers such as tax file numbers, Medicare numbers and card numbers. Suggested redactions appear for review, then the tool permanently removes the text and burns the redaction into the output. Reviewers focus on judgement calls instead of hunting line by line.
Documents in PDF, Word, email and image formats are loaded into a secure processing environment, with scans run through OCR.
Named entity recognition and pattern rules identify personal information, including names, addresses, phone numbers, TFNs, Medicare numbers, bank details and card numbers.
Computer vision detects faces, signatures, handwriting and identity documents within images and scanned pages.
A reviewer sees suggested redactions with reasons, accepts or rejects each one, and adds any the tool missed, applying consistent rules across the batch.
The tool produces redacted copies with text removed and metadata cleaned, plus a redaction log recording what was removed and why.
Before sharing discovery documents in a legal case, a law firm runs them through a PII redaction tool. The AI automatically blacks out all sensitive personal information, preventing accidental data breaches and saving hundreds of hours of manual paralegal work.
NLP (Named Entity Recognition), OCR, Computer Vision, Data Loss Prevention (DLP) software
Under the Privacy Act 1988, disclosing personal information outside the purpose it was collected for can breach the Australian Privacy Principles, and a disclosure caused by failed redaction may be a notifiable data breach under the Notifiable Data Breaches scheme. Tax file numbers carry specific restrictions under the Privacy (Tax File Number) Rule, and health information is sensitive information with stricter handling. Government agencies must also meet freedom of information laws, and courts have their own rules on redacting filed documents. Because the documents being processed are sensitive, use a tool that keeps data in Australian cloud regions or on-premises, and avoid consumer AI services.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Not for high-stakes releases such as discovery, FOI or publication. The tool catches the vast bulk of obvious identifiers quickly, but context-dependent information and poor scans need human judgement. A good workflow uses AI to propose redactions and a trained reviewer to approve them, with the log kept as evidence of a careful process.
It should, but check before buying. Many redaction tools are built for overseas formats and need configuration for Australian identifiers, addresses and phone numbers. Test the tool on a sample of your own documents that include TFNs, Medicare numbers, ABNs and licence numbers, and confirm check-digit validation where formats allow it.
The two-to-four-week band covers configuring entity types and redaction rules, setting up a secure environment, integrating with your document system and testing on real sample files. Training reviewers on the approval workflow is part of setup. Additional use cases, such as a new FOI process, can be added once the first one is running.
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