Can AI Learn What Should Be Redacted in Your Documents?

AI-based redaction can be adapted to organization-specific identifiers through two distinct mechanisms: rule-based custom patterns and machine learning fine-tuning. For most organizations, rule-based configuration handles the use case without any model training. Custom rules let you define regex patterns for internal identifiers like employee IDs, proprietary case codes, or reference numbers, and the detection engine adds them to the standard entity types it already scans for. ML fine-tuning is a more involved process warranted only for complex unstructured entity types that rules cannot capture.

Rule-based custom patterns: no training required

Rule-based customization works by adding regular expression patterns that match your organization's identifiers. Examples:

• Employee IDs: a format like "EMP-XXXXXX" where six alphanumeric characters follow a prefix
• Internal matter codes: "MAT-2026-XXXXX"
• Proprietary reference numbers: formats specific to your practice management system

This approach requires no labeled training data, no model retraining, and no machine learning expertise. You define the pattern, the tool applies it to every document alongside standard entity detection. Results are immediate.

Rule-based patterns work well for any identifier with a consistent, structured format. They work poorly for identifiers that vary in phrasing, appear in free text without predictable structure, or rely on semantic context rather than surface form.

ML fine-tuning: when it is warranted

Some entity types cannot be captured by patterns because they lack a consistent format. Examples:

• Confidential business strategies described in natural language without a code or identifier
• Specialized clinical terminology that varies across physician notes
• Custom legal concepts particular to a niche practice area

For these cases, machine learning fine-tuning on domain-specific labeled data can improve Named Entity Recognition (NER) accuracy. This is the approach described in Stanford NER documentation and is practical for large organizations with sufficient labeled examples and technical resources.

The NIST AI Risk Management Framework recommends documenting the intended use case, data provenance, and validation approach for any AI system handling sensitive data. Fine-tuned models should be tested against held-out labeled data before deployment in production redaction workflows.

Choosing between the two approaches

• Setup time: Minutes to hours for rule-based; weeks to months for ML fine-tuning
• Requires labeled data: No for rule-based; yes for ML fine-tuning
• Best for: Rule-based handles structured identifiers with predictable formats; ML fine-tuning handles unstructured or context-dependent entities
• Maintenance: Update patterns as formats change (rule-based); retrain as language patterns evolve (ML fine-tuning)

For the majority of legal, healthcare, and financial use cases, rule-based custom patterns cover the organization-specific identifiers, and the standard detection engine handles the regulated entity types (SSNs, PHI, account numbers, dates). ML fine-tuning is the exception, not the starting point.

RedactifyAI supports custom entity rules so teams can add organization-specific identifiers without writing code. The free plan covers 50 pages per month and lets organizations test custom patterns against real documents before committing to a paid tier.