Technical evaluation
PII redaction technical overview
Status: in development. This page documents evaluation targets, not a guarantee of currently supported features. Exact coverage and interfaces must be confirmed during pilot scoping.
Reviewed 5 September 2026 · MaskFlare product team
- 01
Identify sensitive values
- 02
Replace with stable tokens
- 03
Restore only inside your boundary
Design-scope matrix
PII types for evaluation
| Data type | Proposed detection approach | Current status |
|---|---|---|
| Names and people | Contextual detection | Planned evaluation scope |
| Email addresses | Pattern and validation | Planned evaluation scope |
| Telephone numbers | Pattern, region, and context | Planned evaluation scope |
| Postal addresses | Contextual and component detection | Requires pilot scoping |
| Payment-card numbers | Pattern and checksum validation | Planned evaluation scope |
| Government identifiers | Country-specific validated patterns | Requires pilot scoping |
| IP addresses | Network-address parsing | Planned evaluation scope |
| Customer-defined identifiers | Schema, pattern, and dictionary rules | Requires pilot scoping |
| Images and scanned documents | OCR plus region-aware redaction | Not committed |
Synthetic examples
Input and transformed output
These examples illustrate intended policy behavior. They contain invented names, addresses, identifiers, and reserved documentation IP addresses.
AI prompt
Input
Summarize the case for Alex Morgan (alex@example.com), account 483029.
Transformed
Summarize the case for [PERSON_1] ([EMAIL_1]), account [CUSTOMER_ID_1].
Application log
Input
password_reset user=alex@example.com ip=203.0.113.42 status=sent
Transformed
password_reset user=[EMAIL] ip=[IP_ADDRESS] status=sent
Support transcript
Input
Alex: Please call me at +1 202-555-0147 about order MF-10492.
Transformed
[CUSTOMER_1]: Please call me at [PHONE_1] about order [ORDER_ID_1].
Policy actions
What can happen after detection
Label replacement
Replace a value with a category label such as [EMAIL].
Preserves general context
Consistent placeholder
Use the same synthetic label for repeated entities.
Preserves relationships
Partial masking
Reveal only an approved subset of a value.
Supports recognition tasks
Block
Prevent the content from continuing to its destination.
For prohibited transfers
Audit-only
Record the category and policy outcome without transforming content.
For evaluation and tuning
Tokenization
Replace a value with a separately controlled reference.
Design option; scope required
Detection methodology
Proposed evaluation pipeline
- 01
Normalize
Parse the supported input and identify fields, text regions, language, and format.
- 02
Detect
Apply schema rules, validated patterns, contextual models, and customer-defined detectors.
- 03
Resolve
Merge overlapping findings and assign entity type, location, and confidence.
- 04
Decide
Evaluate user, destination, data category, confidence, and policy exception.
- 05
Transform
Apply the approved action and produce policy-safe operational metadata.
How quality should be measured
Report precision and recall by data type, language, source, and format. Keep tuning samples separate from the final evaluation set, and review transformation quality as well as detection.
How false positives should be handled
Use confidence thresholds, field context, allow rules, scoped dictionaries, review queues, and versioned policy exceptions. Track overrides without retaining the sensitive value in ordinary telemetry.
Integration architecture
Place enforcement before the destination
Source
Browser, application, API, log collector, or batch pipeline
Detection
Schema, patterns, context, and customer-defined rules
Policy
Identity, destination, purpose, confidence, and action
Destination
AI service, support system, log platform, analytics, or processor
Potential integration modes include an API, inline gateway, application middleware, browser or access layer, log-processing stage, and batch workflow. No mode should be treated as committed until its security boundary and failure behavior are defined.
Known limitations
What an evaluation must challenge
Context can be ambiguous
Names, addresses, and internal references do not always have unique formats. Confidence thresholds trade missed detections against unnecessary redaction.
Language changes performance
Entity boundaries and address formats vary by language and region. Coverage must be measured on the languages used in the target workflow.
OCR is a separate boundary
Text extraction from images and scanned documents introduces recognition errors before PII detection begins.
Custom identifiers require configuration
Customer, employee, case, and transaction identifiers need organization-specific schemas, patterns, or dictionaries.
Redaction does not remove every data risk
Confidential business information, credentials, source code, and sensitive inferences may require separate detection and policy.
Operational telemetry can recreate exposure
Logs and alerts must record safe metadata and transformed context rather than copying the original value.
Next step
Bring your evaluation requirements
Tell us which data paths, types, languages, policy actions, deployment boundaries, and outcomes matter to your organization.