Redaction removes a sensitive value from the text a model receives. Pseudonymization replaces it with a stand-in so the application can preserve a relationship or restore the value later. The right choice depends on what the task needs, which values you can detect, and where the original values remain accessible.
For an internal AI application, begin with the output the employee needs. If the model only needs to classify a support issue, it may not need any person's name. If it must draft a response about several named people, distinct stand-ins can preserve who is associated with each fact. Neither approach means you have detected every sensitive value.
Choose the transformation from the task
The following hypothetical input contains synthetic personal data: “Send Alex Morgan at alex@example.com the status of case 418.” A redacted version might read “Send [PERSON] at [EMAIL] the status of case 418.” A pseudonymized version might use distinct identifiers for the person and the address, with a protected mapping available to the application.
These examples describe the transformation, not a specific API's token format. The case identifier might itself be sensitive in your organization. A general personal-data recognizer will not necessarily know that, so the data inventory matters as much as the transformation mechanism.
| Need | Candidate approach | Question to resolve |
|---|---|---|
| Topic classification | Remove unnecessary values | Can you classify without identity? |
| Drafting a response | Distinct stand-ins with controlled restoration | Who may restore the result? |
| Matching entities across turns | Scoped stable stand-ins | What is the permitted scope and lifetime? |
| Detecting credentials | Remove or block the value | Should the request proceed at all? |
Prefer removing values that the task does not use. Reversibility creates another capability that requires access control and an explicit reason to exist.
Treat detection and transformation as separate problems
A perfect replacement function is ineffective when detection misses the value. Evaluate your recognizers on the text your employees actually submit, including supported languages, abbreviations and document formats. Use synthetic or appropriately controlled evaluation data, rather than adding real sensitive documents to a test repository.
Presidio's anonymizer documentation illustrates this separation: recognizer results identify spans, and operators transform those spans. Its available operations include replacement, masking, redaction and encryption. Choosing an operator does not establish the accuracy of the recognizer that supplied its input.
Create separate measurements for missed sensitive spans and incorrect removals of useful text. A detector that removes product codes because they resemble identifiers may make the model less useful. A detector that overlooks identifiers in another language may expose data while the output still appears fluent.
Scope reversible mappings to the correct boundary
Do not use one global mapping across unrelated organizations just because it is convenient. Stable stand-ins can create links between records. Decide whether a mapping is scoped to a request, a conversation or an organization, and test that isolation explicitly.
Restoration needs authorization too. An employee who can generate text should not automatically gain the right to restore every stand-in from another context. A model response is untrusted output; the application should not treat an arbitrary token-looking string as proof that it may reveal a value.
Keep mappings out of model prompts, broad application logs and diagnostic exports. If you retain them, specify the location, access policy and lifetime. If the product uses a deterministic restoration design instead, assess its equivalent boundaries rather than assuming that the absence of a database removes the risk.
Test responses and streaming behavior
A useful evaluation includes repeated values, overlapping spans, malformed stand-ins, partial output and copied text from an earlier conversation. When responses stream, split an output token across transport chunks and confirm that the client does not receive broken restoration or an unintended value.
Test tool calls independently from displayed prose. A stand-in that works in a sentence might be invalid as a tool argument, and restoring a value before a tool executes can affect authorization. The component that executes the tool must still validate the employee's permission to perform the action.
Do not quietly modify a field whose integrity depends on a provider signature. Your integration needs an explicit contract for such fields. A feature that can restore prose does not automatically support every structured or signed response format.
Review the whole data path
Inspect what is sent to retrieval, telemetry and error reporting. Transforming the final model request does not remove original values from earlier logs. Conversely, restoring the response can reintroduce personal data into a later system that was assessed only against transformed input.
Record both coverage and action. A record should distinguish text that was inspected and transformed from an attachment that the configured detector could not inspect. “No finding” is meaningful only when you know what was evaluated.
Alectura supports personal-data pseudonymization at the inference boundary; its guardrails overview describes the available protection. Assess the supported kinds and limits against your own evaluation set before making a rollout decision. Protection at that boundary complements, rather than replaces, the authorization and data-handling design of your internal application.
Use the guardrails reference to compare the supported detectors with your evaluation cases. Record missed values and incorrect replacements before choosing a production policy.