AI PII Redaction
Catch personal information in text before it leaves the app, and mask it.
What it adds
A redaction pass over text and attachments on the paths that share, export, or forward content to another system.
What your agent is told to do
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What your agent is told to do
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Identify every path where text leaves its original context: exports, share links, support handoffs, outbound webhooks, and anything sent to a model provider. Redaction belongs on those boundaries, not scattered through the UI.
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Run two detectors together and merge their results. Pattern matching handles the formats with a fixed shape, and a model pass handles the cases only context reveals, such as a name and a home address written into a free-text note.
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Treat the model as one signal, not the decision. Anything the model alone flags must carry lower confidence than a deterministic match, and the feature must keep working with pattern detection only when the model is unavailable.
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Decide per destination whether redaction is one-way or reversible. Reversible placeholders that map back to the original value are only acceptable when the mapping is stored server-side and unlocking it is a permissioned, audited action.
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Do not send the whole document to a model to find personal data if a narrower slice will do, and do not log the raw text of anything the detectors matched. Redaction that leaks its own findings into a log line has moved the problem, not solved it.
Edge cases it handles
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Edge cases it handles
8- Pattern detectors and model detectors fail in opposite directions, so run both and combine them. Fixed formats such as card numbers, national identifiers, and email addresses should be caught deterministically, while a model pass catches the personal detail that is only identifiable from the sentence around it.
- A reversible placeholder is a stored link back to the original value, so only issue one where the destination is authorized to unmask, and require an explicit permission check plus an audit entry every time someone reverses it.
- Over-redaction destroys the document. An order quantity, a version number, a product code, or a common first name used as a label must survive, or the recipient gets a page of black boxes and stops trusting the feature.
- The same rules must apply to structured fields, uploaded attachments, and any text the app itself generated. A record redacted in its notes field but exported with an untouched CSV column has not been redacted.
- High-impact documents need a review step where a person sees the proposed masks and can add or remove them before the content is released, rather than the redaction being applied silently on the way out.
- A model refusal, timeout, or malformed response must not let unredacted text through. Fail the outbound operation with a clear message and keep the deterministic matches applied.
- Redaction must be applied to the copy being sent, never to the stored record. Overwriting the source destroys data the workspace still needs.
- Detection quality varies by language and locale. State which locales are covered and be explicit in the UI rather than implying uniform coverage.
Definition of done
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Definition of done
9- Every path that exports, shares, or forwards content runs the redaction pass before the content leaves.
- Deterministic pattern matches and model-derived matches are both applied and are distinguishable by confidence.
- Reversible placeholders exist only where unmasking is permission-checked and recorded.
- Structured fields, attachments, and generated output are covered by the same rules as free text.
- High-impact documents present a review step before release.
- A model failure blocks the outbound operation rather than releasing unredacted text.
- No raw matched value appears in application logs.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Prompt Injection Defense
Prompt Injection Defense
Stop instructions hidden in documents, pages, and tool output from steering the AI.
What it does
A boundary between the app's own instructions and untrusted content, backed by enforced tool permissions and confirmation for consequential actions.
How it works
- 1 Enumerate every route by which content the app did not author reaches a model: uploaded files, retrieved passages, fetched pages, records from connected accounts, and the results of tool calls.
- 2 Keep the app's own rules in the trusted portion of the request and enclose untrusted content in a clearly delimited region labelled as material to be analysed rather than instructions to be obeyed.
- 3 Enforce what the model may reach outside the model. Tool permissions, tenant scoping, and rate limits must hold even when the model has been completely persuaded — an instruction telling the model not to delete things is not a control.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/prompt-injection-defense
AI Import Column Mapping
AI Import Column Mapping
Guess how an uploaded file's columns line up with the app's fields, then ask.
What it does
A proposed mapping from uploaded columns to destination fields, shown with samples and confirmed before the import runs.
How it works
- 1 Take the uploaded headers and a small sample of rows and propose a mapping to the destination fields. Match on exact and near-exact header names first and only consult the model for the columns that remain unresolved.
- 2 Constrain the candidate set to fields the current user and the importer are actually permitted to write. A mapping that targets a read-only, computed, or privileged field must never be offered, whatever the header says.
- 3 Show the proposal as a table: source column, sample values, destination field, confidence, and the transformation that will be applied. A user cannot approve a mapping they cannot see the effect of.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-import-column-mapping
Model Fallback
Model Fallback
Keep AI features working when the primary model provider fails.
What it does
A bounded retry path that reruns a failed AI task on a compatible alternative model and discloses the substitution.
How it works
- 1 Classify failures before reacting. Provider outages, capacity rejections, rate limiting, and timeouts are worth retrying elsewhere; a refusal, an invalid request, or an authentication error will fail identically on any destination and must surface immediately.
- 2 Honour rate limiting with backoff before moving on. Retrying instantly against a provider that asked you to wait deepens the outage and can extend the penalty period.
- 3 Allow fallback only to models that can honour the same output shape and the same tool behaviour as the original. A substitute that returns a different structure turns a provider outage into a data problem inside the app.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/model-fallback
How it works
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Copy the link
Grab the Markdown instruction URL for this feature.
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Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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It inspects, then implements
Your agent reads your existing app first, then adds the feature to fit it.
Works with your stack
These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.
Need it tighter than that? Customize the feature and tell it exactly what you're running.