What is PII (Personal Info) Detector?
Sensitive data often hides in ordinary text. A support log may contain an email and phone number, a pasted config can expose keys, and a customer note might include account, payment, or address fragments that should not travel any further in plain form. In practice, teams often discover those details too late, after copying text into a ticket, chat, or document that was meant to be broadly shared.
PII Detector brings that first review step into the browser. It uses local recognizer logic inspired by Presidio-style pattern detection to flag common structured identifiers and create a masked copy on-device, without sending the source text to the app server.
Sensitive details are easy to miss in routine text workflows
Internal notes, exported CSV rows, incident summaries, and support transcripts often contain personal or secret-like strings mixed into otherwise ordinary text.
Manual review is slow, especially when the goal is simply to remove obvious structured identifiers before sharing a draft with someone else.
Sending text into a remote audit tool just for a quick hygiene pass can create a second privacy concern if the original document is already sensitive.
Before posting into chat, documentation, tickets, or LLM prompts, it is useful to run a local pass that catches common patterns and helps create a safer copy.
Local recognizers plus masked-output generation
This tool scans text in the browser for common structured PII and secret-like patterns such as emails, phone numbers, payment card strings, IPs, URLs, and credential-style tokens.
After detection, it can generate a masked copy using label replacement, block masking, or partial reveal depending on how much context you still want to preserve.
The findings panel makes review practical by showing type counts, individual snippets, and character ranges so you can sanity-check the result before exporting or sharing.
How to Use PII (Personal Info) Detector
- 1Load the source text - Paste notes, logs, contracts, or exported text directly into the browser, or import a text-based file from your device.
- 2Choose a masking style - Pick label replacement, full blocking, or partial reveal depending on your review and sharing needs.
- 3Run the local scan - Let the browser inspect the text for common sensitive patterns without sending the document to app infrastructure.
- 4Review findings by type - Check the summary and per-match results to understand what kinds of values were flagged.
- 5Copy the masked version - Use the generated redacted copy for downstream sharing, drafting, or handoff once it looks appropriate.
Key Features
- Presidio-style local recognizers for common sensitive text patterns
- Private browser-side scanning with no app-server document upload
- Masked output generation using label, block, or partial-reveal strategies
- Per-type findings summary plus match-by-match review
- Offline-friendly route with service-worker support after shell caching
Benefits
- Catch obvious sensitive details before sharing notes, tickets, exports, or drafts
- Keep source documents on-device during review and masking
- Generate a safer redacted copy quickly without opening a full DLP platform
- Use deterministic local logic for repeatable document hygiene checks
Use cases
Support-ticket cleanup
Remove obvious emails, phone numbers, or account-like strings before sharing logs with a wider internal audience.
Prompt hygiene
Prepare notes or transcripts for local or hosted AI tools after masking common identifiers first.
Draft redaction
Create a safer copy of contracts, exports, or reports before sending them into review.
Developer audit
Scan snippets for keys, URLs, IPs, and tokens before pasting them into issues or documentation.
Tips and common mistakes
Tips
- Use the findings list as a review aid instead of assuming every match should always be kept or removed automatically.
- Block or label masking is usually safer than partial reveal when text will be shared outside a tightly controlled group.
- Run the detector before posting logs, transcripts, or exports into collaboration tools where copy-paste spreads quickly.
- Treat the masked output as a draft for review, especially when documents might contain names or custom identifiers that pattern logic cannot infer.
Common mistakes
- Assuming pattern-based local detection can replace a full legal, compliance, or enterprise DLP workflow.
- Treating the absence of findings as proof that the text contains no sensitive information at all.
- Using partial reveal when the output is meant for broad or public distribution.
- Forgetting that custom IDs, names, or domain-specific secrets may require manual review even after the local scan is complete.
Educational notes
- Pattern-based PII detection is strongest on structured identifiers such as emails, phone numbers, card formats, and token shapes, not on every sensitive concept expressed in free text.
- Masking strategy matters: label replacement reduces disclosure, block masking preserves approximate length, and partial reveal intentionally keeps some context for internal review.
- Running the first audit pass locally reduces exposure of raw text to outside infrastructure, but it does not eliminate the need for human judgment on important documents.
- Document hygiene is most effective when local pattern scanning is combined with review habits, least-privilege sharing, and careful downstream storage decisions.
Frequently Asked Questions
Does the source text leave my device?
No. The detector runs inside the browser and keeps the source text on-device during scanning and masking.
Can it find every kind of sensitive data?
No. It focuses on common structured patterns and should be treated as a practical helper, not a guarantee of complete coverage.
When should I use label masking instead of partial reveal?
Label masking is usually better when the audience does not need to preserve the shape or last digits of the original value.
Is this useful before sharing prompts or logs?
Yes. It is well suited to a first-pass cleanup step before pasting text into tickets, chats, docs, or AI workflows.
Can I export the result?
Yes. You can copy the masked text directly and download a JSON report of the latest findings.
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