Redact logs before sharing them with support or AI

Logs are easy to share too early. A support ticket, an AI prompt, a GitHub issue, or a teammate message can all turn into the same problem: useful error text mixed with private details.

KikuAI Local Data Masker is a small browser tool for the first pass. Paste text, or load a .txt or .csv file. The tool masks obvious private values and gives you a masked copy plus a short Markdown report.

It is not a compliance system. It is not a promise that the text is clean. It is a quick way to remove common values before you review and share.

Use this if

Use it when you need to share:

• logs with support;
• snippets with an AI tool;
• a CSV sample with a teammate;
• a stack trace in a GitHub issue;
• a small text sample that may contain secrets;
• a cleaned example while keeping the error context.

The goal is simple: keep the useful diagnostic text, remove obvious values that should not travel with it, then review the result yourself.

What it masks

The current tool looks for common patterns:

• email addresses;
• phone numbers;
• Luhn-valid card-like numbers;
• API-key-looking strings;
• bearer tokens;
• JWT-like strings;
• simple secret assignments such as password = ... or token: ....

For CSV files, matching cells are masked while the table shape stays stable. Row and column counts stay in place, so the cleaned file is easier to compare with the original.

The Markdown report shows counts and review notes. It does not include the original text or masked text.

Do not use this if

Do not use it as your only redaction step for high-risk material.

It can miss or over-mask. It is based on patterns and heuristics, not full understanding of your business data.

It is not the right tool for:

• PDFs, Word files, images, or databases;
• names and addresses that need context;
• custom account IDs;
• custom secret formats;
• legal or compliance review;
• a guarantee that no private data remains.

No findings does not mean safe. It means this tool did not find one of the patterns it knows.

Simple workflow

1. Open Local Data Masker.
2. Paste text or load a .txt or .csv file.
3. Run the masker.
4. Check the preview.
5. Download the masked file or copy the result.
6. Download the Markdown report if you need a review trail.
7. Read the output before sharing it.

That last step matters. Local processing helps you keep control, but it does not replace human review.

Alternatives and trade-offs

Tool	Shape	Good for	Watch out for
KikuAI Local Data Masker	Browser tool	Quick masking of logs, text, and CSV before sharing	Smaller pattern set, review required
Microsoft Presidio	SDK/framework	Custom PII detection and anonymization pipelines	More setup than a quick browser pass
CSV Anonymizer	Browser CSV app	CSV-only client-side masking	Less useful for mixed logs and free text
maskcsv	CLI	Scripted field-based CSV masking	Command-line workflow
Redactomatic	Python/Streamlit tool	CSV conversation and call-log cleanup	More setup than a small browser tool
Zoro	CLI/.NET library	CSV, JSON, and database masking workflows	Better for configured pipelines than one-off sharing
City of Helsinki text-anonymizer	CLI/API/web app	Text and CSV anonymization in its target context	May be language/context-specific
City of Helsinki tabular-anonymizer	Python/Pandas library	DataFrame-level anonymization	Code-first workflow

The difference is not “which tool is best.” The difference is shape. Some tools are libraries. Some are CLIs. Some are broader anonymization systems. KikuAI Local Data Masker sits in the narrow place: one small browser-first pass before you share something.

FAQ

Does it catch everything?

No. It can miss names, addresses, account IDs, custom tokens, and free-form private notes.

Can I trust a blank report?

No. A blank report is not proof that the text is safe.

Does it work with CSV?

Yes. It can mask matching cells and preserve row and column counts.

Does it work with PDFs or images?

No. Use pasted text, .txt, or .csv.

Can I use the cleaned text with AI tools?

You can use it as a safer first pass, but review it before pasting. Do not assume every private value was found.

What if a secret was already committed to GitHub?

Use GitHub’s sensitive-data removal guidance. This masker is a step before sharing, not a repository-history cleanup tool.

Try it

Open the tool: Local Data Masker.

Source reference: kiku-jw/masker.