# LogosGuard: local redaction for AI tools

Canonical: https://mudpie.ai/companies/logosguard/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [LogosGuard: local redaction for AI tools](https://mudpie.ai/companies/logosguard/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

## What it does

LogosGuard is a sensitive-data redaction layer for AI tools. It replaces names, addresses, account numbers and other private values with placeholders before the prompt leaves the browser, while preserving enough structure for ChatGPT, Claude, Gemini and other tools to work. The [YC profile](https://www.ycombinator.com/companies/logosguard) also describes a broader enterprise governance product for controls, red-teaming and continuous monitoring; the [current homepage](https://logosguard.com/) leads with the local browser/desktop privacy layer.

The fit is an individual or organization that wants a practical boundary between internal data and external AI tools. LogosGuard is especially relevant where the problem is accidental copy/paste rather than a full model-governance program.

## Why I’d look closer

The homepage makes a clear privacy claim: detection and redaction run locally in the browser, no account or servers are needed for the free extension, and the AI tool receives placeholders rather than the original values. The [trial page](https://logosguard.com/trial) lists Free at $0, Pro at $5/month billed annually and Enterprise custom. Pro includes unlimited redactions, file redactions, audit logs and alias history; Enterprise adds team policies, SSO/SAML/SCIM, SIEM export and on-prem/VPC options.

The [YC founder biographies](https://www.ycombinator.com/companies/logosguard) describe Sukrut Oak and Abel John with Stanford AI, Apple and SpaceX/AI-tax backgrounds. The launch extends the thesis to NIST AI RMF, ISO 42001, vendor risk and continuous AI-feature testing; that enterprise governance scope should be evaluated separately from the local redaction extension.

## What I’d ask

Which sensitive types and file formats are covered, how are false positives corrected, and what happens when a placeholder changes model behavior? I’d inspect local processing, alias history, export controls and enterprise policy enforcement with synthetic data before putting confidential prompts through it.

## My editorial take

LogosGuard is a useful fit for teams that need a simple “before it leaves the device” safeguard. The free/Pro path makes it easy to test. Enterprise buyers should separately validate the governance and deployment claims rather than assume the browser extension is a complete AI-risk program.

## Quick facts

| Field | Sourced detail |
|---|---|
| Buyer fit | Individuals, teams and enterprises using external AI tools |
| Public pricing | Free; Pro $5/month billed annually; Enterprise custom |
| Privacy claim | Local browser redaction before prompt submission; company-stated |
| Enterprise controls | Policies, SSO/SAML/SCIM, SIEM export and VPC/on-prem options |

## Sources checked

Checked 2026-09-19.

| Source | Used for |
|---|---|
| [YC company profile](https://www.ycombinator.com/companies/logosguard) | Product, founders and governance thesis |
| [LogosGuard homepage](https://logosguard.com/) | Current local-redaction workflow |
| [LogosGuard trial/pricing](https://logosguard.com/trial) | Plans and enterprise controls |

## Cohort context

LogosGuard is listed in Fall 2025. In our 2026-09-18 directory snapshot, 90 of 146 listed companies in that cohort have YC’s primary industry label B2B (61.6%). This is a current-directory comparison, not an original intake count or a performance ranking. [Nine-cohort dataset](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:15:07.842Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Not observed in this response |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |

[Public observations](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.