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DeClaw Security tool screenshot — openclaw.ai

DeClaw

DeClaw is a macOS-native local firewall for AI agents — a 100% local binary that sits between OpenClaw (or any AI app) and external AI providers, redacting PII before requests leave your machine and d

Added
4 weeks ago

About

DeClaw is a macOS-native local firewall for AI agents — a 100% local binary that sits between OpenClaw (or any AI app) and external AI providers, redacting PII before requests leave your machine and de-anonymizing responses on return. Detects SSNs, credit cards, emails, phone numbers, names, and prompt injection attempts. Pro tier adds invisible text detection and a skill scanner. Free tier available; Pro "coming soon." Apple Silicon-native, signed and notarized by Apple.

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Best For

Individual developers on macOS who run OpenClaw or other AI assistants locally and want to prevent accidental PII exposure in their AI conversations — particularly anyone who regularly shares sensitive documents or data with AI assistants and doesn't want raw personal information sent to cloud providers.

Pros & Cons

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Pros

  • check 100% local processing — no cloud, no servers, no middlemen; scanning and redaction happen in memory only
  • check Seamless PII loop: data redacted on the way out, de-anonymized on the way back — AI response feels unchanged to the user
  • check Prompt injection defense intercepts indirect injections (hidden in websites) before the agent acts on them
  • check Apple-signed and notarized — passes macOS security verification, reducing installation friction
  • check Works across all major AI providers (OpenAI, Anthropic, Gemini, Azure AI) — not OpenClaw-specific
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Cons

  • close macOS only — no Windows or Linux support; blocks adoption for developers on other platforms
  • close Local-first means protection only applies on the local machine — not useful for cloud-based or server-side agent deployments
  • close Pro tier (skill scanner, invisible text detection) is "coming soon" — current free tier is limited in scope
  • close PII detection is pattern-based — atypical formats or international PII patterns may escape detection
  • close Middleware approach adds a dependency in the request chain that could cause issues with some integrations

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