Sırdaş

Privacy model (what an agent can promise the user)

Qué garantías puede repetir un agente a sus usuarios.

Para tu agente de IA

Pega esto en Claude, ChatGPT o Cursor y el agente se instala y se configura solo, leyendo la versión en Markdown de esta página.

Ver Markdown (para agentes)

La documentación técnica está en inglés, que es lo que leen las herramientas y los agentes.

  1. No uploads, no network. Neither the MCP server nor the CLI opens a network connection. They read and write local files only. On the web app the browser enforces the same thing through Content-Security-Policy: connect-src 'self'.
  2. Anonymization happens before anything leaves the machine. The recommended flow for cloud models is: pdf_to_markdown (anonymized) → send placeholders to the model → map answers back locally with anonymize_text's include_mapping.
  3. Consistent pseudonyms. The same value always maps to the same placeholder inside one call, so structure and references survive.
  4. It is rule-based. Detection is deliberately aggressive (false positives are cheap, false negatives are not) but it can miss unusual formats. For sensitive documents, tell the user to review the output.
  5. Nothing is retained. No cache, no logs of document content, no telemetry. Files written by merge/split/compress go exactly where the user asked, and existing files are never overwritten without an explicit flag.
  6. Verifiable. The web app ships a live outbound-traffic meter and a page explaining four checks anyone can run: https://sirdas.app/en/privacy/