{"categories":[{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing/3"},{"label":"Security","url":"https://skillfed.io/packages/category/security/3"}],"enrichment":{"capability":"Detects and redacts personally identifiable information (PII) in text using regex, NER, and optional local LLM layers, replacing sensitive data with reversible pseudonyms while preserving meaning for downstream AI systems.","skillfed_tags":["pii-detection","llm-privacy","reversible-pseudonymization"],"use_cases":["Redact user queries before sending to a third-party LLM API, then restore the response in a guarded round-trip to keep PII local.","Audit text for privacy risk before it enters a data pipeline, using the risk-scoring API to flag documents with critical or high-severity PII.","Preprocess customer support tickets or medical notes for internal AI analysis while ensuring sensitive fields never cross the network boundary.","Generate synthetic training data by redacting real documents and replacing pseudonyms with plausible alternatives, preserving linguistic patterns.","Implement a privacy gateway that runs 'fast' mode inline for low-latency LLM proxies and 'auto' mode asynchronously in a parallel audit lane."],"what_it_does":"argus-redact is a local PII detection and pseudonymization library designed to sit between user input and large language models. It scans text through up to three collaborative layers\u2014fast regex matching, statistical NER, and optional local LLM inference\u2014to identify sensitive data (names, phone numbers, ID numbers, medical terms, and 78 other PII types) and replace them with reversible pseudonyms. Each call generates fresh random keys, so the same original data produces different pseudonyms every time, protecting against fixed-pseudonym deanonymization attacks.\n\nThe library preserves semantic meaning: names become codes like 'P-83811', phone numbers show partial digits ('138****5678'), and ID numbers become type-prefixed codes ('ID-03292'). You can restore the original text verbatim using the per-message key, or let the LLM work with pseudonymized data. Three deployment modes trade latency for detection depth: 'fast' (sub-millisecond, regex only), 'ner' (10\u2013100ms, adds entity recognition), and 'auto' (20s, includes local LLM). Chinese receives deepest support; English, Japanese, Korean, German, Ukrainian, and Indonesian add regex plus NER; Brazilian Portuguese is regex-only.","worth_installing":"Yes, with conditions. Install if you need reversible, per-message-key pseudonymization for LLM pipelines and can tolerate medium install friction. Actively maintained, Apache-2.0 licensed, no known vulnerabilities. Do not rely on it as a compliance anonymization tool\u2014it is a data minimization aid. Fast mode will miss obfuscated or novel PII variants; NER and LLM modes are statistical. Restore without a guarded anchor is not secure. Best for workflows where you control both redaction and restoration."},"id":"argus-redact","links":{"html":"https://skillfed.io/packages/argus-redact","md":"https://skillfed.io/packages/argus-redact.md","pypi":"https://pypi.org/project/argus-redact/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"argus-redact","python_support":"supports_current","summary":"Encrypt PII, not meaning. Locally."},"popularity":{"monthly_downloads":89615,"position":13644,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.14"}