Identification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection

This project is a research-oriented implementation of a standard data pipeline (Scrape -> Transcribe -> NER -> Anonymize). While the specific application to Telegram for cybercrime analysis is academically useful, the technical components are entirely commodity. The project currently has zero stars and minimal traction (3 forks), indicating it is likely a code release accompanying an academic paper. Defensibility is very low because the 'moat' consists of combining existing libraries like Whisper for STT and SpaCy/HuggingFace for NER—functionalities that are now integrated as native features in many LLM platforms and cloud services (e.g., Microsoft Presidio, Google Cloud DLP). Frontier labs and established players like Microsoft already offer robust, production-grade PII redaction services that outperform bespoke research scripts. Displacement is imminent as more advanced, multi-modal LLMs (like GPT-4o or Gemini) can handle transcription and entity redaction in a single pass with higher accuracy than the discrete pipeline proposed here.