KavachAI: A Privacy-Preserving Desktop System for Multimodal Sensitive Data Detection and Redaction

Sensitive information can appear in text documents, images, and audio recordings, making it difficult for users to protect personal data before sharing digital content. This paper presents KavachAI, a Windows desktop application designed to integrate multimodal sensitive-data detection, redaction, and protected local storage within a unified workflow. The system combines regular-expression matching and named-entity recognition (NER) for text analysis, optical character recognition (OCR) for extracting text from images, and locally executed speech-to-text transcription for processing audio recordings. Extracted text from images and audio is passed through a shared detection pipeline to identify configured sensitive-data patterns and supported named entities. The application provides text masking, visual image redaction, and protection of audio-derived transcripts. Selected retained outputs are encrypted using AES-256 in Galois/Counter Mode (AES-256-GCM), while the Windows Data Protection API (DPAPI) protects the associated encryption key. SQLite supports local application data and scanhistory management. The system is implemented using Electron, React, and a locally hosted FastAPI backend with Pythonbased processing components. This paper describes the system architecture, implementation, privacy boundaries, and functional verification approach. Quantitative evaluation of detection accuracy, processing latency, and redaction completeness requires a separately documented and controlled assessment