Secuvy is a data discovery and classification platform that uses AI to identify and protect sensitive and complex data across structured and unstructured sources. Unlike pattern-matching approaches that rely on manual regex tuning, Secuvy uses a self-learning data scanner to automatically review and classify discovered data signals with contextual understanding. Key use cases include: - Discovery of complex unstructured and semi-structured data (e.g., XML, JSON) including proprietary data types such as intellectual property, financial disclosures, loan documents, medical notes, clinical trials, genomic data, and controlled unclassified information (CUI), ITAR, EAR, and NARA records (relevant for CMMC 2.0 compliance) - DLP/CASB/SASE policy gap assessments, with smart policy tuning based on self-learned suggestions synced across the security stack, aimed at reducing policy tuning effort by up to 90% - AI governance via an MCP (Model Context Protocol) server that auto-classifies data and prevents data leakage, with support for inference and RAG guardrails The platform is designed for reduced operational overhead, with guided rollout targeting as low as 0.2 FTE for ongoing maintenance and deployment in as little as two weeks.