Stop accidental AI prompt leaks with real-time PII redaction

Executive Summary

Accidental leaks of personally identifiable information (PII) and sensitive corporate data occur routinely as employees paste prompts into third‑party LLMs, share screens during remote demos, or save AI transcripts—exposing organizations to regulatory fines, reputational harm, and IP loss. This problem touches security and compliance teams across enterprises and the roughly 200 million knowledge workers who are adopting generative AI in everyday workflows, creating a growing and under‑addressed exposure vector. You could build a real‑time PII redaction platform that combines client‑side OCR and entity detection with optional cloud moderation/embedding APIs to mask or replace sensitive fields before text is submitted to an LLM or shown during screen sharing. Delivered as a browser extension, desktop agent, and SDK, the product would include centralized policy controls, audit logs, and connectors to existing DLP, IAM and SIEM systems so security teams can enforce rules without disrupting user workflows. The market is attractive now: an addressable opportunity of about $10.0B (200M knowledge workers × $50 ARPU/year), a high market score (90/100) and revenue potential (86/100), coupled with accelerating generative‑AI adoption, increased remote collaboration, and the availability of real‑time moderation APIs that make inline redaction technically feasible. To stand out, focus on a privacy‑first, low‑latency architecture (local pre‑processing with selective cloud validation), enterprise integrations, strong auditability, and developer SDKs so the solution is both effective and easy to adopt; current competition is low, which favors early movers. Be honest about the hard parts: tuning detection to avoid false positives/negatives, minimizing performance impact across platforms, and earning enterprise certifications and buyer trust will require disciplined engineering and sales execution.