Executive Summary

Around 200 million knowledge workers now experiment with LLM-driven chat tools and routinely paste code, configs, logs, or customer data into those interfaces, creating frequent accidental data-leak risks for developers, support agents, legal teams, and product managers. Even a single mistaken paste can trigger compliance violations or exposure of IP, so the problem is pervasive and practical rather than theoretical. The product is a paste sanitizer: a lightweight client-side browser and IDE extension that inspects clipboard contents in real time using a hybrid of deterministic patterns and small local ML models, redacts or warns on likely secrets, and offers one-click safe redaction or contextual paraphrasing before sending text to any chat. It emphasizes shift-left UX by surfacing fixes inline, enforces configurable enterprise policies for teams, and keeps processing on-device by default with an opt-in enterprise telemetry option for logs and audits. This is an attractive moment because the addressable market is roughly $12.0B (200M users × $60/yr average spend), market score is 90/100 and revenue potential 78/100, while competition remains low and adoption drivers like AI-chat usage, shift-left security expectations, and privacy-first UX are accelerating demand. The strongest differentiators are truly local processing, low-friction inline remediation, and easy enterprise integration, but be honest about challenges: platform fragmentation, tuning to minimize false positives, and the need for continuous rule/model updates to cover new leak patterns.

Market Opportunity

Prevent accidental AI-data leaks — paste sanitizer for chat inputs targets a $12.0B = 200M knowledge workers x $60/yr (average per-user spend for paste-protection/DLP features) total addressable market with low saturation and a year-over-year growth rate of 18% (security & DLP adjacent segments).

Key trends driving demand: AI-chat adoption -- more users are interacting with LLMs and pasting snippets, increasing accidental leaks; Shift-left security -- developers expect security tools earlier in the workflow, favoring client-side extensions; Privacy-first UX -- users prefer tools that prevent leaks locally without routing data to third-party servers.

Key competitors include GitGuardian, 1Password (Secrets Automation & Browser Extension), Open-source secret scanners (Gitleaks, TruffleHog, etc.), Microsoft Purview / Microsoft Defender (DLP for web browsers and Cloud Apps), Clipboard/text-cleaning utilities (ClipboardFusion, TextSoap, Ditto) — adjacent workarounds.

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Prevent accidental AI-data leaks — paste sanitizer for chat inputs

Executive Summary

Market Validation

Market Opportunity

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