Jazz is an AI-native Data Loss Prevention (DLP) platform designed to replace traditional rule-based DLP systems with an agentic, context-aware approach to detecting and preventing data loss. Core approach: - Instead of requiring analysts to write rules or chase alerts, Jazz uses an AI-driven investigator called "Melody" that autonomously analyzes signals and surfaces only meaningful incidents. - The platform investigates across four dimensions of context: data, systems, people, and business, to provide full-story incident context rather than raw alerts. Key capabilities: - Infinite coverage: Captures activity across an organization's entire environment, including GenAI tools, desktop activity, shadow apps, and homegrown applications. - Agentic investigation: Melody, Jazz's built-in AI investigator, pre-investigates signals and delivers context-rich answers to analysts, reducing false positives. - Risk mapping: Identifies risky behavior patterns and surfaces insights and suggestions for remediation. - Precise prevention: Makes contextual, in-context decisions to block, nudge, or request justification from users, minimizing disruption to business operations. - Privacy controls: Provides granular control over what is monitored, investigated, retained, and presented to analysts. Operational characteristics: - Deploys in minutes with no rule writing required. - Operates in plain language, enabling a single analyst to manage the full DLP program. - Claims to reduce false positives by up to 99% and achieve record-breaking mean time to respond (MTTR). - No external integrations required for deployment.