Nyx functions as an agentic Data Loss Prevention (DLP) analyst, offering advanced capabilities beyond traditional DLP solutions. It is designed to understand context, intent, and risk, similar to a human security analyst, thereby reducing the volume of false positives that often overwhelm security teams. Nyx is embedded within the Nightfall Console, providing comprehensive visibility into policies, users, files, domains, and violations. A key feature is its conversational intelligence, allowing users to ask questions in natural language and receive insights, summaries, and recommendations without complex querying or dashboard configuration. Nyx identifies suspicious patterns across users, devices, and destinations that rule-based systems might miss, and connects disparate events to highlight coordinated exfiltration attempts or unusual access patterns. It suggests smart actions, such as disabling accounts, blocking file uploads, or revising policies, based on its contextual understanding. Nyx acts as a 24/7 partner for autonomous investigation, allowing users to set goals rather than rigid rules, as it understands intent and context. It provides on-demand forensics and analytics, enabling users to generate incident summaries, create reports, and analyze trends through natural conversation. As Nyx learns an organization's environment, its AI-driven insights and recommendations become sharper and more precise, adapting in real-time to evolving risks.

Features & Benefits

• Agentic DLP Analysis: Investigates, reasons, and acts with the judgment of an expert analyst and the context of a co-worker.
• Conversational Intelligence: Enables natural language querying for insights, summaries, and recommendations.
• Contextual Understanding: AI understands context, intent, and risk, reducing false positives and identifying hidden patterns.
• Autonomous Investigation: Acts as a 24/7 partner for incident investigation, suggesting smart actions and adapting in real-time.
• Goal-Oriented Approach: Focuses on understanding intent and context rather than requiring policies for every edge case.