"Generative AI platforms like ChatGPT, Gemini, Copilot, and Claude are increasingly common in organizations. While these solutions improve efficiency across tasks, they also present new data leak prevention for generative AI challenges. Sensitive information may be shared through chat prompts, files uploaded for AI-driven summarization, or browser plugins that bypass familiar security controls. Standard DLP products often fail to register these events."
"Data loss prevention for generative AI requires shifting focus from endpoints and siloed channels to visibility across the entire traffic path. Unlike earlier tools that rely on scanning emails or storage shares, NDR technologies like Fidelis identify threats as they traverse the network, analyzing traffic patterns even if the content is encrypted. The critical concern is not just who created the data, but when and how it leaves the organization's control, whether through direct uploads, conversational queries, or integrated AI features in business systems."
Generative AI platforms are widely used in organizations and create new vectors for sensitive data exposure through prompts, uploaded files, and browser plugins that can circumvent security controls. Traditional DLP tools focused on endpoints and channel-specific scanning often miss these events. Network Detection and Response (NDR) provides visibility across the traffic path, enabling detection of GenAI-related data flows and analysis of traffic patterns even when content is encrypted. NDR enables policy enforcement, alerts, and captures full packet data for forensic analysis. URL-based indicators, tailored rules per service or department, and sensors across web and mail provide complementary monitoring approaches.
Read at The Hacker News
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