> For the complete documentation index, see [llms.txt](https://help.nightfall.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.nightfall.ai/faqs/nightfall_vs_legacy_dlp.md). # Nightfall vs Legacy DLP: What's the difference? Learn the difference between Nightfall DLP and legacy DLP solutions. Traditional data loss prevention (DLP) platforms focus primarily on securing data on endpoints (devices like laptops, phones, servers) or networks. As such, they don’t provide visibility into cloud applications and cloud data infrastructure that enterprises are rapidly moving towards. Nightfall is the industry’s first cloud-native DLP platform focused on detecting & protecting data in the cloud by integrating directly with these services via their APIs. Legacy DLP solutions are also limited in what they can recognize because they rely on traditional detection methods, such as regex rules and digital signatures/fingerprints. These have severe limitations in accuracy, usually resulting in a high volume of false positives, and significant alert fatigue for end-users. This means negative ROI and limited value. In contrast, Nightfall leverages machine learning to scan data and its surrounding context, meaning Nightfall can scan both structured & unstructured data with high accuracy. Because of this higher accuracy, customers often have Nightfall automate the response to data leakage events, yielding measurable time savings. Security, compliance, and engineering teams can focus on other problems, versus triaging alerts, getting hours back in their day. What was once negative ROI with traditional DLP is now positive ROI with Nightfall. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://help.nightfall.ai/faqs/nightfall_vs_legacy_dlp.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.