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The potential for data loss can bring a halt to GenAI initiatives. Existing DLP approaches have struggled to protect against the loss of unstructured sensitive data like intellectual property and source code. Security teams need to facilitate secure deployments rather than being a “department of no” that stops projects due to security concerns. |
数据丢失的可能性可能会阻碍GenAI的举措。现有的 DLP 方法一直难以防范知识产权和源代码等非结构化敏感数据的丢失。安全团队需要促进安全部署,而不是成为一个出于安全考虑而停止项目的 “不部门”。 |
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Harmonic Security achieves its results with an approach that lends itself to detecting and blocking unstructured data while avoiding alert noise. While AI is helping improve security solutions, large language models (LLMs) can be imprecise and incur latency that results in a poor user experience. The small language models used as part of the Harmonic solution provide precision as well as low latency to facilitate inline blocking where appropriate. |
Harmonic Security 采用的方法可以检测和屏蔽非结构化数据,同时避免警报噪音,从而实现其成果。虽然人工智能有助于改善安全解决方案,但大型语言模型 (LLM) 可能不精确,并且会产生延迟,从而导致用户体验不佳。作为 Harmonic 解决方案一部分的小型语言模型提供精度和低延迟,以便在适当时促进内联阻塞。 |
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With the new version of its tool, Harmonic claims to increase AI tool coverage by 30x to solve for the expanding AI tool ecosystem. As new AI tools crop up that may be sanctioned or risky, Harmonic provides visibility so security leaders can make optimal policy decisions. |
Harmonic声称通过其工具的新版本,将人工智能工具的覆盖范围提高了30倍,以解决不断扩展的人工智能工具生态系统问题。随着可能受到制裁或存在风险的新人工智能工具的出现,Harmonic提供了可见性,因此安全领导者可以做出最佳的政策决策。 |
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Harmonic has led the industry in addressing GenAI data loss concerns and continues to do so with the addition of features like the ability to address key file types and the ability to understand the accounts being used. For example, a sanctioned work account on ChatGPT Enterprise may be fine, but using a personal Gmail with an unapproved tool may violate policy. Harmonic allows users to shape their response based on the context. |
Harmonic在解决GenAI数据丢失问题方面一直处于业界领先地位,并将继续通过增加诸如解决关键文件类型和理解所用帐户等功能来实现这一目标。例如,在 ChatGPT 企业版上使用受制裁的工作账号可能没问题,但是使用带有未经批准的工具的个人 Gmail 可能会违反政策。Harmonic 允许用户根据上下文塑造他们的响应。 |