nlu和生成式人工智能对网络防御系统发展的影响

I. Sukaylo, Nataliia Korshun
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引用次数: 0

摘要

网络安全系统与人工智能的结合是当前信息技术发展的必然趋势。如今,许多网络安全供应商正在将机器学习和人工智能整合到他们的产品或服务中。然而,就从这些产品中产生有意义的可衡量结果而言,投资于先进机器学习和深度学习技术的有效性是一个有争议的问题。在设计这样的系统时,存在实现精度和缩放的问题。本文考虑了人工智能系统的分类、安全产品使用的人工智能模型、其功能,并给出了在网络保护系统中使用生成式人工智能技术时应考虑的建议。ChatGPT的NLP功能可用于简化安全产品中的策略配置。通过人工智能考虑短期和长期指标来衡量进展、差异化和客户价值的方法是合适的。此外,还讨论了基于平台解决方案的生成式人工智能的使用问题,该解决方案可以聚合各种用户数据,在大型社区之间交换想法和经验,并处理高质量的遥测数据。由于网络效应,有机会重新训练人工智能模型,提高所有用户的网络防御效率。这些好处带来了用户参与度增加和网络安全结果改善的虚拟循环,使基于平台的安全解决方案成为企业和个人的一个有吸引力的选择。在使用人工智能对任何IT基础设施进行网络安全审计时,审计的限制和深度是根据以往的经验确定的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
THE INFLUENCE OF NLU AND GENERATIVE AI ON THE DEVELOPMENT OF CYBER DEFENSE SYSTEMS
The combination of cyber security systems and artificial intelligence is a logical step at this stage of information technology development. Today, many cybersecurity vendors are incorporating machine learning and artificial intelligence into their products or services. However, the effectiveness of investments in advanced machine learning and deep learning technologies in terms of generating meaningful measurable results from these products is a matter of debate. When designing such systems, there are problems with achieving accuracy and scaling. The article considers the classification of artificial intelligence systems, artificial intelligence models used by security products, their capabilities, recommendations that should be taken into account when using generative artificial intelligence technologies for cyber protection systems are given. ChatGPT's NLP capabilities can be used to simplify the configuration of policies in security products. An approach that considers both short-term and long-term metrics to measure progress, differentiation, and customer value through AI is appropriate. The issue of using generative AI based on platform solutions, which allows aggregating various user data, exchanging ideas and experience among a large community, and processing high-quality telemetry data, is also considered. Thanks to the network effect, there is an opportunity to retrain AI models and improve the effectiveness of cyber defense for all users. These benefits lead to a virtual cycle of increased user engagement and improved cyber security outcomes, making platform-based security solutions an attractive choice for businesses and individuals alike. When conducting a cyber security audit of any IT infrastructure using AI, the limits and depth of the audit are established taking into account previous experience.
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