New Blog | Navigating New Application Security Challenges Posed By GenAI
By Asaf Harari
GenAI applications—software powered by large language models (LLMs) are changing the way we interact with digital platforms. These advanced applications are designed to understand, interpret, and generate human-like text, code and various forms of media, making our digital experiences more seamless and personalized than ever before. With the increasing availability of LLMs, we can expect to see even more innovative applications of this technology in the future. However, it is important to carefully consider the potential security challenges related to GenAI applications and the underline LLM.
While security challenges in machine learning models have been studied for some time, such as the potential for adversarial attacks where input data can be manipulated to mislead the model. However, challenges specific to LLMs are still relatively unexplored and pose a blind spot for researchers and practitioners. LLMs are distinct from other software tools and machine learning elements in terms of their functionality, the way GenAI applications employ them, and the way users engage with them. For these reasons, for the development and use of GenAI applications, it’s crucial to implement GenAI security best practices, Zero Trust architecture, posture management solutions, and conduct red team exercises. Microsoft is at the forefront of not only deploying GenAI applications but also ensuring the security of these applications, their related data, and their users.
In this blog post, we will review the unique cybersecurity challenges that GenAI apps and the underline LLMs pose according to their special behavior, their unique use, and their interaction with the users.
Read the full post here: Navigating New Application Security Challenges Posed By GenAI
By Asaf Harari
GenAI applications—software powered by large language models (LLMs) are changing the way we interact with digital platforms. These advanced applications are designed to understand, interpret, and generate human-like text, code and various forms of media, making our digital experiences more seamless and personalized than ever before. With the increasing availability of LLMs, we can expect to see even more innovative applications of this technology in the future. However, it is important to carefully consider the potential security challenges related to GenAI applications and the underline LLM.
While security challenges in machine learning models have been studied for some time, such as the potential for adversarial attacks where input data can be manipulated to mislead the model. However, challenges specific to LLMs are still relatively unexplored and pose a blind spot for researchers and practitioners. LLMs are distinct from other software tools and machine learning elements in terms of their functionality, the way GenAI applications employ them, and the way users engage with them. For these reasons, for the development and use of GenAI applications, it’s crucial to implement GenAI security best practices, Zero Trust architecture, posture management solutions, and conduct red team exercises. Microsoft is at the forefront of not only deploying GenAI applications but also ensuring the security of these applications, their related data, and their users.
In this blog post, we will review the unique cybersecurity challenges that GenAI apps and the underline LLMs pose according to their special behavior, their unique use, and their interaction with the users.
Read the full post here: Navigating New Application Security Challenges Posed By GenAI