Published on October 25, 2023, 1:56 pm

TLDR: Enterprises are increasingly adopting AI solutions, specifically generative AI tools, to address security challenges. The use of AI in various forms has significantly increased across industry verticals, with manufacturing and finance leading the way. has emerged as a driving force behind this trend. However, there are risks associated with utilizing generative AI tools, including the release of sensitive information and data privacy/security risks. To mitigate these risks, organizations are likely to seek precise controls for their AI applications and prioritize visibility and access controls. Additionally, businesses will leverage AI to enhance data visibility and improve data hygiene practices for better data protection when using generative AI tools. Furthermore, enterprises are recognizing the role of AI in managing risk and security and expect it to fulfill key roles in strengthening security measures.

Now is the perfect time for enterprises to adopt AI solutions to address their present and future security challenges. The widespread implementation of generative AI tools has ushered in a new era of innovation across various industries, including IT, finance, marketing, and engineering. It’s clear that organizations are reconsidering their traditional approaches to unlock the transformative potential of AI.

Our data analysis on AI/ML trends and usage confirms that enterprises across different verticals have significantly increased their utilization of generative AI tools. This surge in adoption signifies a shift towards embracing AI in various forms. So, what can businesses learn from these trends? And what can we expect in terms of future enterprise developments related to generative AI?

Through our research and numerous conversations with customers and partners, we have identified several key trends that will shape the landscape of generative AI this year and beyond. Our findings reveal that across industry verticals, there has been a substantial growth in the use of AI from May 2023 to June 2023, with sustained momentum through August 2023. Manufacturing appears to be leading the way, demonstrating significant engagement with these tools and offering insights into the rapid innovation driven by Industry 4.0. This highlights the crucial role that AI and ML will likely play in shaping the future of manufacturing.

The finance sector is another industry witnessing remarkable growth in the use of AI/ML tools. This expansion can be attributed to the adoption of generative AI chat platforms like ChatGPT and Drift. Finance companies have experienced continuous growth in these areas since June 2023.

It comes as no surprise that has emerged as a driving force behind this trend, accounting for 36% of observed traffic related to AI/ML. Within this domain, ChatGPT alone accounts for 58% of traffic. However, when it comes to popularity among users, Drift takes center stage followed by ChatGPT and other tools such as LivePerson and Writer. As new use cases for AI continue to emerge, we can expect organizations to adopt AI not only for leveraging generative AI chat tools but also as a core driver of business that creates competitive differentiation.

Our research also reveals that as enterprises embrace AI/ML tools, they are rigorously scrutinizing subsequent transactions. Overall, 10% of AI/ML-related transactions are blocked by Zscaler’s cloud using URL filtering policies. The technology and finance industries lead in this regard, accounting for more than half of the blocked transactions. Interestingly, Drift is both the most blocked and the most used AI application. It is likely that we will see other industries following suit to minimize the risks associated with the use of AI and ML tools.

As discussed in our recent blog post, there are significant risks involved in utilizing generative AI tools within enterprises. These risks can be broadly categorized into two groups:

1. The release of intellectual property and non-public information: Generative AI tools have the potential to allow inadvertently sharing sensitive and confidential data. This data can then be used to train large language models (LLMs) and accessed by unauthorized individuals outside the organization.

2. Data privacy and security risks of AI applications themselves: Different AI applications have different terms and conditions regarding data usage. Enterprises must address questions surrounding data usage such as: Will their queries be used to train an LLM? Will their data be mined or sold to third parties? Additionally, enterprises must consider the overall security posture of these applications and how they secure data.

Considering these challenges, it is highly probable that enterprises will seek precise controls for their AI/ML applications moving forward. Many organizations will start by prioritizing visibility into their AI systems, followed by implementing intelligent access controls to ensure secure usage of these tools within approved parameters. Enterprises need answers to several crucial questions such as:

To effectively prevent data loss across their operations, organizations are likely to rely increasingly on AI for improved data identification and protection. One common challenge faced by enterprises is the lack of comprehensive visibility into all areas where critical data resides. This limited understanding makes it difficult for organizations to create effective policies to prevent data leakage when utilizing generative AI tools.

Thus, we anticipate that businesses will increasingly leverage AI to enhance data visibility and improve data hygiene practices. Machine learning techniques now enable automatic discovery and classification of sensitive data, including financial documents, legal records, personally identifiable information (PII), medical data, and more. From there, enterprises can employ these ML-driven data categories as the foundation for their Data Loss Prevention (DLP) policies. This ensures that data remains secure when using generative AI tools like ChatGPT.

In addition to transforming various business functions, enterprises are also recognizing the role AI plays in managing risk and security. While companies currently leverage AI to uncover insights across IT, technology, marketing, customer experience, and other areas, they will increasingly turn to AI and ML technologies to revolutionize their approach towards risk mitigation. Here are three key roles that AI is expected to fulfill in strengthening security:



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