Tackling ChatGPT Threats with Information Governance

Understanding AI-related risks and how to tackle them with information governance

Tackling ChatGPT Threats with Information Governance

OpenAI’s ChatGPT has gained massive popularity in the technology community and beyond. Since its launch last year, people from nearly every industry seem to be benefitting from its use, including Congressmen and government officials who are delivering speeches written by ChatGPT. However, most companies are resistant to using ChatGPT due to several reasons, including privacy concerns.

The Big Problem

ChatGPT and similar chatbots use machine learning and natural language processing (NLP) by replicating information collected from publicly available data on the internet to deliver human-like responses.

Since companies hold massive amounts of data including company-sensitive information, the big problem for most companies is to share it with the public version of the chatbots. Hence, most companies seek alternatives where they can feed the chatbots with only the data they want. Fortunately, OpenAI has offered a solution.

The ChatGPT Solution

As a solution to the data problem and privacy concerns, OpenAI recently announced the launch of its new platform called GPTs, which allows users to create custom versions of ChatGPT for specific use cases without coding.

The GPT Store will be accessible to ChatGPT Plus subscribers and enterprise customers, who can create internal-only GPTs for their employees. However, despite the new platform being a “no coding required” version, companies still face challenges.

Currently, while these AI tools are still evolving, companies face several challenges with their “no coding required” custom versions of ChatGPT:

  • The first challenge is to gather relevant data from across the enterprise. This initial search is a monumental task, as large companies hold petabytes of data in all file types. Finding relevant data is critical to the efficacy of AI but poses a technological hurdle.
  • The second challenge is to review and scrub data before feeding it to the AI application because any misleading, outdated, or sensitive information can lead to project failures, losses, or even fines.
  • The third challenge is to implement an information governance system, accounting for privacy, compliance, and legal requirements such as eDiscovery before feeding the data to the AI application.

Final Words

Custom-built, internal AI implementations can make many employees’ lives easier and unlock vast insights into the enterprise, but navigating through the associated challenges requires a scaled search capability and proper information governance. For more information on how to feed AI with targeted, governed information, please reach out to our experts here.

Bivek Minj graduated from the Indian Institute of Mass Communication with a degree in English Journalism. He serves as a Content Writer at ZL Tech India's Marketing department. He comes to the industry with a desire to learn and grow.