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Organizations are looking to benefit from the productivity gains of generative AI, particularly ChatGPT, but concerns remain about the security risks associated with confidential data being leaked into large language models (LLMs). AI governance and risk management discussions with boards of directors are increasingly focusing on this issue.

To address this, some organizations are taking an education-centric approach. Alex Philips, CIO at National Oilwell Varco (NOV), believes that ongoing education about the advantages, risks, and current state of gen AI technologies helps set expectations and allows for the implementation of safeguards to prevent data leaks.

In the healthcare sector, several CISOs and CIOs are limiting access to ChatGPT across research and development, pricing, and licensing units. They are divided on how to manage the security threat of confidential data ending up in LLMs. The risks to intellectual property, pricing, and licensing outweigh the competitive disadvantage of not having gen AI as a research tool.

To balance productivity and security, Nightfall AI has launched the first data security platform specifically designed for gen AI. The platform offers data loss prevention (DLP) across API, browser, and SaaS applications. It aims to enable organizations to leverage the benefits of AI while protecting sensitive data and reducing risk.

Nightfall AI’s platform includes Nightfall for ChatGPT, which provides real-time scanning and redaction of sensitive data entered into chatbots. Nightfall for LLMs is a developer API that detects and redacts data used to train LLMs. Nightfall for SaaS offers data leak prevention within popular SaaS applications. These products have been integrated by companies like Genesys, MovableInk, Aaron’s, and Klaviyo.

Overall, ensuring the security of confidential data while harnessing the productivity gains of generative AI is crucial. By implementing platforms like Nightfall AI’s, organizations can strike a balance and protect their sensitive data in the age of AI.

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