The Future of Data Privacy: Preparing for the Impact of Large Language Models

Introduction to LLMs

A technological shift occurs approximately every decade. These important movements usher in new advancements such as electricity, radio, TV, cable, PC, internet, WWW, smartphones, and currently, artificial intelligence and machine learning. Globalization has brought improvements to our lives through these technological shifts. However, with each new era, unforeseen risks often arise for which we are unprepared as humans. The latest wave of artificial intelligence technologies, including large language models (LLMs) such as GPT, Bard, LLaMa, BERT, and Claude, has pushed the boundaries and allowed us to accomplish tasks in seconds that were previously unimaginable. The new AI language models provide human-like answers when asked a question or accomplish a task. These LLMs have become our new friends, vying for our favor in a competitive race to offer their best capabilities in various domains, such as marketing, sales, finance, engineering, healthcare, and others, propelling us to new heights in our personal and professional lives.

Employee efficiency & sensitive relationships with LLMs

With the capabilities of modern LLMs, we can achieve incredible results while saving time on analysis and decision-making. These models process information at an impressive speed, allowing us to feel like superheroes and geniuses. These models enable organizations to leverage AI as a strategic advantage in their operations. The scalability and efficiency of LLMs allow professionals to accomplish tasks easily that were previously complex and time consuming, unleashing the full power of human intelligence in the modern workforce. Those who tailor the process of integrating these sensitive “humanorobotic” relationships will become winners.

Data exposure & risks

According to Cyberhaven’s product team report, approximately 8.2% of employees have utilized ChatGPT in their workplace, with 6.5% admitting to pasting company data into the tool since its launch. It’s safe to assume that employees are also withholding admitting this, therefore that percentage is likely significantly higher. Several knowledge workers have reported that leveraging ChatGPT has increased their productivity by up to 10 times. However, it’s important to consider potential concerns and challenges associated with the use of LLMs. The entire country of Italy banned ChatGPT and other LLM models due to concerns about data privacy and security. The inadvertent exposure of sensitive information during the use of ChatGPT and similar LLM engines could pose risks to privacy and security, as this data may potentially be used to train the models. ChatGPT retains user data for model training purposes and may not erase it even after turning off the computer or deleting a prompt. As per its terms of use policy, ChatGPT’s employees may also have access to this data.

Adopting LLMs vs. prohibiting

As we are still in the early stages of the LLM revolution, it’s difficult to accurately predict the impact of prohibiting the use of LLMs. While it may not unlock superpowers for organizations immediately, some firms have managed to operate efficiently without relying on ChatGPT and LLaMas. However, LLMs will inevitably become integral to the modern workforce. Proactively adapting to these trends can improve employee retention, loyalty, and productivity, while also addressing potential concerns related to data privacy, security, and ethical considerations. Employee effectiveness has to lead the shift.

Multi-LLM environment

Similar to the blooming revolution of cloud computing, organizations are eagerly embracing the potential of combining multiple Language Model Models (LLMs) to magnify their capabilities and optimize for best performance. However, as these powerful technologies become an integral part of business operations, protecting the integrity and confidentiality of sensitive data, including intellectual property and other critical metrics, takes center stage. Moonlight, an AI-powered data protection platform, is at the forefront of addressing this challenge. With its automated and real-time data masking capabilities, Moonlight prevents sensitive information from being exposed to LLMs, ensuring data privacy and security while seamlessly integrating into the data flow.

Conclusion

As Language Model Models (LLMs) continue to gain traction in optimizing efficiency, it’s no longer a question of ‘if,’ but rather ‘when’ they become the norm, much like social media and video streaming in their respective eras. It’s crucial to carefully evaluate the ability to control data on premises while providing a seamless user experience. If there are weak links in the “humanorobotic” relationship, such as security policies of an organization, the opportunity cost may be too high, resulting in potential loss of competitive advantage and increased employee turnover.

Stay ahead of the curve with Moonlight and its automated real-time data masking capabilities.

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