How Can Companies Protect their Data from Misuse by LLMs? 

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Large Language Models (LLMs) have driven huge efficiencies and opened up new streams of innovation for a range of businesses, yet they have also given rise to significant concerns around privacy and safety with respect to their use. Rogue outputs, poor implementations within business operations, the safety of information and more are all valid concerns. While the outputs of such models are a concern, the true essence of the problem lies in the initial stages of LLM model development and data input. 

Ensuring that data is kept safe and protected all boils down to building strong foundations that place safety at the forefront. In other words, safety needs to be considered at the build and input stage of LLM model development, rather than at the output stage. 

The role of LLMOps models 

Unlocking success starts with the building blocks of an AI model, and this is where LLMOps is key. Developing a structured framework that securely stores and processes data at scale, and is able to safely draw data from other locations, ensures language models can’t misinterpret information, expose confidential data or generate potentially harmful answers.  

It’s a well-known fact that building an LLM application without a well-defined Ops model is relatively easy, but this should serve as a warning for businesses. In the absence of a well-considered, structured Ops model, the infrastructure that underpins LLMs and AI applications soon becomes challenging to engineer and maintain in production. Unsurprisingly, this is where things start to go wrong – the wrong data is utilised and exposed, and models go rogue. 

Likewise, these outputs soon become outdated as the process of continuous retraining and adaptation becomes an uphill battle. LLM models are trained with static data uploads, also known as batch data, that offers a single data snapshot from a particular period of time. The accuracy of the LLM model output is then compromised if the data changes until the next batch upload when the relevant data points are udpated, making the application unsuitable for real-time applications. 

Without proper maintenance and updates, these models are far more likely to interpret data in any way they can, producing outcomes biased from their perception of the past that hasn’t been actualized. Unlike humans who are able to think critically, problem solve and renew their knowledge in real time, machines relying on batch data cannot inherently realise where its outputs are incorrect or questionable. Some technologies are helping LLM models access and interpret real-time data streams to avoid this issue, but until all LLM models use this technology as standard, the risks brought by out-of-date LLM models still stand. 

When we strip it right back to the data, what we feed into LLM models is the first and most crucial step in ensuring safety, for an LLM model is only as safe and effective as the data it is trained on. For example, feeding arbitrary data into a model without proper assessment of it would set any business up to fail at the start line. Safety therefore starts not only in the LLM model framework, but also in properly considered data pipelines. 

Setting up for success 

Businesses need to focus on several things to ensure that privacy and safety is placed at the forefront of any LLM development. For example, an appropriate foundation for safety should consider the proper recording and documentation of model inputs and how an LLM has arrived at a conclusion. This helps businesses to identify and signal what has changed within a model – and its output – and why. 

Similarly, data classification, anonymisation and encryption is a fundamental aspect of LLM safety, and the same goes for any type of technological model that assesses information to determine an output. However, many LLM models need to pull data out of its original location to feed through its own systems which can put the privacy of this information at risk – take ChatGPT, for example. OpenAI’s large data breach this summer caused many organisations to panic as sensitive information that had been stored from employees using ChatGPT was now at high risk.  

As a result, businesses must not only adopt proper data storage and anonymisation tactics, but also implement supplementary LLMOps technologies that help companies leverage LLM models without removing their own private data from its original, internal company location, while understanding potential model drifts. Leveraging LLM models that can be fed with both batch and real-time data pipelines from external information sources is one of the most powerful ways of utilising generative AI, but also for protecting sensitive data from a model’s occasional faults. 

Of course, any responsible use of an LLM model has ethical considerations at the heart – all of which should underpin every single decision when it comes to integrating such models. With clear guidelines surrounding LLM use and the responsible adoption of advanced technologies like AI, these models should be built in such a way that reduces bias and accountability in decision making. The same also applies to ensuring model transparency, and knowing the reasoning behind every decision taken by a large language model. 

Safety first 

There is never an ‘easy’ way to implement LLMs, and this is exactly how it should be. Carefully considered development of these models, and a focus on the data and training tools used to shape their outputs, should be the focus of any business looking to implement them. 

Forming the foundations of LLM safety is the responsibility of all who have the desire to build them. Blaming model outputs and attempting to place a bandage on poor LLMOps infrastructure will not make positive contributions to the safe and ethical development of new AI tools, and it’s something all should look to tackle. 

About the Author

Jan Chorowski is CTO at AI-firm Pathway

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