Data Security & Large Language Models - How do you keep your data safe?

Data Security Strategy When Working with Large Language Models (LLMs)

Even for those most skeptical of it, the dangerous approach to generative artificial intelligence & large language models (LLMs) is to simply ignore it, hope it goes away and let it fall by the wayside. However, a better approach is to experiment with it on low risk levels and work your way up.

Here are five key data security strategies when approaching any potential automation/implementation that involves the use of large language models.

5 Ways To Keep Data Safe

1. Don't Expose Your Data - Ensure your data is NOT being used to train the large language model. This may sound counterintuitive given the amount of discussion around specific kinds of smaller language models & models that can be trained on your own data. These are alternative approaches that are also effective. However, the approach with the current fastest time-to-market is to leverage large language models that are NOT trained on your data and then making use of additional logic and fine-tuning to filter out unwanted results and potential hallucination from LLMs. This allows you to benefit from the strength of the LLM while keeping your data secure.
2. Private Instance - If you're on the cloud (which is basically the default nowadays for cutting-edge enterprise-ready technologies), ensure your instance on the cloud is private and not a shared instance across other clients of the technology platform you are working with. This prevents the potential for data leakage and gives you more power to control access to your data.
3. Certifications - Any enterprise-grade artificial intelligence/automation platform will have baseline strong audited security measures in place. Some of the most common are HIPAA, PCI & SOC2 certifications
4. Automatic Data Destruction - Data should be kept in places based on necessity. A common approach clients take when using automation to process data is to automatically destroy that data once it's been processed.
5. Data Encryption - When data is moved around, especially for sensitive data, ensure its encrypted at rest and in transit before and after it interacts with an LLM.

Takeaway

The goal is to ensure your data is safe. Deciding to forgo disruptive technology will set you back, regardless of the size of your organization. Mid-sized firms are competing with larger enterprise companies due to the impact of artificial intelligence to conduct processes faster and more accurately, thus maximizing their human talent.

For example, LLMs have significantly enhanced the speed & accuracy by which intelligent document processing technology can extract data from referrals, prescriptions, lab reports, loan packages, invoices and virtually any other semi-structured/unstructured document. This was never-before-seen as unstructured data was written off in the past as something that simply needed human eyes to retrieve value from.

This is simply one small example where generative AI is cementing its footprint... with a lot more to come.