Along with the promise of innovation comes the critical responsibility of addressing data security concerns associated with utilizing LLMs. Companies have spent years crafting and implementing access control policies and building technical safeguards to protect their data. These cannot be forgotten as LLMs become integrated into day-to-day workflows. In this blog post, we delve into key considerations and strategies to ensure robust data security in the realm of LLMs.
Data is the lifeblood of LLMs, and understanding potential vulnerabilities is crucial. The current state-of-the-art LLMs were trained on unimaginable quantities of publicly accessible internet data, and the next generation is being trained on our interactions with current-gen LLMs. Without caution, this can include you or your organization's private, proprietary data. Extreme caution must be exercised when inputting data into any LLM you do not control, whether that be from a web interface or API. Once your data hits their servers, your data is now outside of your secure control.
If you are not self-hosting your own LLM, you are giving your data to a third party company in which you are putting your trust. What are they doing with your data that you sent to them? Are they re-training future models with it? Is the data being stored, even inadvertently, in their various server logs? Who has access to your data on their side? These are all serious considerations that must be taken into account when selecting an LLM provider, whether that be OpenAI, Microsoft, Google, AWS, or any other provider. Self-hosting, while more challenging, is the only solution to guarantee data security at the LLM.
If you are building an LLM-powered application for your business, it is essential to make sure robust encryption protocols are utilized for all data flow between your data sources, application components, and users. While it is easier to assume network traffic between the various components under your control is secure, it is still essential all data and communication be encrypted with modern standards at rest and over-the-wire. Zero Trust and Assume Breach and modern tenants of cybersecurity that, when followed, will significantly increase the security of your data.
Restricting access to sensitive data is paramount. Implementing access control mechanisms to limit what data an individual employee can access is standard practice. However, these same access controls need to exist in your LLM-powered application as well. For example, if building a retrieval augmented generation (RAG) application, the data being sourced to answer the users query should not come from any sources the user would not otherwise be able to access.
Establishing comprehensive auditing and monitoring practices helps detect anomalous behavior. Tracking the inputs and outputs of the LLM application is essential record keeping that can help detect malicious users as well as provide an audit trail of what data has been sent to your LLM provider (assuming you are not self-hosting). Even if utilizing a completely trusted LLM-provider, data breaches can and do happen. If you have an audit trail of all private data sent to a breached provider, you will immediately know your exposure.
Technical controls and safeguards are absolutely necessary and essential. However, there is no substitute for user education; highlight the importance of collaboration between security, data science, and engineering teams. Fostering communication ensures that security measures are integrated seamlessly into the LLM development lifecycle.
As organizations embrace the potential of LLMs, prioritizing data security is non-negotiable. By implementing robust encryption, access controls, monitoring mechanisms, and awareness training, businesses can harness the power of LLMs while safeguarding sensitive data. Together, we can navigate the evolving landscape of AI with a commitment to responsible and secure innovation.
