In the era of digital transformation, Large Language Models (LLMs) have emerged as powerful tools for businesses, enabling them to automate tasks, generate insights, and improve decision-making. However, the use of these models also brings forth significant privacy challenges. Despite the inherent privacy protections in enterprise solutions like Microsoft Azure OpenAI Services, residual privacy issues persist, necessitating additional measures to ensure comprehensive data protection.
Fine-Tuning
Fine-tuning a pre-trained open-source LLM on a company’s own data is often a crucial step to ensure the model’s performance aligns with specific business needs. This process involves training the model on proprietary data, which can include sensitive information. A major issue arises here with personal data memorization by the AI model, which can then spew the personal data included in its training data in production. Inadvertent exposure of this information is particularly pronounced in industries such as healthcare and finance, where the exposure of personally identifiable information (PII) could have severe consequences.
Fine-tuning often requires the training data to be submitted to the LLM service provider. While this approach can ensure optimal model performance, it does pose potential privacy risks, as sensitive data must leave the company’s environment.
However, some LLM service providers offer the option for in-house training, allowing businesses to fine-tune models on their own servers. This approach can significantly reduce the risk of data exposure, as sensitive information remains within the company’s control. However, even though models like GPT-3 do not remember information between interactions, there is a risk that they could generate outputs based on sensitive details present in the training data.
Despite its privacy advantages, in-house training comes with its own challenges. First, it can be computationally expensive, requiring substantial processing power and storage capacity. This can lead to increased costs, particularly for smaller businesses that may not have the necessary infrastructure in place.
Second, in-house training requires a high level of expertise in machine learning and data science. Companies must have skilled personnel capable of managing the fine-tuning process, troubleshooting issues, and ensuring the model’s performance meets the desired standards. This need for specialized knowledge can further increase the cost and complexity of in-house training.
Additional reading: Fine Tuning LLMs with a Focus on Privacy
Workflow Integration
As businesses integrate LLMs into their workflows, they often need to share data with third parties. Even with privacy protections in place, there is a risk of data leakage during these exchanges. Furthermore, the use of LLMs in global operations introduces additional complexity, as businesses must navigate a myriad of regional and national data protection regulations. Ensuring compliance in such a diverse regulatory landscape can be a daunting task.
Private AI’s Privacy Layer
Enter Private AI, a pioneering solution that addresses these residual privacy issues head-on. Private AI’s technology is designed to mitigate the privacy risks associated with both fine-tuning LLMs and integrating them into business workflows.
For businesses that need to fine-tune their models, Private AI offers a robust layer of privacy protection. It leverages artificial intelligence to identify and redact PII in unstructured data across 52 languages. This means that even if sensitive data is used for fine-tuning, the risk of inadvertent exposure is significantly reduced. This feature is particularly beneficial for industries such as healthcare and finance, where the protection of PII is paramount.
For in-house training, Private AI’s technology can provide an additional layer of security to ensure that the training data used for fine-tuning can’t lead to output generations reproducing those data. By selectively redacting personal information from the data used for fine-tuning, businesses can ensure that their proprietary information remains secure, and that the privacy of their customers is protected. For example, if a model needs to be fine-tuned to learn medical jargon, a proprietary data set composed of medical files can be redacted to reliably filter out all personal identifiers, such as names and numerical PII, while retaining the relevant health conditions and treatment plans. Private AI supports over 50 entity types that can each be individually toggled on or off.
Alternatively, Private AI’s technology can aid in synthetic data creation, a process that generates artificial data that is statistically similar to the original data but does not contain any real-world PII. This feature is particularly beneficial for AI-driven organizations that rely on vast datasets for model training. By using synthetic data, businesses can train their models without exposing any real data, thus preserving data utility while ensuring privacy.
When it comes to integrating LLMs into workflows, Private AI acts as a robust privacy buffer as well. Whether it’s protecting data from third-party exposure or ensuring secure data exchange between clients, Private AI ensures that sensitive information is redacted in real time. This is particularly beneficial for businesses operating globally, as it helps them navigate the complex landscape of regional and national data protection regulations. If the PII is redacted, it is not shared with anybody allowing concerns around cross-border transfers to be more easily addressed.
General Privacy Compliance with Private AI
Beyond these specific applications, Private AI’s technology can address a broader scope of compliance requirements. Its capabilities extend to various use cases, each designed to meet different aspects of data privacy and protection regulations.
For instance, Private AI can redact existing data lakes, ranging from Electronic Health Records (EHRs), Protected Health Information (PHI), and Payment Card Information (PCI), to call transcripts. These unstructured portions of data are traditionally difficult to redact, but with Private AI, businesses can ensure comprehensive privacy protection across all data types.
Private AI also serves as a redaction front-end, safeguarding private information when it needs to leave a client’s environment or enter the organization’s environment coming from a client concerned about sharing personal information. In essence, it acts as a privacy shield, removing or redacting any non-essential personal information.
Finally, Private AI’s technology can assess privacy risk within data by identifying all types of PII and listing quantities of each. This feature is particularly useful during privacy assessments, cybersecurity breach reports, acquisitions, and more.
Conclusion
The safe use of commercial LLMs is not just about compliance; it’s about fostering trust with customers and stakeholders. With solutions like Private AI, businesses can confidently navigate the digital landscape, knowing that their data privacy is in safe hands. As we continue to embrace AI and LLMs, privacy-enhancing technologies will play an increasingly crucial role in ensuring a secure and compliant digital future.
