There has been a surge in the adoption of relational AI applications across various sectors, including security, following the emergence of advanced language models (LLMs) like OpenAI’s GPT-4, Meta, Llama 2, and Google PaLM2. However, the critical issue of privacy and data sovereignty poses a significant challenge, limiting the applicability of these technologies in most LLM use cases. In some instances, employees within organizations unknowingly transmit personally identifiable information (PII) to platforms such as ChatGPT without a full understanding of the associated security risks.
Not all basic models are created equal, leading to potential inaccuracies in their outputs influenced by a multitude of complex factors. How can LLM users ensure that vendors prioritize their privacy, data sovereignty, and security while selecting the most suitable models for their specific requirements?
This article delves into these aspects while aiming to enhance businesses’ capabilities in evaluating and managing different types of LLMs effectively over time.
Open-source vs. Exclusive LLMs
To provide a comprehensive overview, it is essential to differentiate between two categories of LLMs: proprietary and open-source models. Examples of proprietary LLMs include Google’s PaLM 2 (the foundation of Bard) and OpenAI’s GPT-3.5 and LPT 4.5, which are accessible through internet-facing APIs or chat platforms. On the other hand, open-source models, such as those available on platforms like Hugging Face or Llama 2, constitute the second category. Notably, Llama 2 stands out as the most current open-source model suitable for various industrial applications, making it a preferred choice for commercial entities leveraging LLMs.
The primary advantage of open-source models lies in their ability to be hosted directly on organization-owned infrastructure, whether on-premises, dedicated hardware, or personally managed cloud environments. This setup grants owners complete control over the model’s usage, ensuring data remains within their jurisdiction. Although the performance of open-source models may lag behind the latest cutting-edge models like GPT-4 and PaLM 2, the gap is rapidly closing.
Despite the buzz surrounding these advancements, concerns about security persist. Presently, these AI-specific technologies lack robust governance or adherence to stringent regulatory standards. Various legislative initiatives are underway, such as the Artificial Intelligence and Data Acts (AIDA) in Canada, the EU AI Act, the US Blueprint for the AI Bill of Rights, and other specialized regulations formulated by NIST, the SEC, and the FTC. However, regulatory oversight and enforcement remain limited at present.
Users must exercise due diligence concerning their artificial intelligence supply chain, ensuring adherence to established best practices in their machine learning deployments. When vendors incorporate LLMs into their products, two critical questions must be addressed: the type of model being utilized and its hosting environment. It is imperative to consider these factors—proprietary vs. open-source models, performance/accuracy metrics, and the absence of regulatory scrutiny.
Safeguarding LLM Privacy and Security
The journey begins with the initial inquiry. For many contemporary organizations, the default choice may be GPT-3.5 or GPT-4 for specialized models. Conversely, if a vendor opts for an open-source model like Llama 2, it signals a different approach.
Utilizing GPT-3.5 or GPT-4 models may resolve several data privacy and residency concerns. For instance, if a vendor employs the openAI API, any input data is likely forwarded to OpenAI for retraining purposes, potentially violating data governance, risk, and compliance (GRC) policies. In contrast, leveraging the Azure OpenAI service ensures data confidentiality.
To mitigate the risk of PII exposure, technologies are available to cleanse LLM prompts of sensitive information before transmission to custom endpoints. However, absolute certainty in identifying and scrubbing PII remains a challenge. Open-source models hosted locally offer superior protection against GRC violations compared to custom models.
Organizations deploying open-source models must implement stringent security controls to safeguard data and models from malicious actors. While proprietary models offer cost-effectiveness, high performance, and response accuracy, they necessitate robust security measures like encryption on API calls and role-based access controls on datasets.
Implementing an LLM gateway enhances information visibility within AI implementations. This API proxy enables firms to monitor shared data returned to users, conduct real-time verification of requests sent to LLMs, and address security vulnerabilities promptly. Although this technology is in its nascent stages, it is pivotal for developing AI systems with inherent security measures.
Ensuring LLM Accuracy and Consistency
The focus shifts to model accuracy and performance. LLMs undergo training using extensive datasets sourced from platforms like CommonCrawl, WebText, C4, CoDEx, and BookCorpus. The richness of these training datasets determines the breadth of topics an LLM can proficiently address. Models trained on diverse datasets exhibit versatility in responding to inquiries across various domains, reducing the risk of generating inaccurate or nonsensical outputs.
Fine-tuning plays a pivotal role in enhancing LLM performance by aligning the model with specific use cases. This process is critical for addressing niche domains with limited training data, such as cybersecurity. Understanding the data on which a model is trained, particularly during fine-tuning, is crucial for optimizing its performance and applicability.
Despite the increasing prevalence of fine-tuned LLMs, gathering relevant data for tuning base models remains challenging. Vendors must possess robust data engineering infrastructure to collect pertinent attributes in unstructured formats. Understanding the fine-tuning process and the training data underpins a model’s performance evaluation, ensuring reliable outcomes for users.
As we navigate the complexities of LLM usage, managing and monitoring user interactions with these systems becomes paramount to mitigate security, privacy, and operational risks. Incorporating security and privacy measures from the outset is crucial to prevent vulnerabilities akin to past IT deployments. Embracing security and privacy in relational AI delivery presents a unique opportunity to foster trust and reliability in AI systems.
