Trustworthy and Secure AI: How Small Language Models Strengthen Data Security

Brian G. Thamm, President & CEO, Sophinea Corporation

10/8/24


As artificial intelligence (AI) continues transforming industries, data security and privacy concerns have become central to discussions about AI's role in enterprise environments. Large language models (LLMs), such as OpenAI’s ChatGPT, Google’s Gemini, and Meta’s LLama, have captured public interest for their impressive capabilities and providing valuable insights across various domains. However, while LLMs offer remarkable functionality, they can lack precision in specific fields and raise concerns about data security. Issues such as the security of user-provided data and the risk of data leakage through the inadvertent generation of sensitive information are critical considerations.

The Rise of Generative AI

Generative AI (GenAI) has demonstrated its transformative potential across diverse sectors by revolutionizing how we create, interact with, and understand information. This technology can generate human-like text, images, audio, and complex data models, enabling unprecedented levels of automation, creativity, and personalization. In healthcare, GenAI aids in designing new drugs, developing personalized treatment plans, and synthesizing medical research. In the arts and media, GenAI offers new possibilities for creating original content, such as writing, music, and visual art. Moreover, it drives business innovation by automating content creation, streamlining operations, and providing deep insights through advanced data analytics, fundamentally changing how tasks are performed and enhancing human creativity and decision-making.

In the public sector, GenAI has the potential to transform the delivery of public services and enhance operational efficiency. For example, it can automate routine tasks like document processing, data entry, and customer service, freeing government employees to focus on complex and strategic activities. It also has the potential to foster improved citizen engagement through personalized communication, tailored content for public campaigns, and improved accessibility with real-time translation and assistance.

Concerns about LLMs

Despite GenAI’s potential, safeguarding the sensitive data used in AI models is essential, especially in sectors like defense, healthcare, and finance, where data integrity is crucial. With their massive datasets and open-ended capabilities, LLMs provide broad utility but pose significant risks. The larger the model, the greater the vulnerability to potential security breaches, from exposing sensitive information during training to exploitation through deployment loopholes.

Additionally, LLMs trained on extensive, generalized datasets sourced from the internet are susceptible to "hallucination," generating inaccurate or nonsensical information, making them less suitable for mission-critical applications. Moreover, ethical concerns around AI deployment have become increasingly prominent. LLMs often perpetuate biases in their training data, potentially leading to unfair or discriminatory outcomes. These biases, embedded in vast datasets that reflect historical prejudices or social inequalities, can reinforce stereotypes, marginalize groups, or skew information. Managing these biases is challenging due to the subtle, ingrained patterns that models learn from, which are difficult to detect or correct. Furthermore, the opacity of AI decision-making processes, often called "black boxes," complicates efforts to ensure accountability and fairness.

Why SLMs Are the Future of Secure AI

In contrast, small language models (SLMs) offer a focused solution by enhancing security, trustworthiness, and specificity, making them ideal for organizations prioritizing data protection. SLMs, with their compact architecture, provide a streamlined alternative that balances performance with security. Their smaller size allows for more targeted training on specific datasets, ensuring the model is tailored to the unique needs of its domain. This narrower scope improves the relevance and accuracy of outputs and reduces the likelihood of exposing sensitive data, making SLMs particularly suited for enterprises focused on data privacy.

SLMs are also less prone to "hallucination" because they are trained on specialized, domain-specific data, resulting in more reliable, fact-based outputs—crucial in industries where accuracy is vital. In the defense sector, for example, SLMs can process classified documents, providing insights without risking the exposure of sensitive intelligence.

Additionally, by limiting the scope of training data to specific, well-curated datasets, SLMs can be designed to avoid the pitfalls of biased information, which is especially important in sensitive areas like defense or law enforcement, where biased outputs could have serious real-world consequences. The transparency and auditability of SLMs also allow organizations to regularly review and update training data, ensuring alignment with ethical standards and regulatory compliance.

A Trustworthy AI Future with SLMs

As the demand for AI-driven solutions grows, so does the need for models that operate securely, efficiently, and ethically. Small language models represent a critical advancement in developing trustworthy and secure AI, offering enterprises the precision, control, and security necessary to thrive in a data-driven world. By focusing on domain-specific applications and mitigating the risks associated with broader, more generalized models, SLMs are poised to play a vital role in strengthening data security across industries.

Bio - Brian G. Thamm:

Brian founded Sophinea Corporation in 2018 as President and Chief Data and AI Officer (CDAO). He has over 15 years of experience developing enterprise-level Data Analytics and AI solutions for leading organizations and the federal government. For more information on Brian and Sophinea Corporation, please visit https://www.sophinea.io/.

 
This article originally appeared in the Fall 2024 edition of Service Contractor magazine.