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.