LLMs Can Pose Risks for Companies: How to Mitigate Data Breach Threats

LLMs Can Pose Risks for Companies: How to Mitigate Data Breach Threats

Explore effective strategies for securing company data and implementing Generative AI while minimising privacy risks. Discover the significance of refining LLMs with proprietary data and cultivating a data-driven corporate environment to successfully integrate AI within an ever-evolving AI landscape.

Tools such as ChatGPT have raised awareness and fuelled conversations about artificial intelligence (AI) and its potential business benefits. OpenAI’s popular chatbot reached 100 million monthly active users in just two months since its launch. Research by the online learning platform Coursera and YouGov revealed that 83% of UAE firms are poised to integrate Generative AI, such as Large Language Models (LLMs), into their daily operations. So,  AI is here to stay and will transform industries and how we work.

The rise of LLMs represents a pivotal juncture in the trajectory of artificial intelligence. These versatile models can revolutionise our interaction with computers, spawning industry applications. From crafting eloquent writing pieces to generating lines of code, LLMs demonstrate a promising proficiency.

Limitations of LLMs

But companies must ensure that responses are based on the company’s data, avoiding bizarre or “hallucinated” responses due to a lack of context and unvalidated data sources. Of course, these responses should not compromise data compliance and intellectual property.

Large Language Models (LLMs) face a significant obstacle when confronted with specific inquiries related to organisations. These models predominantly rely on vast amounts of publicly accessible internet text spanning diverse topics and domains. However, when faced with precise company-related queries, their responses manifest as either “hallucinations” or “out of context” answers, diverging from the intended user query.

Primarily, these “hallucinations” denote an ill-advised practice that these language models tend to resort to, generating fabricated information that strikingly mirrors reality yet poses challenges in discerning its authenticity. Essentially, “out-of-context” answers entail information generically provided by LLMs, not tailored to the specific context of the question. For enterprises, the effectiveness of generative AI and associated LLMs hinges on the quality and reliability of the training data they ingest.

The looming challenge: data breaches

Also, data privacy is a critical concern for all businesses, as both individuals and organisations grapple with the challenges of safeguarding the data of customers and the company itself. Publicly available Generative AI tools are a perfect example of technological advances that expose individuals and organisations to privacy risks. Such third-party applications can store and process sensitive company information, which could be revealed during a data breach or unauthorised access. 

A secure horizon: avoid data breaches and hallucinated answers

But how can companies overcome these barriers and use Generative AI in their company in a trusted way? Businesses can build their own AI application powered by an open-source LLM with their data. 

With this approach, developers can fine-tune large language models with company-specific data, improving response quality by developing task-specific understanding. This allows the model to understand user queries, provide better answers and deftly handle the nuances of the questioner’s specific language. By integrating a knowledge database, LLMs can access specific information during the generation process. This integration allows the model to generate answers that are not only language-based but also based on the context of its knowledge base. In addition, all data is hosted internally in the enterprise and not shared with external services. 

Driving AI adoption also requires practical change management efforts, including comprehensive employee training and fostering a data-driven culture. Businesses can successfully implement AI-driven data analytics projects by proactively addressing these challenges.

AI is changing rapidly; data is the constant

For business, tools such as ChatGPT have raised much awareness and fuelled conversations about AI and its potential business benefits. Large language models are increasing everyone’s access to data, but this raises many data compliance and intellectual property concerns. Large language models are only as good as the data they’ve been trained on. As the AI market changes rapidly, data and enterprise context will be the constant success of any LLM or AI models.