The hidden risks in applying GenAI to leverage business-sensitive data
In the rapidly evolving landscape of artificial intelligence, businesses are increasingly looking to leverage generative artificial intelligence (GenAI) for various purposes, from customer service to data analysis. While the allure of tools like ChatGPT and similar GenAI applications is strong, relying on these generic solutions can pose significant risks and limitations.
This article explores why businesses should be wary of using so-called Level 1 and Level 2 GenAI solutions and highlights the advantages of adopting Level 3 and 3i GenAI for a more secure and effective approach.
GenAI has already become an integral part of various business processes, promising increased efficiency and innovative solutions. However, not all GenAI applications are created equal, and the indiscriminate use of GenAI tools can pose significant risks to businesses. While the quick-fix allure of these tools is understandable, their limitations and potential drawbacks highlight the necessity for more secure and reliable alternatives.
To critically assess the utility of GenAI across its varying levels and implications for businesses, it is vital to consider the differences in data handling, privacy concerns, and the uniqueness of the AI services provided.
This discussion revolves around three distinct levels of GenAI utilisation:
- Level 1: Basic generative AI tools like ChatGPT
- Level 2: Custom applications built on top of Level 1 platforms
- Level 3: Confidential, standalone models that operate exclusively with an organisation’s proprietary data.
Level 1 GenAI: Limited differentiation and potential data risks
Challenges for Businesses:
- Generic Responses: Level 1 tools provide non-specialised, generic answers that lack the specificity and relevance that come from specialised knowledge of a particular industry or company. Generating answers based on generic datasets, they lack the contextual understanding required for nuanced business decisions. Ultimately, this can lead to misinformation and suboptimal decision-making.
- Lack of competitive edge: Available to everyone, Level 1 solutions leave businesses unable to achieve any unique advantage in terms of AI capabilities. In fact, they might be lulled into not only a de facto dependency but also a false sense of having implemented a bona fide AI strategy.
- Data privacy concerns: Using Level 1 GenAI tools involves sending data to external APIs. This risks exposing sensitive business information or intellectual property (IP) to third-party providers, leading to potential data breaches, leaks, and loss of trust.
- Compliance issues: Many industries are governed by strict data protection regulations, e.g. GDPR or HIPAA. Using public GenAI tools can inadvertently lead to non-compliance, resulting in hefty fines and legal repercussions.
Level 2 GenAI: Custom applications built on public models
Level 2 GenAI applications are built on top of generic GenAI models. These are often customised to some extent but still rely on sending data to external servers, typically managed by companies such as OpenAI. While these solutions offer more tailored functionality, they still inherit many of the problems of Level 1 tools.
Challenges for Businesses:
- Data security risks: While customisation allows for more targeted responses, the underlying data still traverses and is processed by external servers owned by AI providers, creating potential security and privacy vulnerabilities. They still rely on external APIs, continuing to expose business data to third-party environments. This means businesses are not in full control of their data, i.e. the risk of exposure (data breaches and unauthorised access) remains.
- Reliability: The quality and reliability of these applications are contingent on the underlying GenAI models. Any limitations or biases in the base models are inherited by the applications built on top of them.
- Dependence on third parties: Businesses become reliant on third-party providers for their AI needs. This dependence can lead to issues with data ownership, control, and long-term sustainability, as changes in vendor policies or pricing structures can directly impact business operations.
- Customisation limitations: The extent of customisation is often confined by the capabilities and limitations of the parent AI platform.
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Level 3 GenAI: Confidential, standalone models
The optimal solution for businesses seeking to leverage GenAI without compromising data security and reliability is to invest in Level 3 GenAI. These are confidential, standalone language models trained exclusively on a company’s proprietary data. They are, of course, designed to operate independently of any external AI service provider.
Advantages for Businesses:
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Data sovereignty: At the third level, businesses maintain full control over their data, significantly mitigating privacy and IP leakage risks.
By keeping data in-house, businesses can ensure the highest levels of security and compliance with data protection regulations. There is no need to send sensitive information to external APIs. This minimises the risk of breaches and unauthorised access. -
Tailored insights: AI responses are deeply integrated with and highly relevant to the business’s specific operational context and industry nuances.
Level 3 GenAI models are trained on company-specific data. This results in highly relevant and accurate outputs that are directly applicable to the business's unique needs and context. Here, AI becomes a true differentiator, providing insights and solutions that generic models just cannot match. This, in turn, leads to more accurate decision-making and strategic planning. - Security and compliance: Standalone models can be configured to comply strictly with regulatory requirements without the risk of third-party data handling.
- Control and customisation: Businesses have full control over the data being fed into the GenAI models, allowing for ongoing customisation and optimisation. This flexibility ensures that the feeds delivering company-specific, immutable data evolve with the business needs and market conditions.
- LLM agnostic: For the next few years, we’ll hear almost every week that “LLM X just surpassed LLM Y”. As such, it would be wise to build your AI strategy with a “base model agnostic” approach. If a given LLM becomes obsolete, a provider is down or disappears, you could easily replace it with a better, more suitable one. This, of course, is only possible if you retain full control of your data (see above).
- Source-backed information: These models can be designed to reference and back their responses with the company's internal knowledge base, ensuring that all information provided is verifiable and trustworthy. This is particularly important for decision-making processes that require high levels of accuracy and accountability.
A step further: Level 3i - Confidential, Standalone Models based on immutable data
Advantages for Businesses:
- Immutable data and audit trail: Answers backed by source references from the company’s own data ensure transparency and traceability in the decision-making process. The AI's responses are based on immutable data and backed by an audit trail that logs every interaction and decision made. This ensures that the output provided is both transparent and traceable, facilitating regulatory compliance and providing a clear record in case of audits or disputes.
- Role-based access control: This allows the client to customise output for specific roles. For example, C-level positions can access the full dataset; managers can see anonymized data only; and other employees are provided with tailor-made subsets.
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Veracity: The credibility and reliability of the information provided by GenAI are paramount.
In Level 3i GenAI solutions, the source of the data used to train the model carries significant weight. By leveraging authoritative and trusted internal data sources, businesses can ensure that the AI's outputs are not only accurate but also aligned with their standards and expectations. This contrasts with Level 1 and 2 solutions, where the mixed quality and credibility of publicly available data can lead to inconsistencies and errors in the AI's responses.
Even when operating with in-house data only, the quality of the source may vary, depending on the role of the author and/or the given context of the prompt, e.g. an engineer giving technical advice vs a marketing consultant creating content for social media. How we then weigh the importance of their input is crucial to the quality of the response provided by the model.
That you can trust your data and the answers provided by your GenAI will be of paramount importance. This ‘truth factor’ is driving a relatively new trend: Explainable Artificial Intelligence (XAI). Linking AI irrevocably with blockchain, XAI might well prove to be the best approach in the years ahead. A future article will delve into what exactly XAI is and how it can be implemented.
Conclusion
For businesses handling sensitive information, competitive data, or seeking a clear competitive advantage through the implementation of AI, relying on Level 1 or Level 2 GenAI applications introduces significant risks and limitations. These include potential data breaches, loss of any competitive edge due to widespread access to the same AI tools, and dependency on third-party AI service providers. While Level 1 and 2 GenAI applications might offer immediate convenience, they come with significant drawbacks that can undermine a business’s security and its ability to differentiate its role in the market.
In contrast, Level 3 and 3i GenAI solutions provide a robust, secure, and specialised alternative, allowing businesses to fully harness the power of AI without compromising their data or USP. It provides a forceful alternative by aligning AI capabilities directly with business-specific requirements and ensuring complete control over the data lifecycle. This bespoke approach not only secures proprietary data but also embeds the AI deeply into the operational fabric of the company, driving innovation without compromising on confidentiality or compliance. Thus, businesses serious about leveraging AI’s full potential should consider investing in or transitioning to Level 3 or 3i GenAI solutions.
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Chief Technology Officer, Exfluency AG
Co-founder and Chief Technology Officer Jaromir Dzialo‘s philosophy can be summarised in two words: simplicity rocks. He is an expert in innovative technology, blockchain, fintech solutions, and startup business culture. Jaromir is a leader in agile, innovative software development.
