The Importance of Data Privacy in Using RAG Technology

In the digital age, the adoption of advanced technologies such as Retrieval-Augmented Generation (RAG) is revolutionizing business processes. However, with the increasing use of artificial intelligence tools, ensuring the confidentiality of processed data becomes increasingly crucial.

With the increasing use of artificial intelligence tools, and particularly generative models like those offered by OpenAI, Google AI with Gemini and PaLM 2, and Microsoft with Azure AI, it becomes even more crucial to ensure not only the confidentiality of processed data, but also the protection of intellectual property and the compliant use of information. A crucial aspect to consider is the fundamental difference between using APIs of public tools like OpenAI and adopting professional solutions like Google Cloud or Microsoft Azure.

Why Data Confidentiality is Fundamental and the Distinction Between Public APIs and Professional Solutions?

Using APIs of public tools, such as those offered by OpenAI, carries an intrinsic risk of information disclosure. Data sent through these APIs can potentially be used for training the provider’s large language models (LLMs), exposing sensitive information to potential breaches or improper use. This raises serious concerns regarding compliance with regulations such as GDPR and the AI Act, which aim to protect personal data and regulate the use of AI. Furthermore, unauthorized sharing of proprietary data can compromise a company’s intellectual property, resulting in economic and reputational damage.

Conversely, adopting professional solutions like Google Cloud AI Platform/Vertex AI and Microsoft Azure AI offers a proprietary and company-controlled ecosystem. In these environments, data remains within the company’s infrastructure (or in a dedicated and isolated cloud environment), is not used for training public models, and is subject to rigorous security and privacy controls. This difference is fundamental for protecting sensitive information.

Expanding the Concept of Data Confidentiality

Data confidentiality is not limited to simple protection from unauthorized access. It also includes:

• Data minimization: Collecting and processing only the data strictly necessary for the intended purpose.
• Data integrity: Ensuring that data is not altered or corrupted during processing.
• Transparency: Informing users about how their data is used.
• User control: Offering users the ability to access, rectify, or delete their data.
• Security by design: Integrating security and privacy from the design stage of AI systems.

Both Google AI, with its AI principles, and Microsoft, with its Responsible AI approach, place a strong emphasis on these aspects, promoting responsible and safe use of artificial intelligence technologies. However, the key difference lies in the control of the ecosystem and the use of data for training.

Protecting Intellectual Property in the GenAI Era

The use of GenAI raises important questions regarding intellectual property. For example:

• Copyright on generated content: Who owns the rights to texts, images, or code generated by an AI?
• Use of copyrighted data for training: Does training models on copyrighted data without authorization constitute infringement?

It is essential to adopt practices that respect intellectual property rights and use tools that offer guarantees in this regard. Both Google and Microsoft are working on technologies that allow tracing the origin of data used for training models, increasing transparency and accountability. This is particularly relevant when comparing public APIs with enterprise solutions, where control over the origin and use of data is significantly greater.

Cases of Non-Compliant Data Use in GenAI and the Risk of Public APIs

Here are some examples of non-compliant data use in GenAI, with a focus on the specific risk associated with using public APIs:

• Training an image generation model on a dataset containing unlicensed copyrighted images, by sending these images through a public API. This constitutes copyright infringement and potentially exposes the images to unauthorized use by the API provider.
• Using sensitive health data to train a medical chatbot model without informed patient consent, by sending this information through a public API. This violates privacy regulations such as GDPR and potentially exposes the data to unauthorized use.
• Generating defamatory or discriminatory content using a model trained on biased data, by sending prompts through a public API. This can have serious legal and social consequences, with a potential lack of control over the dissemination of content.
• Using confidential business data, such as trade secrets or non-public financial information, in public prompts of GenAI models via public APIs. This can lead to the disclosure of sensitive information and the loss of competitive advantage, with no guarantee of confidentiality from the API provider.

Advantages of Using AI Tools with a High Degree of Confidentiality

Using professional solutions such as Google Cloud and Microsoft Azure with advanced access controls, data encryption, and dedicated deployment options, and especially with the guarantee that data is not used for training public models, significantly mitigates these risks. The advantages include:

• Protection of Sensitive Data: Using AI tools that guarantee a high degree of confidentiality, such as Google Cloud and Microsoft Azure solutions, protects sensitive customer and company information by offering a controlled and isolated environment.
• Regulatory Compliance: Ensuring that the tools used comply with AI Act and GDPR regulations avoids legal sanctions and protects the company’s reputation.
• Customer Trust: Data protection increases customer trust, improving the relationship and loyalty towards the company.
• Control and Customization: Platforms like Google Cloud Vertex AI and Microsoft Azure Machine Learning allow training and customizing models on proprietary data in secure and controlled environments, without sharing them with public models and maintaining full control over the data.

Conclusion

In an increasingly digital world, data protection, the protection of intellectual property, and the compliant use of information are essential. The choice between using public APIs and professional solutions like Google Cloud and Microsoft Azure is crucial for information security. Adopting AI tools with a high degree of confidentiality, such as those offered by Google Cloud and Microsoft Azure, not only protects sensitive information but also ensures compliance with regulations and strengthens customer trust.

RAG and GenAI technology offer enormous advantages, but it is essential to use them safely, responsibly, and in compliance with regulations, favoring solutions that offer complete control over the ecosystem and the use of data.

Furthermore, to further optimize data management and security when using platforms such as Google AI and Azure AI, solutions like AIDOCS exist.

AIDOCS is a platform that integrates with Google AI and Azure AI services, offering advanced features for managing, distributing, and controlling data access.

AIDOCS implements a granular authorization system, which allows precisely defining who can access which data and features, down to the individual user level. This allows companies to maintain centralized and secure control over information, even in complex contexts with numerous users and different levels of authorization.