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How to Address Generative AI Data Privacy Concerns?

How to Address Generative AI Data Privacy Concerns?

Published on: 12 Apr 2024 7 min read

Generative Artificial Intelligence (AI) has changed the way humanity interacts in the digital realm. Its advantages include enhanced automation, advanced content creation, and many others. However, it is also true that its usage poses serious problems with data privacy. 

How to Address Generative AI Data Privacy Concerns?

Read on to find out what they are and what are the main Generative AI data privacy concerns, the most common challenges, ethical considerations, and data protection solutions to mitigate privacy risks.

What Is Generative AI 

Generative AI utilizes deep learning algorithms to generate new data. It transforms the original data obtained in training into text, image, and audio content. Generative AI’s productivity and contribution to the modern economy make it indispensable across all industry verticals, and it is already a fixture in contemporary society. 

Generative AI can affect data privacy, though, especially when its models’ training involves massive datasets that typically contain Personally Identifiable Information (PII). Inadequate management can expose sensitive information, resulting in serious privacy concerns. 

Generative AI and Related Core Privacy Concerns 

Training data and the content it generates are the most likely sources of Generative AI privacy concerns. Large Language Models (LLM) process vast training data that often contain sensitive information. Exposing such stored data can result in significant data breaches. 

Training Data Concerns

Whenever the data that trains Generative AI models appears in the generated content, all sensitive information can resurface as well. That is why datasets that include PII need robust anonymization measures, as they are critical to LLM training. In the worst-case scenario, sensitive data, including internal corporate information, social security numbers, healthcare records, and other personal details, may get into the LLM’s outputs mistakenly.

Inference Issues

The method LLMs utilize to incorporate user inputs or prompts into text is called inference. If prompts that contain sensitive data access the language model and affect the generated content, they can expose such data. The most common use case is providing contract information to an LLM, which might lead to sensitive data entering the LLM and becoming available to other users.

Legal and Ethical Considerations

The rapid upsurge of Generative AI has naturally led to increased attention on the ethical and legal aspects of AI usage. Handling personal information needs to follow tight criteria outlined in data privacy regulations such as the General Data Protection Regulation (GDPR), the California Privacy Rights Act (CPRA), and AI-specific laws such as the European Union’s Artificial Intelligence Act. Therefore, companies that utilize AI solutions have to deal with the challenge of accommodating both technical innovation and compliance.

Privacy Laws and Regulations

Companies that utilize Generative AI extensively quickly realize that the legal landscape is a potential minefield. Privacy regulations, requirements, and restrictions differ from country to country, which makes compliance genuinely challenging. 

Cross-border data transfers, storage locations, and individual data subject rights like the ‘right to be forgotten’ are among the most common legal restrictions. Such regulations are particularly daunting for companies that leverage LLMs since these models cannot selectively erase or ‘unlearn’ pieces of data. This is particularly tricky when complying with legislation that supports an individual’s ‘right to be forgotten,’ which allows individuals to have their personal information erased from systems. 

Data Localization and Data Subject Access Requests

Data localization rules, as well as Data Subject Access Requests (DSARs), complicate matters further. Countries and even regions have local regulations that govern the handling, processing, storage, and protection of user data. Needless to say, this is a considerable disadvantage for businesses that utilize LLMs for their worldwide consumer base.

EU and California individuals can request access to their personal information, which can be problematic if that data has been handled by LLMs. Such complex privacy and compliance environments require guarantees that LLMs do not access sensitive data. 

Approaches to Data Privacy in Generative AI

Prohibited or controlled access to AI systems, replacing actual data with synthetic data, and deploying private LLMs are among the strategies for addressing the privacy challenges related to Generative AI models. 

Prohibited and Controlled Access

Controlled access is an acceptable temporary form of protection. Unfortunately, such restrictions are not that efficient in the long term, and bypassing them might result in data privacy issues.

Synthetic Data

Replacing sensitive with non-sensitive synthetic data might limit PII’s access to the model but compromise the value that drove the sensitive information sharing with the LLM. This would lead to a lack of context and referential integrity between the synthetic and original sensitive data.

Private LLMs

Indeed, cloud providers such as Google, Microsoft, AWS, and Snowflake promote private LLMs as an AI data privacy solution. It can be very convenient for companies to train private LLMs with their internal proprietary data. However, private LLMs’ approach to data privacy leaves a lot to be desired. Their model isolation is insufficient as their access controls lack precision. As a result, anyone with private LLM access can quickly obtain the data it contains.

Data Privacy Vaults: The Ultimate Solution? 

So, is there an ultimate solution to overcome the privacy issues related to Generative AI? 

Innovative companies increasingly adopt data privacy vaults that isolate, protect, monitor, and manage sensitive user data and accommodate region-specific compliance through data localization. Read on to learn more about their benefits.

How Data Privacy Vaults Work

Data privacy vaults store sensitive information away from all systems to guarantee data is intact and well-protected. Such vaults substitute sensitive data with de-identified data that companies can use to refer to the sensitive data they keep in the cloud and downstream services.

To de-identify data, the vaults replace sensitive data with obfuscating tokens. This means downstream services keep a token version of the data, which exempts them from compliance requirements.

Data Governance and Control

Data privacy vaults employ a zero-trust approach to manage sensitive data access tightly, ensuring that user accounts or processes cannot access such data unless access control regulations explicitly permit it. This gives you control over all visibility and its timing, location, continuation, and format to guarantee compliance.

Scalefocus’ AI Adoption Framework: A Conclusion

The Scalefocus AI experts have extensive experience addressing data privacy concerns. Our AI adoption framework аllows companies to introduce AI in short and easily quantifiable steps. Scalefocus takes data privacy very seriously and it is something we address from the very beginning of our partners’ AI journey and develop further once they have a working AI solution. We always assess data regulations that impact the data landscape like HIPAA/GDPR and personal data as part of the thorough assessment of existing data architecture, data sources, quality and availability. This is the only way to have a comprehensive data strategy for collecting, cleaning, and storing data for AI purposes.

Our team prioritizes the development of clear data governance policies, including data privacy and security measures. As a result, we enable data lineage overview over data that comes in and out of AI solutions, and work with enabled data, and much more in the context of data governance framework. We also follow ethical AI practices and guidelines to ensure responsible AI usage. Scalefocus’ tools in place guard against personal and confidential data leaks, set data boundaries for AI-processed data, and create transparent and traceable monitoring over the actions of AI systems.

Book a meeting with our experts to learn all you need to know about data privacy and discuss the AI adoption framework in the context of your specific business case.

About the Author:

Krasimir Kunchev

Krasimir Kunchev

Senior Content Writer

Kras is a true musical force-turned-copywriter. What can we say? Multitalented people do exist, and we got them! He’s been on stage since the mid-nineties when punk rock was alive and kicking. Fittingly, he started his writing career as a rock journalist and later learned his chops in advertising.

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