Using Generative AI with Clean Data to Survive in Shark Infested Waters: GDPR and cybersecurity



“Fix the wiring before you turn on the light.”



Introduction

With all the hype around generative AI , it’s not surprising many organizations are incorporating AI into their strategic plans. The problem is, without clean training data, large language models (LLMs) are worthless.

As organizations strive to harness the potential of artificial intelligence (AI), training data is critical. However, in today's data-driven landscape, data privacy and compliance regulations, such as the EU’s General Data Protection Regulation (GDPR), pose massive challenges and are a significant source of data friction as organization seek to monetize their data. There are many other sources of data friction, including organizational knowledge gaps, data/organization silos, vendor lock-in and technical debt, but for the purposes of this article, we will focus on the importance of utilizing a data fabric for integration, security, and data privacy under GDPR, enabling organizations to obtain valuable training data for LLMs while maintaining compliance.

Key Challenges/Opportunities

  • Pseudonymization and Anonymization

  • Consent Management

  • Data Encryption and Access Control

  • Auditing and Compliance Monitoring

  • Low Code/Efficiency

Data privacy is a growing concern, and regulations like GDPR have been implemented across the globe to safeguard individuals' personal information. Compliance with these regulations is mandatory for organizations handling and processing personal data. When it comes to training Large Language Models(LLMs), organizations must adhere to the principles of data privacy, consent, and lawful processing. This is a massive challenge for most organizations because they have a mix of both legacy and modern IT systems with sensitive data. Let's explore how a data fabric addresses key challenges related to security and data privacy.

Pseudonymization and Anonymization

Under GDPR, CCPA and other data privacy regulations, organizations are required to protect personal data by pseudonymizing or anonymizing it. A data fabric must enable organizations to apply these techniques during the integration process by automatically replacing identifiable information with pseudonyms or removing personally identifiable information altogether. This ensures that training data used for LLMs is privacy-compliant, reducing the risk of unauthorized access or data breaches. The key is to think about change management - What is the cost of reacting to changes in the regulatory environment?  Make sure any data fabric you build or buy has prebuilt data privacy components so new integrations are compliant by design to minimize re-work (technical debt).

Consent Management

Consent is a crucial aspect of data privacy compliance. Organizations must ensure and demonstrate they have obtained appropriate consent from individuals whose data is used for training LLMs. A data fabric must incorporate automated self-service consent management capabilities including automated masking of sensitive data unique to each data requestor. This allows organizations to track and manage consent preferences throughout the data integration process. Training data is then sourced, processed and logged in accordance with the consent given by data subjects, thereby maintaining compliance.

Data Encryption and Access Control

Data security is paramount when handling personal data for LLM training. A data fabric must provide robust encryption mechanisms and automate identity and access management. By implementing encryption protocols, organizations safeguard sensitive training data, preventing unauthorized access, mitigating the risk of data breaches, and giving organizations the fine-grained controls necessary to expose valuable data more broadly to enable citizen data scientists. To be truly secure while providing maximum access to data, a data fabric must follow a zero trust model where access managment is automated. This ensures that data requestors alway have the right permissions to data acces. We’ve also reduced data breach risk by eliminating the chance over-permissioned users or “zombie” users (eg. ex-employees and contractors) are able to access sensitive data.

Auditing and Compliance Monitoring

Data privacy requires organizations to demonstrate compliance and maintain records of data processing activities. A data fabric must enable comprehensive auditing and compliance monitoring, providing organizations with a centralized platform to track data integration processes, access logs, and consent management activities. This facilitates efficient compliance reporting, reducing the administrative burden on organizations.

Low-Code Integration for Efficiency & Scalability

As is the case with most technology projects, data integrations are traditionally project based. Because IT project requirements don’t usually take into account the effects on future work, the result is a large number of point-to-point integrations that fail to re-use prior work; each new integration project gets more expensive, more complicated, and more likely to fail. The current IT industry approach hasn’t helped either. While most vendors pay lip service to interoperability, the reality is quite different. Technology vendors create platform stickiness (lock-in) so they can:

  • Sell additional products and raise prices by controlling or limiting how their platforms work with other technologies

  • Make it more difficult to switch vendors

  • Force you to buy features you don’t want or need

Simply put, it is in the financial interest of cloud data platforms and integrators to create proprietary data structures and interfaces that make it difficult to be replaced when contracts end.  What is needed is flexibility and efficiency, not lock-in. Organizations need a low-code, hot-pluggable data fabric for interchanging custom, open-source, and proprietary components. This is critical because organizations need to be able to swap out AI vendors and integrate new sources of training data as newer platforms emerge. 

The alternatives are:

  • Build a data fabric yourself

  • Use a IT platform vendor with a walled garden approach to data integration that limits flexibility and makes you pay for things you don’t need

  • Use best-of-breed data fabric solution that prioritizes interoperability and use of open source to create a force multiplier (like the PrivOps Matrix)

By utilizing interoperable, low-code data integration, organizations can comply with data privacy requirements in a way that scales, ensuring that only authorized and compliant data sources are used for training LLMs and other forms of Machine Learning (ML).

Conclusion

To get the most out of their AI strategy, organizations must strike a delicate balance between obtaining valuable LLM  and other ML training data, operational efficiency, and maintaining compliance with GDPR and other data privacy regulations. Embracing a data fabric for integration, security, and data privacy is essential for achieving this balance. By leveraging the capabilities of a data fabric like the PrivOps Matrix, organizations can streamline data integration, ensure GDPR compliance, protect personal data, and enhance training data quality for LLMs. With these measures in place, organizations can unlock the full potential of LLMs while upholding the principles of privacy and data protection in today's data-driven world.

Tyler Johnson

Cofounder, CTO PrivOps