Data is the cornerstone of modern AI applications, especially for generative AI (GenAI), where retrieval-augmented generation (RAG) enhances the relevance and utility of generated content. But what if you have sensitive data you DON’T want shared by your GenAI solution?
RAG-based applications draw from private proprietary data, typically in vast amounts, to offer internal context to foundational models. With data as the key differentiator in AI and GenAI applications, data classification also becomes a high priority as it provides processes to gain visibility and control over your data.
Without proper data management and protection, your GenAI applications can run the risk of exposing sensitive information and causing your organization to suffer compliance backlash. Proper data classification can help restrict your AI application’s access to just the data that’s accurate, compliant, and context aware.
In this post I want to explore the data challenges unique to RAG-based GenAI applications, how data classification can solve these challenges, and provide an overview of the current solutions available to NetApp users on AWS to streamline data classification.
Here’s what we’ll cover:
What kind of data challenges do you face with GenAI?
Data classification has become a necessity for GenAI
What should a data classification solution offer?
Practical example: Healthcare use case
Choosing a data classification solution on AWS
What’s next?
What kind of data challenges do you face with GenAI?
RAG-based GenAI offers immense potential by enabling organizations to tailor foundational models to their specific context, creating more relevant and personalized outputs. However, accessing and managing private, large-scale data introduces several technical challenges that must be addressed to keep your AI use in line with privacy regulations.
For RAG models to work effectively, they must retrieve data from a variety of distributed and often fragmented sources. This requires navigating different formats, storage systems, and access protocols without compromising performance. Inconsistent or inaccessible data can hinder model accuracy and efficiency.
GenAI applications require tailored data controls per use case and industry. Filtering must categorize and restrict access to data based on regulatory requirements and relevance. Poor filtering can lead to a compliance violation or to using irrelevant data. That reduces the model's utility.
Handling sensitive data, personal identifiable information (PII), and intellectual property is a core challenge. Encryption, masking, and other security measures are needed to prevent exposing data during storage or processing.
GenAI applications handle vast amounts of data, often in real time. Infrastructure must scale dynamically to accommodate growing datasets, maintain performance, and to avoid bottlenecks and escalating costs.
Robust access control mechanisms such as role-based access control (RBAC) are essential to prevent unauthorized access to sensitive data. Without proper governance, data breaches or misuse can compromise both security and the model’s integrity.
The problem is that GenAI models and applications don’t explicitly care about what data they access or how they access it. It’s up to the developers of the RAG solution to put mechanisms in place for safe and efficient data retrieval that can follow compliance guidelines and maintain performance.
Data classification has become a necessity for GenAI
Data classification enables teams to implement mechanisms that automatically detect and categorize sensitive data, such as PII, healthcare records, or financial information.
With data classification, you can protect your organization against the extended risks that come with mishandling sensitive data, inadvertently exposing it, or allowing unauthorized access, such as data breaches, regulatory penalties, and lasting reputational damage.
A robust data classification framework can give you ways to take control of your data through:
- Comprehensive data visibility
By gaining comprehensive visibility into your data landscape, you can understand the potential risks and keep data properly categorized for further action.
- Automated sensitive data detection
By using automated tools, data classification can instantly scan and identify sensitive information such as PII, healthcare data, or financial records, with high accuracy.
- Data security risk mitigation
When sensitive data isn’t classified, it’s easy for it to be accidentally accessed, deleted, shared, or misused. Classification allows you to mitigate risks by clearly labeling sensitive data and enforcing policies to manage access and usage.
- Automated governance and compliance
As privacy and AI regulations continue to evolve, maintaining visibility and control over data is essential. There are data classification tools that can automate compliance workflows, giving you a head start towards continuously enforcing your legal and regulatory requirements.
By integrating these mechanisms, data classification tools not only protect sensitive information but also allow organizations to confidently deploy GenAI solutions, knowing that their data is secure, governed, and handled with compliance in mind.
What should a data classification solution offer?
A robust data classification solution addresses the inherent data challenges of enterprise GenAI by offering the following features:
- Agentless discovery helps you identify and classify data without the need for extensive software installation on every data source— whether on premises or in the cloud. Through this real-time analysis, data classification also provides up-to-date insights into your organization’s data security posture.
- Security by design provides pre-built policies that align with common regulations, such as general data protection regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA). Compliance guardrails should be recommended by the data classification solution to help your organization follow security best practices from the outset and with minimal overhead.
- Customizable workflows tailor policies and classification workflows to your organization’s specific governance practices. Custom workflows can incorporate industry-specific standards so data classification is also contextually relevant.
- Automated actions execute predefined actions on sensitive data, such as masking, encryption, or redaction, based on predefined rules. For instance, when sensitive data is masked, data remains retrievable, but sensitive portions of it are obscured, with the model clearing indicating that the information is masked.
Other advanced functionalities include multi-language support, integrations, reporting, alerting, access control, and analytics.
Practical example: Healthcare use case
Consider a healthcare organization utilizing RAG-based GenAI. Their goals are to increase the efficiency of their operations and provide better patient care. In this scenario, an advanced data classification solution can automatically detect and categorize sensitive patient records, to help follow HIPAA regulations.
The system can mask patient identifiers, flag non-compliant data, and restrict access based on user roles.
This multifaceted approach allows the healthcare provider to fully leverage the power of GenAI for predictive analytics, personalized treatment plans, and operational efficiencies, all while maintaining stringent control over patient privacy and data security.
Data classification solutions for GenAI on AWS
Each data classification solution option caters to different needs, making it essential to select the right one based on your organization’s specific requirements.
- Amazon Bedrock is an AWS service for building and deploying GenAI models. With support for basic data governance features like data tagging and access controls, it can provide a simple data classification solution for non-complex environments.
- AWS Glue is a fully managed extract, transform, load (ETL) service. This data integration service includes features for data cataloging and classification, allowing users to discover, organize, and manage data, however it comes with some management overhead.
- NetApp® BlueXP™ classification is a built-in part of BlueXP that can automatically search and report on the contents of your data repositories both on AWS and on premises in order to identify sensitive private information, PII, and other data cost and security issues. It uses natural language processing (NLP) powered by AI, producing results that are fast, contextually aware, and fully understandable in a readable report dashboard.
The above services detail some of the data governance tools at the GenAI developer’s disposal. Using a tool that can deliver a comprehensive solution for data classification, governance, and privacy control will be key to address the more advanced, enterprise-grade requirements of a GenAI application.
