Understanding Data Security and Authorization in Generative AI Workloads

Data security and data authorization are increasingly crucial in today’s business architectures, especially as we leverage generative AI technology. With innovations such as large language models (LLMs) and multimodal foundation models (FMs), organizations find exciting new ways to harness internal data. However, with these opportunities come significant responsibilities, especially in handling sensitive information. In this article, we’ll explore the essential aspects of data security and authorization in generative AI workloads.

Navigating Risks in Generative AI

Traditionally, machine learning and deep learning utilized labeled data within enterprises to train models. Generative AI, however, opens up new avenues to deploy both private and public data, which may include unstructured and semi-structured data from various sources like databases or data warehouses. For instance, a software firm might simplify log analysis using a retrieval-augmented generation (RAG) pipeline that enables incident responders to interact naturally with data. This raises a critical question: How can designers ensure that data access is limited to authorized users or applications?

The Challenge of Output Stability

Generative AI’s non-deterministic output system poses challenges for data security. Unlike traditional models, which adhere to a specific schema for output, generative AI can produce various content types (text, images, audio) unpredictably. This unpredictable nature increases risks, especially when fine-tuning LLMs with sensitive data. Malicious actors can exploit this unpredictability through sophisticated prompt injection methods, making it crucial to have a robust data authorization framework governing data access and usage.

For instance, consider a scenario where a user queries an LLM to access additional data from an application. If the LLM’s output is erroneously used to dictate authorization, unauthorized access may be granted, leading to serious data breaches.

Ensuring Proper Authorization

Once data becomes part of an LLM through training or fine-tuning, any principal (user or application) can access the model and, consequently, the data trained into it. For example, if an internal log dataset is used to enhance LLM performance, how can we ensure that only authorized users can extract sensitive information? Algorithms for advanced prompting can filter patterns, but they aren’t capable of making authorization decisions.

To adequately protect sensitive data, roles and permissions must be enforced through mechanisms integrated into the generative AI application rather than relying solely on the LLM’s inference outputs. Implementing RAG in your application means that the application itself should manage data access permissions, ensuring only authorized individuals can interface with key datasets.

Tackling the Confused Deputy Problem

Data access should strictly be granted to authorized principals. The “confused deputy” problem can occur when a privileged entity inadvertently grants access to unauthorized users. For instance, if a user isn’t allowed access to a data source but is given access to a generative AI application, they may exploit this to circumvent data access controls. Implementing the correct authorization constructs while delivering data to the LLM is vital to prevent these scenarios.

What Should You Do?

So, as a business leveraging generative AI, what steps must you take to secure sensitive data while reaping the benefits of this technology?

1. Understand Risks: Familiarize yourself with risks tied to handling sensitive data, including first-party data and personal health information (PHI).

2. Implement Strong Data Security: Introduce well-structured data authorization mechanisms tailored to your architecture. Ensure only authorized principals gain access to sensitive information during both inference and model training phases.

3. Focus on Data Governance: Before using sensitive data in your generative AI applications, determine what data exists, what it comprises, and access levels for each principal involved.

4. Use Trusted Identity Sources: In agent-based architectures, ensure the LLM accesses data only after verifying the user’s authorization through specialized APIs. This will significantly bolster security against unauthorized access.

Harnessing Amazon Bedrock Agents for Strong Authorization

When implementing generative AI systems that interact with sensitive data, consider architectures where your AI system interfaces with real-time and proprietary data. With Amazon Bedrock Agents, you can ensure that sensitive data remains protected while enabling rich interactions.

For instance, an application designed for a healthcare setting can differentiate user roles (doctors vs. receptionists) and maintain strict data access controls. By delegating the authorization checks to backend systems and utilizing secure communication methods (like session attributes), you can effectively mitigate the risks of sensitive data exposure.

Here’s a simplified flow:

1. The user query is submitted with session attributes containing their identity context to an agent API.
2. The authorization is performed at the backend, ensuring the user can only access data they are permitted to see, therefore blocking any attempts at prompt injections.

Conclusion

Understanding and implementing robust data security and authorization measures is essential for organizations using generative AI. By recognizing the risks and adopting thorough governance practices, businesses can leverage both public and private data sources effectively.

For more insights on generative AI security, check out related posts on the AWS Security Blog or explore additional resources on building secure AI applications.

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