New Blog | Best practices to architect secure generative AI applications

Microsoft

By Roee Oz

 

As development of applications powered by these advanced generative AI (Gen AI) tools surges, offering unprecedented capabilities in processing and generating human-like content, so does the rise of security and privacy concerns. One of the biggest security risks is exploiting those tools for leaking sensitive data or performing unauthorized actions. A critical aspect that must be addressed in your application is the prevention of information leaks and unauthorized API access due to weaknesses in your Gen AI app.

 

This blog post delves into the best practices to securely architect Gen AI applications, ensuring they operate within the bounds of authorized access and maintain the integrity and confidentiality of sensitive data.

 

Understanding the risks

Gen AI applications inherently require access to diverse data sets to process requests and generate responses. This access requirement spans from generally accessible to highly sensitive data, contingent on the application's purpose and scope. Without careful architectural planning, these applications could inadvertently facilitate unauthorized access to confidential information or privileged operations. The primary risks involve:

  • Information Leaks: Unauthorized access to sensitive data through the exploitation of the application's features.
  • Escalated Privileges: Unauthorized elevated access, enabling attackers or unauthorized users to perform actions beyond their standard permissions by assuming the Gen AI application identity.

Mitigating these risks necessitates a security-first mindset in the design and deployment of Gen AI-based applications.

 

Best practices for granting permissions

Limit Application Permissions

Developers should operate under the assumption that any data or functionality accessible to the application can potentially be exploited by users through carefully crafted prompts. This includes reading fine-tunning data or grounding data and performing API invocations. Recognizing this, it is crucial to meticulously manage permissions and access controls around the Gen AI application, ensuring that only authorized actions are possible.

 

A fundamental design principle involves strictly limiting application permissions to data and APIs. Applications should not inherently access segregated data or execute sensitive operations. By constraining application capabilities, developers can markedly decrease the risk of unintended information disclosure or unauthorized activities. Instead of granting broad permission to applications, developers should utilize user identity for data access and operations.

 

Read the full post here:  Best practices to architect secure generative AI applications

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