Testing AI Without Risk: How Distributional Balances Security with Insights

At the heart of Distributional’s design is a simple yet crucial belief: testing AI application data is essential. This ensures not only the safety and reliability of AI systems but also helps to deliver a seamless end-user experience. Regardless of where teams are on their AI journey, this conviction resonates with every organization we’ve engaged with.

For organizations deploying AI apps, the most effective way to test these apps is by leveraging their production logs, which capture the real usage interactions. This approach provides unparalleled insights, and grounds testing in real world usage.

Given the highly sensitive nature of production logs, companies are understandably cautious about their security. This is why many AI systems are designed using hosted models and deployed on-premises or within VPC infrastructure, and production logs are typically stored in highly secure environments with strict access controls. This careful approach ensures that sensitive data remains protected and doesn’t fall into the wrong hands.

However, this limited access often prevents companies from leveraging their production logs to test and understand their AI apps. In turn, this means they miss out on a significant opportunity to enhance the quality of their AI systems and provide a more reliable and safe user experience.

Distributional has been deeply mindful of these security concerns since the beginning. Our goal has always been to create a testing platform that is secure and compliant—one that protects user privacy and adheres to regulatory requirements without compromise—while still enabling teams to gain insights from their data. To achieve this, we’ve architected our product to be private and secure by design.