Security analysts need fast answers about their cloud infrastructure without deep familiarity with domain-specific query languages. Orca Security’s tool covers over 900 cloud models (the resource types Sonar tracks, such as an Amazon Elastic Compute Cloud (Amazon EC2) instance or an Amazon Simple Storage Service (Amazon S3) bucket). Querying that data required precise model names, nested relationship navigation, and selection from 18 operators (comparison functions like equals or contains used to filter results).

To solve this, Orca Security partnered with AWS to build Discovery AI, a natural language query agent on Amazon Bedrock. Orca provides context-aware security for cloud, code, and AI, and Sonar is its domain-specific query system where users search, filter, and explore across the entire data graph.

In this post, we share the architecture and results of Discovery AI, which converts a natural language question into a Sonar query. As a result, Orca reduced query costs by 90 percent, input tokens by 93 percent, and latency by 75 percent compared to the initial baseline.

Orca Security’s Sonar query system supports 900+ cloud infrastructure and application models with complex relationships between them. To query this data, users construct JSON objects that specify exact model names, select from 18 operators, and navigate parent-child hierarchies across cloud providers. Doing this well requires deep familiarity with the Sonar schema.

To build this capability, the Orca team set out to build a natural language interface that can generate valid Sonar queries from plain text.

Their initial proof of concept loaded the full Sonar schema into a single prompt on the largest available model.

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