Natural language security queries at Orca Security using Amazon Bedrock

The rapid transformation of natural language into structured security queries unlocks unprecedented efficiency and accessibility for crucial operational insights. This piece details how Orca Security developed Discovery AI, an agent built on Amazon Bedrock that translates plain-English questions into complex Sonar queries. The core finding is a dramatically improved operational profile, reporting 90% lower query costs, 93% fewer input tokens, and 75% reduced latency compared to their initial baseline, all while making sophisticated security data accessible without deep technical query language expertise. This capability democratizes access to critical security posture information, enabling faster threat detection and compliance checks. For a mid-sized financial services firm in Charlotte, North Carolina, this approach could mean their compliance officers, who aren't SQL experts, can directly ask "Show me all unencrypted S3 buckets containing personally identifiable information created in the last 30 days" and receive an actionable report instantly, dramatically cutting down the time and specialized resources usually needed for such audits. An indie SaaS founder based in Austin, Texas, specializing in collaboration tools, could leverage a similar natural language interface to monitor their multi-tenant cloud environment for unusual API activity or misconfigurations, identifying potential security gaps before they become breaches, all without hiring an expensive, dedicated security analyst in their early growth stage. Even a logistics startup operating out of Chicago, tracking vast amounts of sensor data from their fleet, could adapt this pattern to query their operational data lakes in plain English for anomalies or performance bottlenecks, allowing operations managers to identify issues like "Which delivery vehicles in the greater New York area experienced unexpected downtime exceeding 15 minutes last week?" directly. The fundamental shift is the removal of the technical translation layer, allowing those closest to the business problem to directly interrogate complex systems. This accelerates decision-making, reduces reliance on specialized technical staff for routine data access, and ultimately lowers operational overhead. To put this into practice this week, identify one frequently asked, moderately complex question within your organization that currently requires a data analyst or developer to translate into a database query. Consider how a natural language interface could bridge that gap, and sketch out the basic components needed to accept a plain English input and map it to your data schema, even if just as a conceptual exercise.