--- title: Intelligent security at ClickHouse speed: How Cogent... description: Cogent Security built an AI-native vulnerability management platform on ClickHouse, migrating from Postgres to achieve sub-second query latency on hundreds of... canonical: https://daily.dev/posts/intelligent-security-at-clickhouse-speed-how-cogent-security-built-an-ai-native-vulnerability-manag-obfsc8odp twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Intelligent security at ClickHouse speed: How Cogent Security built an AI-native vulnerability management platform | daily.dev og:description: Cogent Security built an AI-native vulnerability management platform on ClickHouse, migrating from Postgres to achieve sub-second query latency on hundreds of... og:url: https://daily.dev/posts/intelligent-security-at-clickhouse-speed-how-cogent-security-built-an-ai-native-vulnerability-manag-obfsc8odp og:image: https://api.daily.dev/og/posts/oBFsC8OdP.png og:image:alt: Intelligent security at ClickHouse speed: How Cogent Security built an AI-native vulnerability management platform og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Intelligent security at ClickHouse speed: How Cogent Security built an AI-native vulnerability management platform **[ClickHouse](https://daily.dev/sources/clickhouse)** · 10 min read · 1 upvotes · 0 comments ## Summary Cogent Security built an AI-native vulnerability management platform on ClickHouse, migrating from Postgres to achieve sub-second query latency on hundreds of millions of rows without caching. The architecture uses a hot/cold data lakehouse with Iceberg on S3 and ClickHouse as the serving layer, with denormalized tables, projections, and tuned compression codecs as core design principles. Their Chart Agent uses an agentic loop with RAG, live SQL execution, and interleaved thinking to convert natural language into ClickHouse SQL, improving accuracy from 40% to 94%. Pre-computed ClickHouse projections were the single biggest accuracy driver, simplifying the SQL the agent needed to generate. An in-house Ontology Service keeps the data model and its semantics co-located to prevent agent confusion when schemas change or customers have custom fields. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [ClickHouseの速度で実現するインテリジェントセキュリティ:Cogent SecurityがAIネイティブな脆弱性管理プラットフォームを構築した方法](https://daily.dev/posts/clickhouse-cogent-security-ai--gggs505xd) · ClickHouse · 0 upvotes · 0 comments - [How iFood built its agentic security platform on ClickHouse Cloud](https://daily.dev/posts/how-ifood-built-its-agentic-security-platform-on-clickhouse-cloud-0yktjc5xt) · ClickHouse · 0 upvotes · 0 comments - [Agentic coding with ClickHouse. 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