On-Demand Tech Talk

How Drata Built a Secure, Real-Time Agentic AI System in 60 Days

Kevin Kho
Kevin Kho
Senior AI Engineer, Drata
Anita Kirkovska
Anita Kirkovska
Head of Growth, Vellum
Sharon2
Sharon Xie
Head of Product, Decodable

Building enterprise AI requires more than just interacting with large language models (LLMs)—it demands real-time context to generate accurate answers based on fresh data while maintaining compliance, security, and governance. In this webinar, Decodable, Vellum, and Drata will showcase how a streaming-first architecture, powered by Change Data Capture (CDC) and Apache Flink, enables high-throughput Retrieval-Augmented Generation (RAG) at scale.

Drata shares how they process millions of events per day across thousands of isolated vector databases—leveraging Decodable for real-time data ingestion and Vellum to rapidly experiment and validate AI features. We’ll explore how linking relevant context before indexing improves AI accuracy, how CDC enforces compliance by handling opt-in checks and deletions in real-time, and how to overcome system dependencies in production environments.

Why Watch?

  • Deliver real-time AI context: See how streaming data enhances AI accuracy.
  • Ensure compliance and security: Learn how CDC enforces governance at scale.
  • Accelerate AI development: Discover how Drata went from prototype to production in just two months.

Learn how real-time data streaming powers secure, high-performance enterprise AI.

On-Demand Tech Talk

How Drata Built a Secure, Real-Time Agentic AI System in 60 Days

Kevin Kho
Kevin Kho
Senior AI Engineer, Drata
Anita Kirkovska
Anita Kirkovska
Head of Growth, Vellum
Sharon2
Sharon Xie
Head of Product, Decodable

Building enterprise AI requires more than just interacting with large language models (LLMs)—it demands real-time context to generate accurate answers based on fresh data while maintaining compliance, security, and governance. In this webinar, Decodable, Vellum, and Drata will showcase how a streaming-first architecture, powered by Change Data Capture (CDC) and Apache Flink, enables high-throughput Retrieval-Augmented Generation (RAG) at scale.

Drata shares how they process millions of events per day across thousands of isolated vector databases—leveraging Decodable for real-time data ingestion and Vellum to rapidly experiment and validate AI features. We’ll explore how linking relevant context before indexing improves AI accuracy, how CDC enforces compliance by handling opt-in checks and deletions in real-time, and how to overcome system dependencies in production environments.

Why Watch?

  • Deliver real-time AI context: See how streaming data enhances AI accuracy.
  • Ensure compliance and security: Learn how CDC enforces governance at scale.
  • Accelerate AI development: Discover how Drata went from prototype to production in just two months.

Learn how real-time data streaming powers secure, high-performance enterprise AI.