Drata. Scaling Data-Driven Decision Making with Cube's Semantic Layer and AI Agents

Drata provides an AI-native trust management platform, automating and centralizing governance, risk, compliance, and assurance for thousands of businesses globally, including a third of the cloud 100. As their customer base grew and their data platform matured, Drata needed to deliver real-time insights both internally to their teams and externally to their customers and partners.

The Challenge: Scaling Analytics and Streamlining QBRs

Drata faced the critical challenge of scaling its analytics capabilities without scaling its team linearly (scaling without adding proportional headcount).

Drata's Customer Success Managers (CSMs) were burdened by time-consuming manual processes:

• Manual QBR Generation: CSMs needed to create data-driven Quarterly Business Review (QBR) presentations for every major account, a process that typically took 2 to 3 hours per QBR. Multiplied across accounts, this resulted in CSMs spending 20 to 30 hours per quarter—almost a full work week—just building presentations.
• Inconsistent Data: The manual process required CSMs to pull metrics from up to five different dashboards, calculate benchmarks by hand, and then format the data into slides and build a narrative. Disconnected data sources often resulted in inconsistent metrics, which eroded client trust.
• Multi-Instance Management: Clients who manage 10 to 20 Drata client accounts (like Managed Security Service Providers) needed a unified dashboard. Previously, they had to log in and out of each account individually to check key metrics, which was exhausting and time-consuming.

The Solution: AI-Driven Analytics

Drata adopted a dual approach to Cube, using one consistent semantic layer to power two distinct consumption patterns: structured API queries and AI-powered natural language integration. Cube became their single source of truth, providing a consistent, governed foundation for analytics that scales. Crucially, the success of the AI-powered solution relied on Drata’s investment in an AI-focused semantic layer design. This involved ensuring clear, descriptive dimension and measure names, rich metadata, explicit relationships, and pre-calculating advanced metrics (like segment benchmarks).

“Cube becomes our single source of truth for metric definitions and powers everything from customer-facing dashboards to AI-driven quarterly business reviews. CSMs gain back dozens of hours each quarter and are equipped with everything they need to deliver intelligent storytelling, enabled by Cube’s semantic layer and agentic analytics. This represents a shift from static reporting to intelligent storytelling.”

— Anthony Cronander, Senior Analytics Engineer, Data

The Results: Intelligent Storytelling and Time Savings

By leveraging Cube, Drata achieved significant operational improvements, transitioning from manual effort to automated, strategic insights:

• Time Savings: CSMs get dozens of hours back in a quarter. The previous manual process consumed 20–30 hours per CSM per quarter
• Shift in Output: The workflow creates a shift from static reporting to intelligent storytelling. Cube generates data-driven narratives and insights written in natural language, not just numbers
• Strategy & Insight: Cube surfaces retention risks and expansion opportunities, comparing metrics to benchmarks and identifying outliers
• Customer Trust: Context injection allows Cube AI to prioritize relevant metrics based on customer concerns. The resulting QBRs feel tailored, not templated, demonstrating deep knowledge of each account, which builds trust and guides the relationship to a strategic partnership
• Scalability: Drata can build once and consume many ways (APIs, AI agents, embedded analytics) from a single rock-solid semantic foundation, ensuring consistency across all consumption layers