AI You Can Trust: How We’re Building Secure, Contextual, and Composable Analytics

Most AI tools can give you answers. Very few can explain where those answers came from, or why you should trust them.

For many organizations, it’s no longer a question of if AI will be part of their analytics experience — it’s a matter of how.

But with that shift comes a new kind of trust challenge.

In traditional BI, trust was about consistent metrics, data lineage, and row-level access controls. It was a back-end concern — something you set once and relied on.

In AI-powered analytics, trust becomes something else entirely.

We’re now asking models to interpret questions, guide decisions, and surface new insights. The black-box nature of large language models introduces both power and uncertainty.

And that’s why trust in AI can’t just mean accuracy.

It has to mean intentional context, transparency, and control by design.

What Trust Actually Means in the Age of AI

“AI you can trust” is an easy marketing phrase. But when you break it down, trust in an enterprise AI analytics context really means five things:

• Privacy. Your data stays where it belongs. By default, no customer data is sent to external LLMs — AI responses are powered by metadata only. When needed, you can bring your own LLM and retain full control over its configuration and access.
• Context. The AI doesn’t just guess. It reasons within the bounds of your semantic layer — governed metrics, definitions, and business logic.
• Transparency. You understand what the model responded with, why it gave that response, and how it arrived at its conclusion. This includes visibility into the query exchange, key drivers, and supporting components — nothing is hidden.
• Control. AI is not “always on” — it’s enabled at the tenant level, with model-level controls and access governance built in.
• Governance. Every prompt, response, and outcome can be tracked, audited, and improved. AI is part of the product, not an uncontrolled plugin.

This kind of trust doesn’t happen by accident. It has to be designed.

Security is the foundation of trust in AI. While privacy and governance are essential, they both depend on one thing: keeping your data secure at every step. That’s why security in modern AI analytics platforms isn’t a layer — it’s built into the core. From how access is managed to how models are isolated and data is handled, every component follows strict enterprise-grade protocols. No shortcuts. No surprises. In the age of AI, protecting your data isn’t just a feature — it’s a prerequisite for everything else.

Why We Built GoodData AI This Way

We didn’t start with “how do we add AI to dashboards?”

We started with a different question: “How do we bring AI-native exploration into analytics, without compromising the trust that BI is built on?”

That led us to build:

• A semantic-first architecture, where every AI response is grounded in the logic you already trust.
• A metadata-only prompting model, keeping raw data secure and unexposed.
• A model orchestration layer that selects the right engine (LLM, SLM, ML) based on cost, complexity, and performance.
• Embeddable and brandable AI, so it fits seamlessly into your environment and workflows.

In other words: AI, yes. But AI that respects the governance, scale, and responsibility that modern analytics demands.

From Assistant to Infrastructure

The idea of “chatting with your data” isn’t new. But most AI assistants today are either too shallow to be useful or too risky to be trusted in real environments.

GoodData AI is more than just an assistant — it’s a governed analytics platform powered by AI. That distinction matters. Here’s what it enables at a glance:

• Multi-tenant deployments with workspace-level governance.
• Embedded AI for SaaS platforms and on-prem customers.
• Full audit trails for prompts and responses.
• A clear path from exploration to operational reporting.

What’s Next: Protocols, Ontologies, and Composability

We believe context is the missing piece in most AI experiences — and we’re not alone. That’s why we’re working with emerging standards like MCP (Model Context Protocol) to define how AI systems exchange meaning, not just text.

We’re also exploring how ontologies can align model outputs with business-specific language, especially in industries where terminology drives insight.

Combined with our composable AI architecture, this gives teams the ability to deploy AI in a way that’s private, contextual, and incrementally scalable — not all-or-nothing.

Final Thought: Speed is Good. Trust is Non-Negotiable.

AI can make analytics faster and more accessible. But that’s not enough.

It needs to be secure. It needs to be explainable. It needs to speak the language of your business and live inside the systems where decisions actually happen.

That’s what we’ve built GoodData AI to do — and why we believe the future of analytics isn’t just AI-powered, but trust-powered.