There’s a perfect storm forming around enterprise AI adoption. As AI models consume live operational data at unprecedented speed, their roles are also evolving: no longer are they confined to only generating recommendations for decision-makers, but they are also making business-critical decisions independently. 

This is a massive opportunity — but creating complex, real-time pipelines without adequate governance, visibility, or guardrails is risky. Enterprise teams are racing to connect operational data sources (streaming platforms, databases, transactional systems) directly into AI models. But the infrastructure to govern, monitor, and secure these AI-native systems hasn’t kept pace.

We’ve seen this pattern before: as innovation accelerates, gaps in control and visibility emerge — leaving organizations scrambling to build these functions after the damage has already occurred.

The operational control gap for AI-native systems

At Conduktor, we’ve spent years helping enterprises unify and govern their operational data — long before AI entered the conversation.

But today, advancements in AI are redefining the entire data stack. Model Context Protocols (MCPs) enable algorithms to connect with external tools, services, and data, vastly widening their capabilities and enabling them to execute multi-step tasks. Agentic AI, which consists of networked AI agents, can act independent of human supervision. Lastly, retrieval-augmented generation (RAG) continually queries live data, ensuring accuracy and trustworthy outputs without resorting to retraining the entire model.

However, these new developments also raise new questions, such as:

• Which AI agents accessed which datasets?

• What decisions were made based on which data inputs?

• Were enterprise policies actually enforced in real-time agent interactions?

• How are we detecting and responding to unintended AI behaviors?

The control plane to answer these questions simply doesn't exist in most enterprises today.

Introducing Conduktor Trust for the AI era

That’s why we built Conduktor Trust — the first platform designed specifically to give enterprises full governance, observability, and policy control across real-time, AI-powered, operational systems. Whether agents are triggering operational workflows or RAG systems are querying live data, Trust enforces policies and creates audit trails at the point of action.

This is not a prototype or an idea — Trust is currently in preview and quickly attracting attention. 

Conduktor Trust gives enterprises:

• Full visibility into real-time, AI model-to-data interactions

• Policy enforcement embedded directly into AI agent workflows

• Audit trails across streaming data, databases, and retrieval pipelines

• The ability to identify and investigate data quality anomalies

• Guardrails that evolve as enterprise AI adoption scales

The goal is simple: to allow enterprises to move fast, deploy agentic AI, and innovate safely — without losing operational integrity.

Why focus on AI trust — and why now?

Today, AI is no longer just augmenting data science teams; it’s also becoming embedded directly into the operational fabric of business. Examples of AI usage today include: 

• Sales agents powered by LLMs

• Customer support copilots

• AI-assisted supply chain systems

• Real-time financial and risk management agents

As one CISO recently said to us: “We’ve given AI the keys to the operational kingdom. But we have no visibility into what rooms it’s actually accessing.”

Without operational control, AI-native systems risk becoming opaque, fragile, and dangerously self-reinforcing. This is exactly why Conduktor Trust exists — to give enterprises the visibility, control, and confidence to operationalize AI safely.

Helping enterprises navigate this perfect storm

By adopting Trust, engineers can more easily execute live AI audits across operational pipelines, thereby reducing downstream policy violations—and their associated costs. In addition, by baking in governance, teams can also speed up time-to-production for AI agent deployments. All of this ultimately improves confidence across data, security, and compliance teams. 

We’re not chasing the AI hype cycle. We’re focused on helping real enterprises address real operational risks emerging right now.

The AI operational governance storm is here. With the right foundation, enterprises don’t need to retreat — they can fly directly into the storm and accelerate.

If you're building your enterprise AI roadmap and facing these control challenges, we'd welcome a conversation. Get in touch with us here.