We're Launching The AI Trust Summit

While companies race to deploy AI agents that can automate finance workflows, customer operations, and compliance processes, a critical gap is widening. These systems need access to enterprise data to work, and that creates new risks that most organizations aren't fully prepared to manage.

That's why we're launching the AI Trust Summit 2026, a one-day virtual event, to lead the conversations about AI trust needs to happen now. Before more companies learn the hard way what happens when AI systems act on stale data, expose sensitive information, or make confident-sounding decisions based on ungoverned datasets.

Enterprise AI programs are rapidly moving beyond pilots and into production. But the moment AI starts interacting with real systems, real data, and real customers, the stakes change. The highest-value use cases, the ones leaders want most, are often the hardest to deploy safely.

What is AI trust?

AI Trust, at it's core, is about whether you can confidently deploy AI systems that access your enterprise data without creating business-critical risks.

Think about it: an AI agent helping your collections team needs access to customer account data, payment histories, and communication records. If that data is stale, inaccurate, or poisoned by a malicious actor, the agent might draft messages that damage customer relationships or violate compliance requirements. Even worse, it might expose personally identifiable information in contexts where it shouldn't appear.

Air Canada learned this lesson when their AI-powered chatbot incorrectly promised a bereavement fare discount that didn't exist. A tribunal held them legally liable for their agent's autonomous response. Whether the error came from a model hallucination or unverified internal data, the outcome was the same: real financial and reputational consequences.

At its core, AI Trust ensures AI systems are using the right data, with the right ermissions, under the right oversight.

Why AI trust matters more than ever

Agentic AI is expanding the surface area for automation faster than most security and governance frameworks can adapt.

Here's what we're seeing in enterprise deployments:

Data quality risks are unavoidable. Unlike other AI risks that can be managed through narrow permissions, data quality issues affect nearly every agent scenario. Data freshness changes daily or hourly, and agents can easily produce confident-sounding but incorrect outcomes when working with stale information.

Sensitive data exposure is widespread. Recent research shows that 8.5% of prompts submitted to major foundation models contain sensitive data, with nearly half categorized as customer information. Business users are already engaging with AI in workflows involving sensitive information, often without realizing the risk.

Ungoverned data creates hidden vulnerabilities. Enterprise data warehouses and lakes contain testing data, sample datasets, and other information not intended for production use. Agents may lack the context to distinguish between reliable, governed datasets and unreliable ones.

AI ROI slows not because the technology isn’t capable, but because organizations lack the operational controls needed to trust the outcomes.

What you'll leave with:

The AI Trust Summit is designed to address exactly these challenges.

Rather than another broad AI conference, the summit is a focused conversation about what it actually takes to run AI in production environments where reliability, governance, and accountability matter.

We'll bring together enterprise leaders who are actively working through these challenges: the CIOs deploying AI in finance and operations, the AI governance leaders establishing oversight frameworks, and the data leaders responsible for ensuring the systems behind AI are reliable.

The goal is simple: help enterprise leaders move from experimentation to defensible production AI.

Attendees will walk away with practical approaches for:

• Managing risk as AI systems access enterprise data
• Building governance that works in real environments
• Creating auditability and oversight for AI-driven decisions
• Ensuring the data powering AI systems is trustworthy

In other words, turning “AI trust” from a concept into an operational capability.

Join the conversation

If you're responsible for delivering AI outcomes (and managing the risk that comes with them) this summit is for you.

Registration is now open, explore the agenda and reserve your spot today.