How to Use AI in Compliance Training – 3 Use Cases You Can Use Today

If you're in compliance, you've likely heard the AI hype. You might even be facing pressure to do more with less using AI, yet struggling to figure out how to operationalize it, and we’re here to help.

The bottom line in compliance is still the same: regulators and boards are looking for evidence of effectiveness, real-time responsiveness, and tailored interventions. The good news is that AI can deliver this, but only when it’s used with intention.

In this article, we’ll focus on three practical AI compliance training use cases you can deploy today to increase program defensibility, precision, and operational efficiency, all without sacrificing control or compliance integrity.

Why AI in Compliance Training Deserves Attention

AI has moved beyond the innovation lab and into the compliance tech stack.

Why? Many compliance teams are under-resourced and tasked with managing complex global risk landscapes. At the same time, expectations around program rigor from regulators and internal stakeholders alike are rising.

Compliance teams are also increasingly expected to respond not just to policy violations, but to patterns of non-compliance. AI helps find the signal in the noise, translating data into action before a small issue becomes a systemic problem.

AI offers a way to:

• Segment training based on actual risk signals
• Translate content faster without losing fidelity
• Surface policy answers instantly without relying on institutional knowledge

While AI won’t replace core governance, it can reduce the manual burden of program administration, allowing your team to spend more time on investigation oversight, remediation planning, and strategic compliance decisions.

The key is treating AI as a governance asset, not just a productivity tool.

3 Compliance Training Use Cases Where AI Can Add Value Now

1. Risk-Segmented Training Deployment

Many compliance programs still rely on role-based or time-based training assignments. It’s better than nothing, but it isn’t truly risk-aligned.

AI makes it possible to ingest data from investigations, due diligence systems, and other third-party platforms alongside real behavior to dynamically identify which individuals or groups of employees pose elevated compliance risk and adjust training accordingly.

For example:

• An employee with repeated hotline mentions or control breaches might receive more frequent or tailored training
• Individuals approving high-dollar vendor invoices could be flagged for refresher anti-bribery modules
• Regional teams with low Speak Up rates might trigger cultural integrity or reporting pathway content

In a recent webinar, compliance expert and former CCO Andrew McBride shared what he is seeing in this space. By using third-party due diligence data and invoice approver metadata, compliance teams can retrofit training logic to ensure coverage where there is actually risk, not where an org chart says it exists.

This kind of segmentation ensures training isn't just “delivered”; it’s defensible. By showing your logic and tracing your triggers, you can prove that your program responds to evolving risk, not just job titles.

2. Policy Interpretation via Natural Language AI

Compliance policies don’t just need to exist; they need to be accessible to be used and understood. But for many employees, that’s easier said than done. Navigating dense PDFs or clunky intranets that easily get outdated often means they don’t get the guidance they need in the moment that matters.

That’s where natural language AI can help. These tools let employees ask real questions—like “Can I expense dinner if I’m traveling with a client in Germany?”—and receive direct links to the relevant clauses in your Code of Conduct, Gifts & Entertainment, or Data Privacy policies. Instead of memorizing definitions or hoping they interpret a policy correctly, employees can quickly locate authoritative answers, anchored in your actual documentation.

Importantly, the AI isn’t making a judgment call. It’s not approving expenses or interpreting legal nuance. It functions like a highly efficient Control-F: retrieving relevant excerpts, noting regional or regulatory context (e.g., Works Council approvals), and linking out to full policies.

To stay risk-aligned, these tools should be configured for retrieval-only output—no autogenerated “yes/no” responses or compliance decisions. That ensures policy interpretations remain centralized, auditable, and consistent.

While you could create a custom AI chatbot yourself, deployment, configuration, and testing can be complex. That’s why Ethena introduced Policy Bot, included for all Ethena customers.

3. AI-Powered Translation with Audit Controls

For global compliance teams, content localization is critical—but also expensive, time-consuming, and often incomplete. This often leaves a painful tradeoff involving big budgets and complexity around customizations.

AI-powered translation tools have matured to the point where they can rapidly generate multilingual versions of policies and training. But here’s the key: you still need to layer in compliance context and controls.

Some systems allow for:

• Side-by-side translation validation
• Custom glossaries for legal terms
• Flags for jurisdictions with strict Works Council or data privacy requirements (e.g., Germany)

The unlock here is that high-quality translation is more scalable and auditable than ever before, but it’s still best practice to include human reviews. That way, you retain control over tone, accuracy, and legal fidelity, all while cutting turnaround time from weeks to days.

Ethena now offers AI translations at no added cost.

Getting Started: Tactical Steps for Compliance Teams

You don’t need to overhaul your program or hire a data scientist to start using AI in compliance training. Here’s how to get started:

1. Identify Friction Points Tied to Risk

Ask yourself:

• Where are we relying on gut feel instead of data?
• Where do we get repeat questions from employees or managers?
• Which areas of our program would be hard to defend under audit?

These are your AI entry points for compliance training and policy management.

2. Map Your Data Sources

AI is only as good as the signals it can act on. Inventory your existing sources:

• Investigations data
• Due diligence results
• Monitoring platforms (e.g., controls management tools)
• Learning management systems (LMS)

Start thinking about how these could be integrated to trigger smarter, more tailored interventions.

3. Run a Pilot with Clear Controls

Start by picking one use case, like AI-enhanced translation for a new anti-harassment module or natural language Q&A for your Gifts & Hospitality policy.

From there, define the scope, establish review protocols, and document your guardrails (e.g., “AI suggests but does not auto-publish”). This creates a model you can replicate and scale.

The Bottom Line

AI has the power to be a compliance training accelerator. When used thoughtfully, it makes your program more adaptive, more risk-informed, and more audit-ready.

Whether it’s segmenting training based on actual exposure, surfacing the right policy guidance in the moment of need, or localizing content without blowing your budget, AI can help your team do more of what matters with fewer manual cycles.

The best part? This isn’t “someday.” These use cases are implementable right now, in real, everyday programs.

The goal isn’t just AI so that we can say we’re using AI: it’s smarter compliance, with better outcomes and stronger documentation.