Building GenAI in Regulated Industries: A Guide to Secure, Compliant AI

There’s no denying it: generative AI is one of the most powerful technologies to hit the enterprise in decades. But if you’re operating in a regulated industry, the question isn’t just how to adopt GenAI - it’s how to do it while meeting regulatory, privacy and compliance requirements.

We’re hearing this tension everywhere: business teams want to move fast and integrate GenAI into everything from legal review to IT automation, while compliance and security teams are waving red flags about data privacy, residency, auditability, and vendor risk.

Here’s the good news: you can adopt GenAI in a way that meets the highest bar for security and compliance. But it requires being intentional about infrastructure, deployment, and governance. This blog post walks through the best practices we’re seeing from working with regulated enterprises.

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Why public GenAI APIs don’t cut it (for most regulated use cases)

Cloud-hosted APIs like Azure OpenAI, Bedrock or Vertex are fantastic for experimentation. But when it comes to production use cases that involve sensitive or proprietary data, many legal and compliance teams are shutting them down. Why?

1. Data residency and localization: In many industries and jurisdictions, regulations require that customer data stays within national or regional borders. Sending it to a US-based, multi-tenant data center often breaches those rules. Because hosted APIs typically only support certain models in certain regions - and are always multi-tenant - it becomes nearly impossible to meet these residency and isolation requirements.
   
   
   • Example: In the EU, GDPR prohibits the transfer of personal data outside the EU unless certain safeguards are met. That means using a US-hosted LLM API to process any client information could trigger compliance violations. Similarly, firms operating in Luxembourg must comply with CSSF rules that demand full transparency into where and how data is stored.
     
2. Multi-tenancy concerns: Even if data is encrypted, public LLMs run in environments where data from multiple customers is processed together. That’s a non-starter for firms that require complete isolation.
   
   
   • Example: If you're a financial institution processing sensitive M&A documents, the idea that your prompts and outputs are handled in a shared infrastructure raises major concerns. Even if logical separation exists, regulators and CISOs often prefer true tenant isolation to prevent any chance of data crossover or exposure.
     
3. IP and confidentiality: There’s lingering concern that your proprietary prompts, data, or outputs could be retained, learned from, or exposed - even if the terms say otherwise. Many customer contracts also prohibit sharing data with 3rd party services.
   
   
   • Example: A pharmaceutical company exploring drug discovery use cases using GenAI won’t risk uploading internal R&D documents to an external model if there’s any ambiguity about data retention or model training. Despite vendor assurances, the legal team may point to ongoing lawsuits against major AI companies over copyright and data usage as red flags.
     
4. Auditability and explainability: It’s hard to audit or explain the behavior of a closed, third-party model. If a regulator asks what data was used, who accessed it, or why a model gave a certain output, most public APIs can’t provide that visibility. Hosted models also tend to change frequently - models are updated or retrained behind the scenes without prior notice, which makes it even harder to reproduce past results or demonstrate consistent, explainable behavior to auditors.
   
   
   • Example: A healthcare provider using GenAI for patient communications may be required to demonstrate how an AI-generated message was produced, what model generated it, and whether it adhered to internal safety rules. If the model is a black-box API, they won’t be able to show that full audit trail - which is often a hard requirement in sectors like healthcare, insurance, and finance.

Trends in Enterprise GenAI for Regulated Industries

Across finance, healthcare, energy, and other regulated sectors, there’s a growing wave of tech and data leaders that are reaching the same conclusion: we need a GenAI strategy that gives us full control over where models run and how data is handled.

Specifically, we’re seeing:

• A shift away from single-vendor, cloud-native GenAI services
• A move toward single tenant cloud, multi-cloud or on-prem deployment options
• An insistence on full audit logs, monitoring, and policy enforcement
• A growing interest in modular, containerized inference that can be deployed anywhere, with minimal lift
  
  

Compliant GenAI Stack Architecture for Regulated Enterprises

Here’s what we think is becoming the gold standard for building GenAI in regulated environments:

1. Self-hosted inference

Run models in your own environment - whether that’s on-prem, in a VPC, or across multiple clouds. That way, you control where data is processed, who can access it, and how models are monitored. Bonus: this lets you meet data residency and sovereignty requirements without building entirely separate applications for each jurisdiction. See how a global pharma achieved that here.

2. Bring-your-own-model architecture

Use open-source or commercially licensed models, not black boxes. This gives you control over what’s running in your stack, and makes it easier to validate, fine-tune, or swap models out in the future. For example, Digits achieved nearly a 50% increase in accuracy when switching from general LLMs to custom, fine-tuned models.

3. Abstraction and routing layers

Build (or adopt) a middleware layer that abstracts away the underlying models and routes requests based on policy. This gives your teams a consistent API to work with, while letting you enforce different rules for different use cases (e.g. smaller models for light-touch internal tasks, larger models for high-impact analysis).

4. Auditability and observability by default

Log every request and response, integrate with your existing monitoring tools (e.g. Datadog, Splunk), and make sure every use of a model is traceable. This is essential for internal governance and optimising the configuration of the stack, but it also means you’re ready when regulators come knocking.

5. Privacy-preserving data pipelines

Build data handling rules into your stack: classify what data can be used where, strip or obfuscate sensitive fields before they hit the model, and make sure outputs are also reviewed and logged appropriately. Some firms are even using automated layers to scrub PII from prompts before inference.

6. Modularity and portability

Make it easy to migrate workloads between environments. That means containerization, infrastructure-as-code, working with OpenAI compatible APIs and avoiding lock-in to any specific cloud, model provider, or orchestration tool. If your compliance team blocks a provider tomorrow, you should be able to redeploy with minimal friction.

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Start small, scale safely

We’re seeing the most success with companies that pick a few back-office or low-risk use cases (e.g. summarizing internal docs, supporting finance workflows), build a compliant foundation there, and then expand to higher-value use cases once the stack and controls are proven.

The key is to get your governance, observability, and deployment model right from day one. That doesn’t mean weeks of red tape - it means starting with the right principles so that you don’t have to unwind everything six months from now.

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Final thought

GenAI doesn’t need to be scary. But it does need to be controlled. For regulated enterprises, the best path forward is clear: bring GenAI to your data, not the other way around. Build with flexibility, auditability, and security baked in. And give your business teams the power of AI without putting your company’s trust, reputation, or legal standing at risk.

If you're navigating this right now, you're not alone. And the right architecture makes all the difference.

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