Senior leaders want the benefits of AI—faster decisions, leaner processes, and better compliance—without jeopardizing confidentiality or the integrity of the promises made to customers, regulators, and auditors. The good news: you can use AI on your company’s most sensitive data without leaking information if you architect for least-privilege access, private networking, and rigorous governance from day one. Microsoft Azure provides the right foundations and certifications (including ISO/IEC 27001), plus native controls to enforce policy at scale. In practice, you keep models and data inside your tenant boundary, apply Azure Policy for compliance, and back it with auditable controls.  

This article sets out a practitioner-friendly blueprint for IT and management teams: what “good” looks like for secure AI, where to start on Azure (AI Foundry, Prompt Flow for RAG, content guardrails, AKS/Docker hosting), how to choose between system prompts and fine-tuning, and a 90-day plan to move from a pilot to an enterprise platform. We conclude by discussing how RSM can help you get there—safely and efficiently.

This article was written by Mourad Seghir ([email protected]) and Sascha Sieffers ([email protected])). Mourad & Sascha are consultants with RSM Netherlands Business Consulting, focusing on AI, technology, and business strategy.

What “good” looks like: Secure by design

Secure AI requires a disciplined approach. It is not only about deploying a model; it is about embedding the right governance, controls, and operating principles from day one. At its core, a secure AI environment should remain entirely within the company’s trusted perimeter. Public access is disabled, and only private endpoints are exposed; sensitive keys or credentials are never hard coded in application code. Encryption, both at rest and in transit, becomes non-negotiable, ideally supported by customer-managed keys stored in Azure Key Vault. These measures are not abstract technicalities; they are the practical steps that allow management teams to map AI operations to ISO 27001 controls and to demonstrate compliance during audits. Equally important is how the AI model interacts with information. Instead of generating answers in isolation, leading practice is to ground the model’s responses in the company’s own data through retrieval-augmented generation (RAG). This ensures that outputs are anchored in verifiable evidence, reducing the risk of “hallucinations” and helping employees trust the answers they receive. Finally, AI must be treated like other mission-critical business systems. This means versioning prompts and datasets, defining service-level objectives (SLOs) for performance, and running regular “red team” exercises to test resilience against misuse or malicious input. 

Where to start on Azure

Microsoft Azure offers a comprehensive suite of tools that enable the design of innovative and secure AI systems.

  • The AI Foundation 
    Azure AI Foundry acts as the central hub for building and governing AI applications. It includes a model catalogue, safety policies, and integration with enterprise identity systems. The platform supports isolation, ensuring that experimentation never risks crossing security boundaries.
  • The AI Logic 
    Prompt Flow offers a structured workbench for developing retrieval pipelines. Here, companies can test and optimize how prompts are combined with internal data, measure accuracy, and iterate safely before releasing to employees.
  • Business Policies 
    Azure AI Content Safety provides filters for text and images, helping organisations protect against harmful or unsubstantiated outputs.
  • Making it accessible to the whole team 
    Azure Kubernetes Service (AKS) allows companies to host AI assistants at scale. By deploying containerized applications behind API Management and Web Application Firewalls, organisations gain enterprise-grade reliability, autoscaling, and resilience.

This combination of tools enables businesses to start small, demonstrate value, and then expand without needing to reinvent the architecture each time.

What AI Changes in Business Functions

Different parts of the business will see different benefits from AI adoption. The examples below highlight where companies can begin to see tangible improvements.

  • Tax: AI can automate repetitive tasks such as preparing workpapers, classifying transactions, or drafting supporting narratives. CFOs should view this as a way to reduce cycle times and errors, while still maintaining human oversight for judgement-based decisions.
  • Supply Chain: By predicting delivery times, triaging exceptions, and identifying supplier risks, AI enhances resilience and visibility. The real value lies in reducing surprises and improving service levels.
  • Legal: AI can support clause extraction, playbook-guided reviews, and horizon scanning. However, governance is essential to ensure that confidentiality and bias controls are in place.

A Step-by-Step Azure Blueprint

Scaling AI from a pilot to a platform requires a structured, time-bound approach. The following 90-day roadmap offers a tested sequence of actions.

  • Establish a secure landing zone. This involves setting up management groups, applying ISO 27001 policy initiatives, and enforcing strict networking boundaries with private links. Success metrics should be defined early—for example, reduced turnaround time or improved accuracy of outputs.
  • Deploy an initial model using Azure AI Foundry, connect it to a small but high-quality data corpus, and orchestrate retrieval with Prompt Flow. The pilot should be tested by a limited group of users, with every answer logged and sources recorded for auditability.
  • Transition to Azure Kubernetes Service for production-ready hosting, integrate cost controls, and establish approval workflows for new data sources. Monitoring and alerting should be connected to enterprise security systems, ensuring issues are identified quickly.
  • Resolve any findings from red-team exercises, document operational runbooks, and formalise service-level objectives. Once stability is proven, expand the solution to the next business area, such as supply chain or legal, using retrieval filters to tailor results. 

System prompts vs. fine-tuning (and why RAG wins early)

Most enterprise assistants don’t need fine-tuning on day one. Start with a clear system prompt that enforces tone, scope, and citation rules. Combine it with high-quality retrieval over your policies, contracts, or SOPs. Fine-tune only when you need rigid output formats at scale, domain-specific style fidelity, or when RAG cannot bridge knowledge gaps (for example, dense tabular reasoning locked in scanned PDFs). This approach reduces costs and avoids training on confidential data that is not essential for governance and change control.

Common pitfalls (and how to avoid them)

While the roadmap is straightforward, businesses often encounter challenges that slow progress. Three stand out:

  1. Rushing into fine-tuning – Training customized models is costly and unnecessary for most early use cases. System prompts combined with RAG are often sufficient.
  2. Weak retrieval practices – Poorly organized or outdated data will undermine AI performance. Investment in data quality and document lineage is essential.
  3. Uncontrolled pilots – Shadow AI projects outside the landing zone create long-term risks. Establishing a centralized “paved path” reduces this problem.

Why Azure is a pragmatic choice for secure enterprise AI

Azure’s strength lies in its consistency. With one identity model, one set of policies, and one compliance framework, businesses avoid fragmentation and duplication. AI Foundry provides governance, Prompt Flow offers a repeatable approach to building assistants, and AKS ensures enterprise-grade deployment. Together, these components mean that organisations can grow AI safely without creating silos or exposing themselves to unnecessary risk. With AI Foundry you get the model catalogue, deployment governance, evaluation, agent services and policy hooks in one place. Prompt Flow gives your teams a repeatable way to build and test RAG. AKS provides an enterprise-grade runtime for internal apps, and Azure’s compliance portfolio and ISO 27001 alignment simplify control mapping and audits. In short: one cloud, one identity model, one set of controls from experiment to production.  

Forward thinking

AI is no longer a skunkworks experiment; it’s a capability you can (and should) run like any other mission-critical system. Start with security and governance, build a thin but solid foundation, and scale what works.  

At RSM Netherlands, we help organisations align AI adoption with business goals, map security measures to regulatory frameworks, and design operating models that can be trusted by both engineers and auditors. Our support spans four key areas:

  • Strategy & Governance: Defining the right controls and policies from the outset.
  • Architecture & Build: Implementing the secure landing zone, deploying models, and creating retrieval flows.
  • Data & Quality: Building reliable ingestion pipelines and ensuring high-quality retrieval.
  • Operations & Adoption: Setting service standards, monitoring performance, and training teams to own the solution.

The result is a secure AI platform that delivers measurable business value in as little as 90 days.

RSM is a thought leader in the field of Strategy and Digital Law consulting. We provide frequent insights through training and the sharing of thought leadership, based on our detailed knowledge of industry developments and practical applications gained from working with our customers. To discuss a secure AI roadmap tailored to your environment, please contact one of our consultants. 

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