Highly regulated financial institutions can make generative AI (genAI) usable by governing the data estate before scaling the model. This means prioritizing key areas of governance such as information classification, remediating excessive access and establishing accountable oversight so the AI can operate effectively on controlled and auditable data.
It also means introducing balance. On one hand AI is a powerful productivity tool that has the potential to change how teams and systems operate; on the other, it is a growing risk and point of vulnerability that organizations must address from the outset. As the Microsoft Data Security Index pointed out – 32% of companies surveyed had experienced a data security incident because of genAI, and 88% are planning on increasing their budgets to align with the demands of compliance and data security.
Key takeaways:
• 32% of organizations surveyed in the Microsoft 2026 Data Security Index experienced a data security incident involving generative AI and 35% expect these incidents to increase.
• Most GenAI risk in financial services sits upstream in the data estate, in classification, access controls and policy, not in the model itself.
• Data classification and access control are prerequisites for usable AI in regulated environments, not post-deployment fixes.
• Secure Copilot and Dynamics 365 deployment depends on native Microsoft controls: Microsoft Purview, Sensitivity Labels, Data Loss Prevention and the Copilot Control System.
• Moving from pilot to production requires a staged operating model that includes readiness and risk assessment, controlled pilot, then governed scale.
Why is it important to understand why AI pilots stall in highly regulated institutions?
Pilots stall because AI exposes weaknesses that already exist. These are often found in over-permissioned access, unlabelled or poorly classified data, inconsistent policies and unclear accountability. It isn’t the model underperforming, it’s the systems holding back its potential or inhibiting its capabilities with weak data and poor controls. In financial institutions, where a model may reason over customer, transactional and compliance data simultaneously, these gaps can have a direct legal and reputational cost.
The truth right now is still the same as it has always been when it comes to AI: If you put it on top of any broken process, the process will remain broken but it will speed up. And that speed will only create a deeper fracture between the institution and the customer. Pilots can only scale if the work is done on the system, the data estate and its controls.
What does a governed Microsoft stack look like in financial services?
A governed Microsoft stack rests on native controls already in the estate. These include key components such as Microsoft Purview, Sensitivity Labels and Data Loss Prevention tools that classify data and constrain how it moves. For Microsoft 365 Copilot, the Copilot Control System adds the governance layer to monitor, manage and respond to AI-related risks. The result is that AI then operates within a data estate that’s classified and controlled and auditable.
Governance and security are not the same discipline, however. Governance defines ownership and accountability and policy while security enforces the controls and detects the threats. Strong security over weak governance protects data the institution does not fully understand. Clear governance over weak security runs the risk of classifying data it cannot adequately protect. And the gap between these two disciplines is where the AI risk lives.
How do regulated institutions move genAI from pilot to production?
The successful route from pilot to production doesn’t sit within one single deployment as there are multiple layers and considerations. The institution needs to progress through clear stages to get to the point where AI is flourishing and where governance and risk are effectively managed. It starts with a readiness and risk assessment that determines exactly where the gaps lie, and then follows through to a controlled pilot activation, then to governed scale, and then it is built out within enterprise governance frameworks and a Centre of Excellence. This includes a defined path to retrofit AI that is already live.
This is where Mint has delivered consistently when supporting highly regulated institutions with roadmaps and implementations that turn AI from a concern into an asset. With Mint’s expertise, companies can turn their genAI into an explainable, auditable and human-in-the-loop tool that delivers real value rather than becoming an unmanaged risk.
For regulated finance, the institutions finding success with genAI are the ones who are moving at a speed that’s fit for their purpose. They’re first classifying, controlling and auditing before scaling so their pilots are built on the right foundations. And this steady approach is giving them more control over the potential risks of AI while still benefitting from its capabilities.
Speak to Mint about building the governance architecture that lets your institution move GenAI from pilot to production without compromising compliance.