Enterprise AI integration for ADGM

The regulations are technology-neutral, so the same principles apply — lawful basis, purpose limitation, data-subject rights, transfer control and safeguards around automated decision-making. What AI changes is the difficulty of evidencing them, because training data, prompt logs and inference records are all personal data if they contain personal data. We treat those three as in-scope from the start.

Sometimes, and sometimes not. Document extraction, classification and rules-based automation work perfectly well on thin history. Demand or cash forecasting generally does not, and fitting one anyway produces a confident model that fails on the first regime change. Where history is insufficient we say so in discovery and sequence the learned models for later.

Yes, and we prefer to. Most of the infrastructure, access and change-management controls an AI system needs are already covered by an existing information-security management system. We map the AI-specific additions — model governance, training-data provenance, drift monitoring, human oversight — onto what you already hold rather than duplicating the estate.

Engineering, very little. Documentation, quite a lot: different instrument, different regulator, different terminology. The comparison table above sets out the practical differences we design around. Firms operating in both maintain two control mappings, not one.

Yes. The constraint that matters in a sandbox context is that whatever you build must be explainable and reversible, because the supervisory relationship depends on being able to describe precisely what the system does. That is compatible with our default design; it is incompatible with opaque end-to-end automation, which we do not build anyway.

In your own cloud tenancy, in-region, by default. UAE regions are available from the major providers, our engineers work through your access controls, and the training data never leaves your environment. Deviations from that default are named in the architecture and signed off, not assumed.

With a design document and a log. The design states, per decision type, who decides and what they see. The log records what they actually did, including disagreements and overrides. An oversight claim with no override records in eighteen months is a red flag we would raise with you ourselves.

Yes, and it is usually easier. Modern finance platforms tend to expose well-documented REST APIs and event streams, so the integration is a matter of contract design rather than reverse engineering. The architectural rule is unchanged: extend beside the system of record and write back as ordinary transactions.

Less than firms expect if an information-security management system already exists, considerably more if it does not. When we scope it we separate genuine AI-specific governance from general control work that was outstanding anyway, so you can see what is actually attributable to the AI programme.

We work under the client's outsourcing and third-party risk terms as standard, including audit rights, subcontractor disclosure, exit assistance and record retention. Firms in ADGM should expect their own regulatory obligations to flow down to us contractually, and they do.