Private markets firms are rethinking the public-cloud-plus-external-AI default. Here is why data sovereignty is back at the top of the agenda, and how fund operations can have AI without surrendering control of fund and investor data.
For a decade the default was simple: move everything to the public cloud, and send your data to whatever service promised the most value. In private markets, that default is breaking down. The arrival of generative AI in fund operations has made the question sharper, because the data involved, fund accounting records, investor information, portfolio company financials, is exactly the data managers cannot afford to leak.
Because the data is too sensitive to hand over. Fund and investor data carries confidentiality obligations, competitive value and regulatory weight. Sending it to a third-party AI engine, where it may be logged, retained or used to train models, is a risk a growing number of managers, regulators and LPs are no longer willing to take.
The pull towards external AI is real: extraction, analytics and oversight are genuinely faster with modern models. But the value has to be captured without the data leaving the manager's control. That is the tension on-prem and private deployment resolve.
Once confidential data leaves your environment, you lose control of where it lives and how it is used. Third-party AI services vary widely in retention, logging and training policies. For a regulated manager, that creates exposure under data-protection regimes (for example GDPR restrictions on international transfers), client confidentiality terms, and the operational-resilience expectations of the FCA and CSSF.
For managers in the EU and the Middle East in particular, data residency is not a preference but a requirement. "Where does our data physically sit, and who can see it?" is now a standard question in every technology evaluation.
Not a return to racks in a basement. Modern on-prem means deployment flexibility: in the manager's own cloud tenancy, in a private cloud, or fully inside their own data centre, with the AI brought to the data rather than the data shipped to the AI. The model runs where the data already lives.
The principle is simple: bring AI to your data, not your data to AI. That keeps the speed of automation while keeping confidential information inside a boundary you control and can audit.
daappa runs multi-cloud, in your own cloud, or fully on-premise inside your environment. Extractor AI, DataHub, Analytics and the rest of Studio+ can operate without sending your data to any external AI engine. Where you already run your own internal AI and data infrastructure, daappa integrates with it, so you get the automation on top of the stack you have chosen and trust.
That means a fund administrator or manager can automate document extraction, run analytics and operate NAV oversight, with a full audit trail, while every piece of fund and investor data stays under their own control.
Enterprises and regulated managers who cannot let client data leave their control; firms with their own internal AI and data platforms who want to use them rather than a third party's; and managers in jurisdictions, notably the EU and Middle East, where data residency is non-negotiable. For all of them, on-prem is not nostalgia. It is how you adopt AI responsibly.
We will show you how daappa runs inside your own environment and integrates with your internal AI and data tools.
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