Risk modeling, fraud detection, and financial analysis on confidential data that must stay within your jurisdiction — plus cross-institution AML collaboration without sharing raw records.
Financial institutions operate under strict data residency mandates, regulatory frameworks, and competitive pressure that prohibit sending raw financial data to a commercial cloud API. Every prompt sent to a hosted model is a potential compliance violation.
Private AI for financial services: risk, fraud, and analysis on confidential financial data — with the governance and audit trail regulators require.
Cross-bank AML collaboration: run shared fraud and risk models across institutions without pooling raw transaction data.
Explore Prism AIEU financial institutions operating AI systems face DORA (Digital Operational Resilience Act, in force January 2025), GDPR, MiFID II data governance requirements, and sector-specific data residency mandates from national regulators. DORA specifically requires managing ICT third-party risk — making cloud AI APIs a regulated dependency requiring documentation, audit rights, and exit plans. On-premise AI eliminates this third-party ICT risk entirely.
Yes. DORA (in force January 2025) requires EU financial entities to manage ICT third-party risk under Articles 28–30. Cloud AI APIs — including generative AI services from major providers — are ICT third-party services under DORA. Financial institutions must maintain contractual documentation, ensure audit rights, and have exit plans. Deploying AI on-premise removes the cloud AI provider from the third-party ICT risk register.
Prism AI enables multi-party AI collaboration using Trusted Execution Environments. Each bank's transaction data stays sealed in its own TEE — no other party, including the TEE operator, can read it. A shared AML model is trained or evaluated across all parties' data, with only the result returned. This satisfies both the technical requirement (data never leaves each bank) and the regulatory requirement (no raw data sharing between competitors).
Data residency for AI means that the data processed by the AI system — prompts, documents, query results, model outputs — must physically reside and be processed within a specified jurisdiction. For cloud AI APIs, data residency is a contractual commitment subject to the provider's technical architecture. For on-premise AI deployment, data residency is a hardware guarantee: data cannot leave the perimeter because there is no path for it to do so.
Cube AI's audit trail records every AI interaction — model, user, timestamp, input summary, output summary, guardrail decisions — in a queryable, exportable log stored inside your perimeter. For MiFID II and national regulator audit requirements, this provides a complete, accurate record of AI-assisted decisions. The audit trail is owned by the financial institution, not the AI vendor, and can be produced directly to regulators without intermediary involvement.
Confidential computing (AMD SEV-SNP, Intel TDX) protects financial AI workloads at the hardware level — model weights, inference inputs, and outputs are encrypted in CPU memory, invisible to the host OS, hypervisor, and cloud operator. This is relevant for financial institutions processing proprietary risk models, trading algorithms, or client data on shared or co-located infrastructure where hardware-level isolation is required.
Talk to the team about financial services AI deployments, data residency mandates, and cross-bank collaboration.
