Could your organisation produce legal-grade evidence of what your AI systems did? This whitepaper explains why the gap is an architecture problem, not a compliance problem and how to close it in 120 days.
Most large enterprises run their critical operations on systems that were architected before the current regulatory environment was conceivable. On-premise data centres running operationally complex applications built on legacy technology stacks form the backbone of how most large enterprises actually run. Batch ETL frameworks designed in the early 2010s, monolithic applications with tightly coupled business logic, identity systems built around human users, and logging infrastructure designed for operational debugging rather than evidentiary defence. None of this is broken. It is fit for the purpose it was designed for. The purpose has changed.
On 2 August 2026, the EU AI Act's high-risk AI obligations enter full force. They require things most legacy estates were never built to produce: documented data provenance, immutable audit trails, point-in-time queryability, automated logging at the decision layer, and ten-year technical documentation retention. The Digital Operational Resilience Act has been live since January 2025, requiring four-hour incident reporting from data infrastructure that most financial institutions cannot produce evidence from at speed. Industry research published in early 2026 found that 33% of organisations have no evidence-quality audit trails at all. A further 61% have fragmented logs spread across systems that cannot be reconciled into a defensible evidentiary record. The gap between what the regulators now expect and what legacy estates can produce is no longer a future problem. It is a current architectural liability.
