Technolegal De-risking of Probabilistic AI Algorithms with Confidential Data Flows

The convergence of confidential computing and probabilistic AI opens a unique frontier in which technological assurance and legal enforceability must evolve in tandem and reinforce each other. Existing approaches to confidential data processing (spanning CPU/GPU enclaves, WebAssembly-based isolation, and hardware attestation) promise strong technical guarantees of execution integrity, yet fall short as a sustainable and extensible legal foundation in an era where European law adopts an increasingly restrictive attitude towards AI applications. Such technical guarantees will therefore remain incomplete without demonstrable parallel legal embedding mechanisms that define, constrain, and enforce the kind of data that may flow into, within, and out of these environments in a fast and secure manner. This PhD investigates the technological embedding and implementation of  minimization – a cornerstone of data protection law – as a computational design principle. To businesses with high-sensitivity data flows, our pioneering research derisks crucial operational processes by provably ensuring only strictly necessary data fragments traverse confidential pipelines at precisely defined moments, across heterogeneous hardware. By orchestrating legally validated secure flows of information between CPUs, GPUs, and domain-specific architectures, we define technolegal patterns that reconcile the opacity of probabilistic AI algorithms with the increasing need for transparency demanded by overarching legislation. Our ambition leads us  to couple technical attestation with strong, enforceable legal guarantees, such that businesses may safely derive insights from data they cannot access in raw form. In doing so, this research seeks to spearhead a new class of secure, trusted compute patterns that simultaneously push the boundaries of engineering feasibility and legal possibility.