Gradual Identity Upload Protocol: A Safety-First Neuro-Symbolic Framework for Cognitive Digital Twins and Brain–Computer Interfaces

Advances in brain–computer interfaces (BCIs), longitudinal personal data, and generative artificial intelligence have made it increasingly plausible to build persistent computational models of individual cognition. These systems, referred to here as cognitive digital twins (CDTs), may model, predict, simulate, or act as partial communicative and decisionmaking proxies for a person. However, the technical possibility of increasingly personal AI does not establish identity equivalence, continuity of consciousness, or the legitimacy of proxy action. This paper proposes the Gradual Identity Upload Protocol (GIUP): a safety-first research framework for incrementally constructing, testing, governing, and, where appropriate, retiring high-risk cognitive digital twins. GIUP treats “identity upload” not as a binary event but as a sequence of falsifiable, reversible, and auditable stages. It combines multimodal behavioral data, optional neural signals, neuro-symbolic representations, uncertainty estimation, longitudinal evaluation, version control, and human governance. The framework introduces three core concepts: identity fidelity, an ordinal, multi-dimensional assessment of behavioral and value-consistency under controlled evaluation—deliberately not collapsed into a single fitted score; epistemic drift, a set of independently thresholded indicators of divergence between a CDT’s outputs and the validated evidence about its source person; and reversibility, the ability to halt, isolate, roll back, or retire a model without irreversibly changing the person or delegating authority without consent. The paper proposes an Identity Turing Benchmark, a neuro-symbolic architecture for auditable proxy reasoning, a risk taxonomy for cognitive digital twins, and a governance model derived from the NIST AI Risk Management Framework and CDT-specific governance proposals. It is positioned explicitly against the closest adjacent framework in the literature, Gradual Cognitive Externalization (Zhao, 2026), which makes a functionalist metaphysical claim GIUP deliberately declines to make. The central claim is modest but consequential: before claims of mind uploading or digital immortality can be evaluated, the field needs rigorous methods for measuring what a model represents, what it does not represent, when it may act, and how humans can contest or terminate its authority. Current BCI platforms, including nanoparticle-based non-surgical approaches, may eventually supply new neural modalities, but they remain experimental and should not be treated as validated systems for decoding personal identity.