After Deployment: Why High-Risk AI Requires a Continuous Evidence System

The Commission study indicates that deployment is not the end of conformity. Once a high-risk AI system operates, its data, version, users, integrations and intended purpose may change, weakening the representativeness of earlier evidence. The institutional problem of high-risk AI is not whether conformity documents were completed, but whether those documents continue to represent the operating system after deployment. Data, models, interfaces, deployment environments, user behaviour and intended purpose can change incrementally. Any one of those changes may reduce the continuing validity of the original risk assessment, testing and allocation of responsibility. This Analysis therefore does not treat evidence decay and continuous governance after deployment of high-risk AI as a self-contained technical or policy update. It separates the institutional facts supported by the official anchor, SNN editorial inference and outcomes that remain unverified. The reader should be able to see where the source ends, where interpretation begins and which conclusions the present evidence cannot support. To make the lead decision-ready, it answers five questions together: what has occurred, which first-party record supports it, through what mechanism the effect may travel, which outcome evidence is still missing, and what next observation could strengthen or overturn the judgement. Any causal relationship not stated by the source remains an editorial inference and is not converted into a factual claim through confident wording.

Providers, integrators and deployers control different parts of the AI lifecycle. Conventional governance concentrates classification, risk assessment, technical documentation and conformity decisions before release, followed by post-market monitoring. The AI Act places providers, importers, distributors and deployers in different responsibility positions, while a real system may combine a foundation model, application, custom configuration and customer process. Each actor controls different information. If the value chain exchanges only a conformity statement without version, purpose and change events, a downstream user cannot determine the configuration to which the evidence applies. Institutional context must identify the rule setter, implementer, data owner, reviewer and affected market. Those roles may sit in different organisations or functions, and publication of a document, deployment of a system, enterprise adoption and delivery of an outcome are different evidence states. Time and authority must also be separated. An announcement date is not an effective date; a pilot is not general adoption; a technical specification is not a legal obligation; and voluntary enterprise use is not regulatory approval. Putting these events on one timeline shows when an institutional development actually enters data, contract, investment or disclosure processes and which actor is authorised to make that transition.

The risk is not limited to system failure. Records may remain available while no longer describing the operating system. Distributed responsibility and incremental modifications make governance boundaries and reassessment duties harder to determine. Evidence Decay does not mean that the original document was false. It means that the distance between evidence and the actual system increases over time. Model weights may remain unchanged while the input population shifts; software may remain stable while use exceeds intended purpose; monitoring may identify bias while remediation is not reflected in the risk file. The documents exist but no longer support the present judgement completely. When upstream evidence lacks stable identity, formation time, applicable boundary and version, a standardised output may still be impossible to reconstruct. The material risk is not one missing field. It is the silent conversion of the wrong entity, an expired method, an inferred relationship or an unapproved version into an apparent fact as information moves downstream. Concrete failure modes include incorrect entity matching, an incomplete data population, inconsistent boundaries, unversioned methods or factors, exceptions without rationale, approval occurring after publication, and downstream reuse outside the original purpose. Each failure can turn a reasonable individual record into a conclusion that cannot be defended after aggregation, comparison or machine-assisted interpretation.

A continuous evidence system should bind system versions, purposes, data, responsible actors and governance boundaries. When material change occurs, it should identify which evidence remains valid, which needs supplementation and which requires reassessment. A continuous evidence system assigns stable identities to systems, models, datasets, deployments, uses, roles, risks, controls, incidents and corrective actions. Every update triggers an evidence impact assessment that identifies which tests, documents and approvals remain valid and which require supplement or repetition. The reasoning used to distinguish substantial modification from ordinary maintenance is also preserved. The implementable control unit is a governed evidence object. Each material claim links to its primary source, calculation or judgement method, organisational and temporal boundary, accountable owner, control state, exception, approval and version. When any component changes, the system preserves the difference and affected uses instead of overwriting the earlier basis. A minimum operating control set includes a claim register, evidence owner, source snapshot, method identity, valid period, control frequency, exception threshold, review, approval and permitted downstream use. High-judgement or high-financial-impact items receive a stronger review tier. Lower-risk records use automated completeness and consistency checks so that governance effort is concentrated where a wrong claim would change a decision.

The study draws on 544 consultation responses, three expert workshops with 166 recorded participations and a follow-up survey. Intended purpose, role allocation, value-chain information and substantial modification repeatedly emerged as implementation concerns. The Commission study supports claims about consultation scale and the importance of intended purpose, roles, value-chain exchange and substantial modification; the AI Act provides the legal architecture. Neither establishes that every high-risk system decays in the same way. Evidence Decay is an analytical concept describing changed applicability and does not replace legal classification or case-specific conformity judgement. Evidence assessment begins with the official anchor and uses independent primary or method-transparent sources to test context and limits. The sources support stated institutional facts and explicit figures. Claims of comprehensive adoption, causal improvement or universal cross-market effectiveness require separate implementation evidence. Every material sentence should enter a claim ledger and be classified as official fact, direct measurement, estimate, corporate commitment, delivered outcome or SNN editorial inference. The ledger records the precise scope that each source supports. Conflicting evidence is retained with the resolution rationale; absent evidence is marked pending rather than filled with a convenient analogue from another entity, period or jurisdiction.

Conformity therefore becomes a maintained condition rather than a one-time conclusion. Monitoring may detect change, but governance must also determine boundary shifts, responsibility and whether previous evidence still supports institutional reliance. Governance effectiveness therefore depends on whether monitoring signals change evidence status. An organisation that collects performance and incident indicators without updating risk, responsibility and use boundaries has more data but a static institutional judgement. Boards and regulators need to see not only that the system was monitored, but which prior conclusions were maintained, constrained or withdrawn as a result. The purpose of this information density is not length for its own sake. It is to shorten the verification distance between claim and decision. Boards, investors, regulators and operational teams should be able to distinguish fact, estimate, commitment, progress and outcome, then update the judgement when conditions change without reconstructing the case from scattered files and oral explanation. Accountability therefore attaches to decision rights. The data owner maintains the source, the method owner controls calculation, the business function defines the use case, internal control or assurance tests reproducibility, and the approver accepts responsibility for final use. An exception without an expiry date, remediation owner and impact scope stops being temporary treatment and becomes persistent evidence debt.

SNN editorial analysis: EU AI Act continuous-governance expectations reach Taiwan semiconductor, ICT, manufacturing, healthcare and finance through European customers and cross-border product responsibility. Changes to datasets, model versions, integrations, uses, deployment environments or responsible roles require renewed evidence-validity decisions. Taiwan semiconductor, ICT, medical, financial and manufacturing companies supplying AI components to Europe or deploying systems may act as providers, integrators and deployers simultaneously. Model versions, training or input data, customer configuration, use, incidents and remediation need to travel across organisational boundaries, while Taiwan voluntary frameworks remain distinct from EU legal obligations. One initial test report cannot be treated as permanently valid. For Taiwan, relevance should be traced through an actual transmission path. An international rule or customer requirement first enters finance, procurement, contract, supplier-data and assurance processes, then changes local systems and controls. It does not automatically become Taiwan law. Companies need to identify the applicable scenario, preserve bilingual mappings and make the evidence chain reviewable under controlled access. Taiwan companies can perform the transmission test on concrete objects: the company and legal entity, facility, product, batch, supplier, contract, financing instrument and disclosure field. Chinese and English names, internal and external classifications and different reporting frameworks should resolve to the same claim identity. Traceability must still preserve commercial confidentiality, personal data and access boundaries; it does not require unrestricted publication.

Future high-risk AI governance will depend on evidence flowing back from deployment, updates, incidents and corrective actions. Human review should verify the official study, the AI Act scope and the responsibilities assigned to each actor. Future evidence should follow EU guidance, standards, substantial-modification criteria and post-market monitoring practice. A low-regret action is to select one deployed AI system, reconstruct six months of data, configuration, purpose and responsibility changes, assess the impact on every original test and approval, and create remediation and reassessment controls for changes that were not recorded. Future monitoring should separate final text, technical guidance, adoption scope, operating controls, supervision and observable outcomes. A low-regret step is to select one high-risk claim for an end-to-end reconstruction test and record missing identity, source, method, accountability and version. That is governance preparation, not a compliance guarantee or forecast of results. Monitoring should be event-triggered as well as calendar-based. A final rule, amended technical guidance, expanded scope, supervisory action, adoption data or observed outcome creates a new version and a reassessment of the earlier judgement. The prior conclusion is not erased. It retains its original basis, identifies the new evidence that changed it and states which decisions or downstream uses now require review.