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Alan Woodward

Publications and source records attributed to Alan Woodward.

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Sovereign by necessity? Frontier AI export controls, cyber security, and the limits of national AI capability

A small number of firms based in two states produce the most capable frontier AI models. The governments of those states have shown both the legal power and the political will to decide which other countries may use these systems. In June 2026 the United States required a leading developer to obtain licences before releasing its most advanced models to any foreign person, including foreign nationals resident in the United States. The affected models were withdrawn worldwide at short notice, partly because the restriction proved impractical to administer. This followed within months of the first documented case of a largely autonomous, AI-run cyber espionage campaign, and coincided with mounting evidence that frontier models alter the economics of both cyber attack and cyber defence. This article examines how these two developments interact, and situates them within the unusual market dynamics now driving large-scale AI development. It argues that access to frontier AI is becoming part of national cyber defence, that such access can be revoked, and that the obvious remedy of sovereign capability remains only partly feasible for all but a handful of states. Drawing on evidence about training costs, the concentration of computing power and the support offered by national AI programmes, it asks what sovereignty can realistically mean for small and middle powers, and for large powers as well. The article proposes a layered strategy: negotiated access guarantees, sovereignty at the level of inference, hedging with open-weight models, pooled regional capability, sustained talent development and continued investment in basic cyber resilience. The open-weight hedge proves at once more capable and more politically exposed than is commonly assumed. Much of the near-term risk lies in how capable models are deployed and contained rather than in their apparent performance.

cs.AI

Quantifying Compromise Risk in Exceptional Access Architectures Under Sparse and Indirect Evidence

Lawful exceptional access (EA) systems hold the cryptographic keys that decrypt protected communications for authorised parties. The debate over their risks has been long and qualitative, complicated by two problems: no public dataset of EA-specific compromise events exists, so assessment must use sparse, indirect evidence; and prior work has treated structurally different designs as equivalent, though transmission-layer EA in carrier infrastructure (T-EA) and over-the-top EA at the platform layer (OTT-EA) differ in how cryptographic keys relate to ciphertext data. This paper builds a structured uncertainty framework for evaluating systemic compromise risk in EA architectures. It does not produce predictive forecasts, which the evidence cannot support; it separates findings robust to assumptions from those that depend on calibration. Four analytical layers are applied to T-EA and OTT-EA: three empirical pillars (historical analogues, a Monte Carlo scenario layer, a channel-independence decomposition) plus a Bayesian Structural Risk Model on a parallel-subgraph attack graph. The central findings are structural. First, EA-equipped architectures of either class carry strictly higher modelled risk than their no-EA counterfactual, an ordering independent of calibration. Second, the classes differ in distribution shape: T-EA risk is dominated by central tendency, OTT-EA by the tail under correlated campaigns. Third, calibration-conditional annual probability ranges span 1.4% to 12.9% for T-EA across the structured-judgement targeting-premium interval. Over multi-decade horizons, cumulative compromise is well above zero; key-material exfiltration is irreversible, weighing heavily on OTT-EA's larger user populations. The framework quantifies compromise probability, not expected harm; consequence modelling and benefit estimation are outside its scope.

cs.CR

Establishing Trust in Crowdsourced Data

Crowdsourced data supports real-time decision-making but faces challenges like misinformation, errors, and contributor power concentration. This study systematically examines trust management practices across platforms categorised as Volunteered Geographic Information, Wiki Ecosystems, Social Media, Mobile Crowdsensing, and Specialised Review and Environmental Crowdsourcing. Identified strengths include automated moderation and community validation, while limitations involve rapid data influx, niche oversight gaps, opaque trust metrics, and elite dominance. Proposed solutions incorporate advanced AI tools, transparent reputation metrics, decentralised moderation, structured community engagement, and a ``soft power'' strategy, aiming to equitably distribute decision-making authority and enhance overall data reliability.

cs.SI