Strategic commitments shape collective cybersecurity under AI inequality

2026-05-10

AI Summary

This study addresses the systemic security risks arising from AI capability disparities, which hinder resource-constrained defenders from deploying effective protections. To mitigate this issue, the authors propose an evolutionary game-theoretic model that integrates commitment-based defense strategies with targeted subsidy mechanisms—a novel combination designed to incentivize the widespread adoption of strong defensive behaviors. Leveraging finite-population evolutionary dynamics, social learning rules, and multi-parameter simulations, both theoretical analysis and empirical results demonstrate that the proposed mechanism reliably converges to a high-security equilibrium in heterogeneous AI environments. This approach significantly increases the adoption rate of robust defenses, substantially reduces attack success rates, and simultaneously enhances defender welfare while effectively curbing attacker payoffs.

Abstract

The growing integration of AI into cybersecurity is reshaping the balance between attackers and defenders. When access to advanced AI-enabled defence tools is uneven, resource-limited defenders may be unable to adopt effective protection, creating persistent system vulnerabilities. We study the impact of differential AI access using an evolutionary game-theoretic model in a finite population. We first show that when high-capability defence is costly, the population is driven toward low-cost, weak-defence behaviour, sustaining attacks and weakening long-run security. To address this problem, we introduce differential access to AI defence tools by allowing defenders to choose between low- and high-capability protection based on their resources. We then examine the role of a small group of committed defenders who always adopt strong defence and influence others through social learning. Although commitment increases the prevalence of strong defence, it alone cannot stabilise secure outcomes due to high defence costs. We therefore incorporate a targeted subsidy to remove the cost disadvantage from committed defenders. Our analysis shows that subsidised commitment significantly increases strong defence adoption, suppresses successful attacks, and improves overall system resilience. Simulations across a broad parameter space confirm that subsidies consistently outperform commitment alone. In addition, social-welfare analysis shows improved defender outcomes while keeping attacker gains low. These findings suggest that targeted support for key defenders can be an effective mechanism for stabilising cybersecurity in AI-driven environments and provide a theoretical bridge between cybersecurity policy, AI governance, and strategic allocation of defensive AI capabilities.

Problem

• AI inequality
• cybersecurity
• strategic commitment
• defence disparity
• system vulnerability

Innovation

• evolutionary game theory
• AI inequality
• committed defenders
• targeted subsidy
• cybersecurity resilience