The Game-Theoretic Symbiosis of Trust and AI in Networked Systems
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Abstract (arXiv)
This chapter explores the symbiotic relationship between Artificial Intelligence (AI) and trust in networked systems, focusing on how these two elements reinforce each other in strategic cybersecurity contexts. AI's capabilities in data processing, learning, and real-time response offer unprecedented support for managing trust in dynamic, complex networks. However, the successful integration of AI also hinges on the trustworthiness of AI systems themselves. Using a game-theoretic framework, this chapter presents approaches to trust evaluation, the strategic role of AI in cybersecurity, and governance frameworks that ensure responsible AI deployment. We investigate how trust, when dynamically managed through AI, can form a resilient security ecosystem. By examining trust as both an AI output and an AI requirement, this chapter sets the foundation for a positive feedback loop where AI enhances network security and the trust placed in AI systems fosters their adoption.
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References · 13
- 2024PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediationcited by 30
- 2024Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Gamescited by 14
- 2023Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approachcited by 12
- 2024Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theoriescited by 9
- 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attackscited by 9
- 2024Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfarecited by 8
- 2015Deception by Design: Evidence-Based Signaling Games for Network Defensecited by 4
- 2021Informational Design of Dynamic Multi-Agent Systemcited by 4
- 2022Accountability and Insurance in IoT Supply Chaincited by 2
- 2023Zero Trust for Cyber Resiliencecited by 2
- 2024Attributing Responsibility in AI-Induced Incidents: A Computational Reflective Equilibrium Framework for Accountabilitycited by 2
- 2022Commitment with Signaling under Double-sided Information Asymmetrycited by 1
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