Designing Secure Composite AI Systems: Cross-Domain Holistic Threat Model and Mitigation Framework

Composite artificial intelligence (AI) systems are increasingly deployed in mission-critical environments, such as defence, aerospace, industrial control systems, and critical infrastructure, where they enable adaptive, autonomous, and real-time decision-making. However, the growing complexity of these systems introduces multilayered security risks that extend far beyond the assumptions of traditional, component-centric security models. In this work, we introduce a structured taxonomy that decomposes composite AI systems into five tightly interconnected layers: core AI and machine-learning (ML) components, integration and orchestration mechanisms, data flows and shared computational resources, cross-layer system interactions and emergent vulnerabilities, and legacy or deterministic software modules that coexist with AI. Leveraging this taxonomy, we propose a holistic, cross-domain threat modelling approach to systematically identify threats, architectural weaknesses, and design-level security flaws across the entire system lifecycle. Finally, we outline mitigation strategies and architectural best practices aimed at building secure, resilient, and trustworthy composite AI systems c