As decentralized AI systems become more prevalent, ensuring privacy, security, and trust is no longer optional—it is essential. Existing approaches often address these concerns in isolation, leaving critical gaps in accountability and resilience. This paper proposes a multi-layer architecture that integrates privacy, security, and trust as interconnected components of responsible AI design. The Privacy Layer applies differential privacy and data minimization techniques, the Security Layer ensures encrypted communication and authenticated computation, and the Trust Layer supports auditability through dynamic reputation scoring and blockchain-based logging. Together, these layers form a cohesive and adaptable framework suitable for sectors such as healthcare, finance, and smart cities. To validate these concepts, we present a conceptual simulation illustrating trust–privacy–utility trade-offs, supported by a qualitative comparison with existing frameworks and a security analysis. While not a substitute for large-scale implementation, this conceptual validation highlights the feasibility of our approach and identifies key engineering challenges. By aligning technical mechanisms with legal and ethical requirements, the paper offers a foundational blueprint for building decentralized AI systems that are not only secure and private but also accountable.