Blockchain-Enabled Security Architectures for Agentic AI: Threat Models, Accountability Mechanisms, and Preservation Strategies

Abstract

The emergence of agentic artificial intelligence represents a significant shift in how autonomous systems operate within distributed digital ecosystems. Unlike traditional AI models that function within predefined boundaries, agentic systems possess goal-oriented reasoning, adaptive learning capabilities, and the ability to coordinate with other autonomous entities. While these capabilities unlock substantial operational efficiency, they simultaneously introduce complex security, accountability, and governance challenges. Conventional centralized protection mechanisms often struggle to maintain integrity, transparency, and traceability when AI agents operate across decentralized and dynamic environments.

Blockchain technology offers a structurally different approach to trust management. By leveraging distributed consensus, cryptographic validation, and immutable record-keeping, blockchain infrastructures can provide an additional layer of resilience for agentic AI deployments. However, integrating blockchain with AI systems is not a straightforward solution; it introduces performance trade-offs, scalability concerns, and architectural complexities that must be carefully evaluated.

This paper critically examines blockchain-enabled security architectures designed for agentic AI systems. It explores threat models specific to autonomous multi-agent environments, evaluates accountability mechanisms such as decentralized identity and smart contract enforcement, and analyzes privacy-preserving strategies including federated learning and zero-knowledge proofs. Rather than presenting blockchain as a universal remedy, this review identifies both its strengths and limitations within real-world AI ecosystems. The findings highlight the importance of layered security architectures, hybrid on-chain/off-chain designs, and adaptive governance models to ensure trustworthy and scalable autonomous AI systems.