Secure Agent-Based Architectures for Decentralized AI Identity Management: The DAIS Framework

Abstract

This study proposes the multi-layered Decentralised Autonomous Identity System (DAIS) for safe, scalable, and policy-aware identity management for autonomous AI agents. DAIS uses distributed ledgers, trust registries, cryptographic credential processing, and adaptive governance principles to authenticate and authorise without central authority. For security and operational reliability, the architecture uses efficient verification techniques, Byzantine fault-tolerant consensus, hierarchical credential state management, and behaviour-aware trust scoring. The mathematical definitions of credential validation, revocation propagation, and consensus guarantees show how DAIS accomplishes constant-time verification and quick revocation dissemination over distributed systems. A 50-agent testbed, PBFT-based registry duplicates, and enterprise-grade hardware were used to evaluate DAIS to OAuth 2.0 and OpenID Connect under identical settings. DAIS regularly outperforms centralised solutions in latency, revocation speed, computational overhead, interoperability, and scalability, improving verification time by fourfold and revocation responsiveness by sixteen-fold. These findings show that DAIS is a durable, high-performance, and future-ready identification infrastructure for large autonomous agent ecosystems.