SPIFFE-Driven Identity and Trust Management for Secure Agentic AI Ecosystems

Abstract

The Agentic artificial intelligence ecosystems increasingly depend on multiple specialized agents that exchange information, invoke tools, access models, and coordinate decisions across distributed execution environments. Such architectures introduce an identity-management problem that differs from conventional user authentication because agents may be ephemeral, dynamically instantiated, independently deployed, and continuously involved in machine-to-machine interactions. This paper develops a conceptual SPIFFE-driven identity and trust management framework for secure agentic AI ecosystems. The study adopts a reference-driven analytical methodology, synthesizing the supplied literature on graph structures, recurrent and memory-augmented neural systems, relational representations, and SPIFFE-based zero-trust authentication. The framework conceptualizes agent identity as a continuously verifiable workload property rather than a persistent secret. It integrates workload identity, cryptographic authentication, trust establishment, identity-aware authorization, lifecycle management, and contextual relationship modeling. Particular emphasis is placed on the relationship between agent identity and the structured interactions that characterize multi-agent environments. The analysis indicates that SPIFFE-based workload identity can provide a strong authentication substrate for agent ecosystems while reducing dependence on long-lived credentials. However, identity alone does not establish behavioral trust, semantic correctness, or resistance to compromised legitimate workloads. Consequently, secure agentic AI requires a layered trust model in which cryptographic identity constitutes the foundation for, rather than the complete solution to, authorization and behavioral assurance.