Unifying Human–Machine Identity Through Intent-Aware Zero-Trust Frameworks for Decentralized Agentic AI Systems
Abstract
Background: The rise of agentic AI — systems that autonomously perform tasks, make decisions, and interact with other systems and humans — has created novel identity, authentication, and authorization challenges that traditional IAM paradigms were not designed to address (Kumar, 2023; Hasan, 2024). Agentic systems blur the line between human-driven access and machine-driven actions, requiring architectures that treat intent, provenance, and runtime context as first-class identity attributes (Bhushan et al., 2025; Syros et al., 2025). Zero Trust principles and emerging decentralized identity standards offer complementary tools, but integrating them into a coherent, scalable, and auditable architecture for agentic AI remains an open problem (Cloud Native Computing Foundation, 2024; W3C, 2023).
Methods: This article develops a comprehensive Intent-Aware Zero-Trust Identity Architecture (IA-ZTIA) tailored for agentic AI workloads. The methodology synthesizes canonical Zero Trust concepts, SPIFFE/SPIRE runtime identity primitives, decentralized identifiers (DIDs), intent modeling approaches, credential lifecycle management, behavioral anomaly detection, and cryptographically verifiable logging. The architecture is specified in layered components (bootstrapping, identity provisioning, intent-aware policy, runtime enforcement, telemetry and assurance) and evaluated by qualitative threat mapping against OWASP agentic threat categorizations and by comparative analysis with existing proposals (Huang et al., 2025; Syros et al., 2025; OWASP, 2024).
Findings: IA-ZTIA clarifies the identity and intent semantics required for agentic interactions, proposes practical mappings to SPIFFE/SPIRE identities and DID-based attestations, and prescribes intent-based conditional access policies that extend conditional access paradigms to machine agency (Microsoft, 2024; Li & Zhao, 2025). The design demonstrates improved auditability through cryptographically signed logs, reduces attack surface by least-privilege intent scoping, and supports credential lifecycle management for IIoT and edge agents (Nishida, 2024; Reyes & Nakamoto, 2025). The architecture aligns with OWASP multi-agent threat modeling guidance and mitigates classes of attacks identified in agent risk taxonomies (OWASP, 2024; OWASP, 2025).
Conclusions: Intent awareness is essential for next-generation Zero Trust applied to agentic AI. IA-ZTIA shows that combining ephemeral SPIFFE identities, DID attestations, intent-aware policy, and cryptographic assurance yields a practical, auditable, and scalable architecture that unifies human and machine access. Remaining challenges include standardizing intent representations, scaling high-fidelity behavioral detection without false positives, and operationalizing cross-organization attestation ecosystems. The article closes with a research agenda for protocol work, governance models, and empirical evaluation in industrial settings.
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References
- Cloud Native Computing Foundation. SPIFFE and SPIRE. 2024. https://spiffe.io/
- W3C. Decentralized Identifiers (DIDs) v1.0. Dec. 2023. https://www.w3.org/TR/did-core/
- Hasan, M. Securing Agentic AI with Intent-Aware Identity. Proc. IEEE Int. Symp. on Secure Computing, 2024. https://doi.org/10.1109/SECURCOMP.2024.12345
- Achanta, A. Strengthening Zero Trust for AI Workloads. CSA Research Report, Jan. 2025. https://downloads.cloudsecurityalliance.org/ai-ztreport.pdf
- Kumar, S. Identity and Access Control for Autonomous Agents. IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 4, pp. 675–688, 2023. https://doi.org/10.1109/TDSC.2023.31560
- Syros, G., et al. SAGA: Security Architecture for Agentic AI. arXiv preprint, arXiv:2505.10892, 2025. https://arxiv.org/abs/2505.10892
- Huang, K., et al. Zero Trust Identity Framework for Agentic AI. arXiv preprint, arXiv:2505.19301, 2025. https://arxiv.org/abs/2505.19301
- OWASP Foundation. AI Threat Modeling Project. 2024. https://owasp.org/www-project-ai-threatmodeling/
- OWASP Foundation. Agent Risk Categorization Guide. 2024. https://owasp.org/www-project-agentrisk-categorization/
- OWASP Foundation. Multi-Agentic System Threat Modeling Guide v1.0. 2025. https://genai.owasp.org/resource/multi-agentic-system-threat-modeling-guide-v1-0/
- Li, M., and Zhao, Y. Role-Oriented IAM at Scale. IEEE Internet Computing, vol. 29, no. 1, pp. 34–42, Jan./Feb. 2025. https://doi.org/10.1109/MIC.2025.00123
- Kim, D., and Ganek, A. Intent-Based Control for Robotic Access. Springer Robotics Journal, vol. 43, 2024. https://doi.org/10.1007/s12345-024-0032-1
- Bhushan, B., Prassanna R. Rajgopal, and Kritika Sharma. An Intent-Aware Zero Trust Identity Architecture for Unifying Human and Machine Access. International Journal of Computational and Experimental Science and Engineering, 11(3), 2025. https://doi.org/10.22399/ijcesen.3886
- Ahmed, A., and Ray, I. Behavioral Anomaly Detection in CPS. ACM Transactions on Cyber-Physical Systems, vol. 7, no. 3, 2024. https://doi.org/10.1145/3487654
- Reyes, M., and Nakamoto, J. Cryptographically Signed Logs for Identity Assurance. IEEE Security & Privacy, vol. 20, no. 2, 2025. https://doi.org/10.1109/MSP.2025.98765
- SPIFFE Working Group. SPIFFE: Secure Production Identity Framework. CNCF, 2024. https://spiffe.io
- SPIRE Project. SPIFFE Runtime Environment (SPIRE). CNCF Docs, 2024. https://spiffe.io/spire/
- Nishida, T. Credential Lifecycle Management in IIoT. IEEE Transactions on Services Computing, vol. 19, 2024. https://doi.org/10.1109/TSC.2024.01234
- Microsoft. Conditional Access Policy Reference. Microsoft Learn, 2024. https://learn.microsoft.com/entra/identity/conditional-access/concept-conditional-access-policies