International Journal For Multidisciplinary Research

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A Secure, Trustworthy, and Regulated Framework for AI Agents in Distributed Networks

Author(s) Dr. Alex Mathew
Country United States
Abstract The rapid decentralization of Artificial Intelligence into autonomous agents operating within the network of clouds, edges, and IoT devices implies a paradigm shift in the field of security. The nature of distributed AI agents is inherently dangerous, as they are deployed in an environment where Byzantine failures and adversarial manipulations are prevalent, and perimeter-based defenses are ineffective. Secure in this case implies the cryptographic assertion of authenticated identity, data integrity, and authorized operations facilitated by a Zero-Trust Architecture. Trustworthy implies the quantifiable reliability, predictability, and responsibility of the decision-making process of an agent. We introduce the Trusted Agentic Mesh (TAM) as our primary contribution. This single system combines hardware-supported identity with a Byzantine Fault-Tolerant decentralized trust plane, featuring a novel Proof-of-Behavior consensus and a proactive governance plane, all of which are compliant with the NIST AI RMF. The results of the simulation show that TAM reports detection rates of more than 99 percent for both Sybil and collusion attacks and can control the natural performance cost of Byzantine resilience, thereby laying the groundwork for a blueprint of secure and accountable autonomous ecosystems.
Keywords Trustworthy AI, Autonomous AI Agents, Distributed Systems Security, Byzantine Fault Tolerance (BFT), Zero-Trust Architecture (ZTA), Proof-of-Behavior Consensus, Multi-Agent System (MAS) Security, AI Governance, NIST AI RMF, EU AI Act, Decentralized Trust Management, Secure Distributed Learning, Threat Modeling for AI
Field Computer > Network / Security
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-17
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.66724
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