The paper proposes a Semantic Gateway and a Zero-Trust security model to formally validate and secure autonomous AI agents operating in enterprise systems, achieving a 100% discovery rate of unauthori…

The paper defines AI Identity as the correspondence between an agent's declared state and its observed behavior, concluding that current infrastructure and standards are fundamentally inadequate for g…

This pilot study investigates SME readiness for Zero Trust Architecture (ZTA) and proposes a realistic three-stage adoption path based on survey data from IT professionals.

The paper introduces AgentWard, a lifecycle-oriented, defense-in-depth architecture designed to systematically secure autonomous AI agents by protecting them across all stages of their operation.

The paper proposes and validates a comprehensive four-layer Zero Trust security architecture designed to mitigate critical vulnerabilities in autonomous AI agents handling Protected Health Information…

The paper systematizes the interaction between autonomous AI agents and blockchain platforms using a bidirectional trust framework, identifying significant gaps in current standards and proposing a ta…

The paper argues that LLM agent security is fundamentally an agent-human interaction (AHI) problem, demonstrating that industry practices rely on human-centric mechanisms while academic research focus…

The paper introduces MolTrust, a production-deployed trust infrastructure built on W3C standards (VCs and DIDs) that provides a verifiable, multi-layered authorization framework for autonomous AI agen…

This paper conducts a literature review of non-academic publications to consolidate current knowledge, trends, and future challenges regarding the industrial integration of IoT devices within a Zero T…

The paper proposes the Redpanda Agentic Data Plane (ADP), an architecture that uses out-of-band metadata channels to deterministically enforce security policies and governance for autonomous AI agents…

The paper proposes a portable authorization standard for autonomous agents, addressing the structural gaps in existing identity models when agents operate across organizational boundaries.

This multivocal literature review analyzes the convergence of IoT and Zero Trust security, finding that academia focuses on IoT modifications while industry prioritizes practical integration within ex…

The paper introduces the Agent Name Service (ANS), a DNS-inspired trust layer implemented in Kubernetes, to provide secure discovery, identity, and governance for autonomous AI agents.

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

This paper introduces the Machine Identity Governance Taxonomy (MIGT), a comprehensive framework designed to govern the rapidly expanding and currently ungoverned machine identities used by AI systems…

The paper argues that Agentic AI fundamentally breaks the historical security tradeoff between deception fidelity and scale, necessitating a shift from authenticating actors to evaluating actions.

The paper proposes a comprehensive, dual-layer architectural framework for AI identification and traceability, ensuring continuous accountability and regulatory oversight throughout the entire lifecyc…

AgenTEE is a system that enables the secure, confidential execution of complex LLM agent pipelines directly on edge devices by using isolated confidential virtual machines.

The paper argues that agent security must be treated as a systems problem, requiring the enforcement of security invariants at the system level rather than solely relying on improving the underlying A…