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 proposes Operational AI Deployment Assurance (OADA), a governance framework that translates complex AI evaluation metrics and operational uncertainties into actionable, deployment-oriented a…

The paper introduces SAMD, an automated tool that uses STPA-Sec to identify potential false data injection attack scenarios in AI/ML-enabled medical devices during the design phase.

The paper proposes a comprehensive, phased hybrid migration framework to transition vulnerable IoT-based healthcare systems to quantum-safe cryptography.

The paper proposes Sovereign 2.0, a control-plane-centric model redefining cloud sovereignty as enforceable governance authority and operational control, rather than mere data location.

The paper proposes Federated Computing as Code (FCaC), a declarative architecture that enforces sovereignty-critical constraints in federated systems by compiling authority into cryptographically veri…

This paper provides the first systematic threat analysis of State-Space Models (SSMs) in safety-critical applications, introducing novel attack classes and formal metrics to quantify their security an…

This survey provides a comprehensive, systematic roadmap for achieving cyber-physical system (CPS) resilience by integrating five interconnected themes: system-wide properties, handling data scarcity…

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 proposes a novel, on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for IoMT environments, achieving high classification accuracy while providing transpar…

The paper introduces the Canonical Security Telemetry Substrate (CSTS), a standardized, AI-ready foundation designed to harmonize fragmented and heterogeneous cybersecurity data into a unified model f…

The paper proposes the Energetic Paradigm, a model-agnostic architectural framework that allows states to maintain decision sovereignty and control over military AI systems, even when using proprietar…

This paper introduces the FHIR Resource Access Graph (FRAG) to formally model and detect concurrency-related race conditions—such as Simultaneous Write Conflict and TOCTOU Authorization Violation—in h…

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 introduces an AI red teaming agent that drastically reduces the time and effort required for security testing by allowing operators to define complex attack goals using natural language, com…

The paper introduces SURGENT, a multi-agent assistance system designed for the entire perioperative workflow, which outperforms standard LLMs by providing context-aware, traceable, and privacy-preserv…

The paper proposes CyberAId, a hybrid multi-agent system designed to enhance cybersecurity for financial institutions by integrating specialized LLM subagents with existing SIEM/XDR telemetry, address…

This paper analyzes the intersection of clinical handling practices and cyberbiosecurity risks associated with both regulated and unregulated (DIY) artificial pancreas systems, highlighting the legal…

HySecTwin introduces a knowledge-driven digital twin framework that uses semantic modeling and hybrid reasoning to provide explainable, context-aware, and high-speed threat detection for complex Cyber…

The paper introduces a novel six-agent AI architecture for cybersecurity risk assessment, demonstrating high accuracy and speed compared to human experts, though its performance is ultimately limited…