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Influence of the Manufacturing Process and Dynamic Testing on the Residual Stress State of Additively Manufactured Ti-6Al-4V Lattice Structure Specimens
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Evaluation of AI Attack Mitigation : from Citrix Bleed to Self-evolving Malware; Modernizing Aerospace Cyber Defense with AI
No full textBeyond Static Security : A Context-Aware and Real-Time Dynamic Zero Trust Architecture for IIoT Access Control
No full textIn industrial environments, cyber threats are escalating at an unprecedented rate, yet many existing security solutions fail to account for both contextual factors and the criticality of different network segments. This challenge is especially pronounced in diverse, large-scale, and highly dynamic Industrial Internet of Things (IIoT) environments. This paper presents a dynamic Zero Trust Access Control (ZTA) model that adapts to real-time device status, network conditions, and user behavior to enforce context-aware, security-driven access decisions. At its core, our framework combines mathematical threat assessment with fuzzy logic-based state management (FSM) to continuously adjust trust levels and access permissions. We validated our approach, through a proof-of-concept using a cluster of virtual machines (VMs) to simulate a controlled environment. This setup demonstrates the ZTA model’s effectiveness in small-scale networks and provides a foundation for testing various access scenarios and evaluating security policies
Experimental investigation of nanosecond pulsed laser ablation of polycrystalline diamond composite
Full text linkGenerative AI as a co-pilot in annual audits
No full textThis thesis examines generative artificial intelligence as a co-pilot in annual audits in the context of digital transformation. It focuses on how planning, execution, and reporting change when generative AI models are used in individual audits and what consequences this has for the quality, reliability, and ethics of the audit. At the same time, it highlights how automation, multimodality, and large context windows are reshaping traditional audit procedures and the division of labor between humans and systems. The relevance of the study lies in understanding the changing dynamics. New audit strategies are needed that leverage efficiency gains while ensuring standardization, documentation, and verifiability. In the spirit of “shared responsibility,” generative AI provides breadth, speed, and consistency, while the auditor contributes judgment, professional skepticism, and contextual knowledge. This brings role models (e.g., prompt and data competence) and governance issues to the fore. Based on a case study, the paper shows that generative AI can substantially accelerate analytical processes, document review, and report drafting and serve as an early warning system. At the same time, the depth of interpretation, explainability, and data protection limit the immediate substitution of human judgment. The contribution consists of a sober assessment of specific areas of application and guidelines for responsible use: Generative AI as a tool, not as a decision-maker. Results must be verified, documented, and justified in the corporate context. This creates a viable path that increases audit quality and cost-effectiveness while maintaining professional standards
