The paper concludes that the convergence of trustworthy AI, robust multimedia analysis, and privacy-preserving architectures is essential for responsible AI adoption in regulated domains, and highlights open challenges in cross-document synthesis, multimodal robustness, and scalability for future research.
This study presents a detailed, actionable approach to constructing secure, privacy-focused RAG systems and culminates in the Integrated Privacy-Preserving RAG Framework (IPRAG), a five-tier architecture supported by a three-phase deployment protocol.
Firoz Mohammed Ozman· International Journal of Fro...· 0 citations
Large language models (LLMs) are increasingly embedded as core components of data-centric systems, supporting analytical decision making, and automated reasoning over large-scale, heterogeneous datasets. Yet their deployment in open-world environments raises fundamental challenges to security and trustworthiness: LLMs...
Lu Lin, Jinghui Chen, Ting Wang et al.· Proceedings of the 32nd ACM...· 0 citations
CTRAG is presented, a novel Retrieval-Augmented Generation pipeline designed for automated compliance checking that employs advanced strategies, including adaptive chunking, dynamic retrieval configurations, and in-context learning, to improve the precision and relevance of compliance assessments.
Muhammad Roman, Karen Rafferty, Barry Devereux· 0 citations
This research investigates the security of large language models (LLMs) with the aim of identifying key security and safety threats, control measures, and governance considerations that are relevant to their trustworthy adoption in the Extended Reality (XR) domain. To achieve this goal, the research combines an extensi...
The Open Radio Access Network (O-RAN) replaces vendor-locked RANs with a modular and interoperable architecture that fosters competition and accelerates innovation. With this openness comes increased complexity and a larger attack surface, making security a critical concern. Today, assessing O-RAN security requires man...
Corban Villa, Michele Guerra, Syed Khandker et al.· 0 citations
Cloud misconfiguration remains a leading cause of security incidents, yet whether LLMs and SLMs can generate security-compliant Infrastructure-as-Code is an open question. We benchmark seven models, three closed LLMs (Claude Opus 4, GPT-5.4, Gemini 2.5 Pro) and four open SLMs (Qwen2.5-Coder-14B, WizardCoder-33B, CodeLl...
Francis Luis Santos Vargas, R. Mansilha, Diego Kreutz· 0 citations
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