Large Language Models (LLMs) offer significant potential for clinical decision support, but their deployment is constrained by hallucination, privacy risks, and limited interpretability. Retrieval-Augmented Generation (RAG) improves reliability by grounding outputs in patient-specific electronic health records (EHRs); however, it introduces new challenges related to sensitive data exposure. This paper proposes a governanceaware, privacy-preserving RAG framework for clinical triage decision support using the MIMIC-IV-Ext dataset. The framework integrates retrieval-based grounding, differential privacy, explainability, and an agentic orchestration layer within a unified architecture. An $\epsilon$-ledger is introduced to track cumulative privacy expenditure, and a Privacy-Utility-Explainability (PUE) framework is used to systematically evaluate trade-offs. Experimental results demonstrate that the proposed approach improves classification performance (e.g., achieving up to 82% accuracy and 88% AUROC) and reduces under-triage risk, while privacy mechanisms introduce only modest performance degradation. Retrieval-based attribution further enhances interpretability by linking predictions to clinically relevant cases. These results highlight the effectiveness of combining retrieval, privacy, and explainability for developing trustworthy and deployable healthcare AI systems.
A. K. I. Riad, Md Abdul Barek, Hossain Shahriar et al.· International Conference on...· 0 citations
Large Language Models (LLMs) are now deployed at an unprecedented scale across many critical sectors, rapidly transitioning from experimental AI tools to embedded components of production software systems. This accelerated adoption, often enabled by low-code integrations, has lowered technical barriers while simultaneously expanding the attack surface of modern applications, particularly when deployments occur without sufficient domain-specific security expertise. In many cases, security maturity has not progressed at the same pace as capability expansion, creating systemic exposure across confidentiality, integrity, and availability dimensions. To provide structured clarity amid this rapid growth, this paper presents a comparative and standards-aligned analysis of LLM security risks and defense mechanisms grounded in the OWASP GenAI Top-10 (2025). We systematically examine each vulnerability class, map representative attack patterns to primary mitigation strategies, evaluate their security property impact, and analyze practical limitations and implementation trade-offs. In addition, we introduce a severity-based assessment to prioritize risks according to operational and systemic impact, offering a quantitative perspective on defensive readiness. Our findings indicate that current mitigation strategies are predominantly reactive, concentrated at inference time, and unevenly distributed across the LLM lifecycle. Controls addressing training pipelines, supplychain dependencies, and autonomous system behaviors remain comparatively less mature and less standardized. By integrating vulnerability classification, defense mapping, severity prioritization, and trade-off analysis within a unified framework, this study provides actionable guidance for strengthening secure, resilient, and standards-driven LLM deployment in high-stakes environments.
Md Abdul Barek, Md Bajlur Rashid, A. K. I. Riad et al.· Annual International Compute...· 0 citations