Memory-Augmented Retrieval for Personalized Cybersecurity Threat Intelligence Analysis
Keywords:
memory-augmented retrieval; personalized threat intelligence; cybersecurity analytics; retrieval-augmented generation; fairness; governance; infrastructureAbstract
Cybersecurity threat intelligence analysis increasingly suffers from information overload, fragmented evidence sources, and retrieval interfaces that are insensitive to the analyst’s role, sector, and investigative history. This article presents a systems-level examination of memory-augmented retrieval for personalized cybersecurity threat intelligence analysis. The proposed perspective integrates persistent user memory, organizational knowledge models, and retrieval-augmented generation over heterogeneous security corpora. Rather than evaluating a single artifact, the article analyzes the architectural foundations, memory representation choices, retrieval coordination mechanisms, infrastructure deployment conditions, and governance constraints that determine whether personalization improves security decision-making. The discussion emphasizes structural trade-offs between personalization fidelity and institutional consistency, between adaptive memory and adversarial manipulation, and between retrieval efficiency and privacy compliance. It argues that memory-augmented retrieval can reduce analyst query burden and improve contextual relevance when memory is scoped, auditable, and aligned with operational workflows. The article further examines how threat taxonomies, source trust, vulnerability disclosure norms, and fairness considerations shape system design. The analysis draws on security engineering, information retrieval, machine learning, and socio-technical infrastructure studies to provide a comprehensive research agenda. It concludes with implications for sustainable deployment, robustness, and regulatory oversight in security operations centers and federated intelligence-sharing environments.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
