A Distributed Multi-Agent Artificial Intelligence Framework for Proactive Cybersecurity Risk Detection and Enterprise Business Analytics
DOI:
https://doi.org/10.70715/jitcai.2026.v3.i4.093Keywords:
: Multi-Agent Systems, Cybersecurity Risk Management, Enterprise Business Analytics, Proactive Threat Detection, Artificial Intelligence GovernanceAbstract
The digital transformation of enterprises has precipitated an exponential expansion of attack surfaces, exposing organizations to sophisticated cyber threats that increasingly elude traditional security controls. Concurrently, the imperative for data-driven business intelligence demands that cybersecurity investments generate measurable operational and strategic value beyond loss prevention. This article proposes a Distributed Multi-Agent Artificial Intelligence Framework (DMAI-Cyber) that integrates proactive cybersecurity risk detection with enterprise business analytics through a collaborative architecture of specialized AI agents. The framework operationalizes five core agent types Sentinel Agents for continuous monitoring and threat detection, Analyst Agents for contextual risk assessment, Prognostic Agents for predictive analytics, Business Intelligence Agents for translating security insights into operational metrics, and Orchestrator Agents for adaptive governance and response coordination. Drawing upon established theoretical foundations in multi-agent systems, cybersecurity frameworks (NIST CSF, MITRE ATT&CK), and business intelligence models, the proposed framework addresses critical gaps in existing security operations center (SOC) paradigms, including the inability to contextualize technical alerts within business risk priorities, prohibitive manual investigation overhead, and the disconnect between security operations and strategic decision-making. The framework's architecture supports real-time behavioral analytics, semantic threat detection, automated incident investigation, and the bidirectional integration of security intelligence with enterprise performance management systems. Implementation considerations encompass scalability, latency constraints, privacy-preserving mechanisms, and integration with legacy security infrastructure. The article contributes a novel synthesis of distributed AI governance, proactive cyber defense, and business-aligned risk management, offering a blueprint for organizations seeking to transform cybersecurity from a cost center into a strategic business enabler.
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Data Availability Statement
The data supporting the findings of this study are not publicly available because this research presents a conceptual framework and does not report results derived from proprietary or participant-generated datasets. Publicly available benchmark datasets, including CIC-IDS2017, CSE-CIC-IDS2018, UNSW-NB15, TON_IoT, and Bot-IoT, are identified for future empirical validation. No new datasets were generated or analyzed during the current study.
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Copyright (c) 2026 ali hassan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
