Risk Aware Cybersecurity Governance Model with Real Time Threat Intelligence Integration and Predictive Anomaly Detection for Enterprise Network Infrastructures

Firman Pratama; Fandan Dwi Nugroho Wicaksono

doi:10.66472/cybernet.v1i1.10

Authors

Firman Pratama Universitas Pamulang
Fandan Dwi Nugroho Wicaksono Perbanas Institute

DOI:

https://doi.org/10.66472/cybernet.v1i1.10

Keywords:

Cybersecurity Governance, Machine Learning, Predictive Anomaly, Risk Exposure, Threat Intelligence

Abstract

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

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