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Intelligent Risk Assessment and Optimization Frameworks for Strengthening Organizational Network Security
Subject area: Science,Engineering and Technology · Area of research: Network Security Risk Assessment
DOI: https://doi.org/10.64388/IREV9I9-1715068
Abstract
The increasing complexity and frequency of cyber threats have made intelligent risk assessment and optimization frameworks essential components of organizational network security. This study explores the integration of artificial intelligence and machine learning to enhance predictive capabilities, automate decision processes, and improve the precision of risk management strategies. By employing AI-driven risk scoring models and optimization algorithms, organizations can identify, prioritize, and mitigate vulnerabilities more effectively, achieving a balance between proactive defense and resource efficiency. The proposed framework emphasizes continuous adaptive learning, allowing systems to evolve with changing threat environments while maintaining transparency through explainable AI. Optimization techniques facilitates dynamic allocation of resources, ensuring compliance and resilience across complex network infrastructures. The findings underscore the transformative potential of AI-based optimization and risk assessment solutions in establishing more robust and agile defenses against increasingly sophisticated cyber risks in contemporary digital ecosystems.
Keywords
Artificial intelligence, machine learning, risk assessment, network security, optimization, explainable AI, adaptive defense
How to cite this paper
@article{1715068,
author = {Dr. Deepak Tomar, Dr. Kismat Chhillar},
title = {Intelligent Risk Assessment and Optimization Frameworks for Strengthening Organizational Network Security},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {787-794},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/17150681.pdf},
abstract = {The increasing complexity and frequency of cyber threats have made intelligent risk assessment and optimization frameworks essential components of organizational network security. This study explores the integration of artificial intelligence and machine learning to enhance predictive capabilities, automate decision processes, and improve the precision of risk management strategies. By employing AI-driven risk scoring models and optimization algorithms, organizations can identify, prioritize, and mitigate vulnerabilities more effectively, achieving a balance between proactive defense and resource efficiency. The proposed framework emphasizes continuous adaptive learning, allowing systems to evolve with changing threat environments while maintaining transparency through explainable AI. Optimization techniques facilitates dynamic allocation of resources, ensuring compliance and resilience across complex network infrastructures. The findings underscore the transformative potential of AI-based optimization and risk assessment solutions in establishing more robust and agile defenses against increasingly sophisticated cyber risks in contemporary digital ecosystems.},
keywords = {Artificial intelligence, machine learning, risk assessment, network security, optimization, explainable AI, adaptive defense},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715068}
}