Cybersecurity Risk Assessment in Enterprises Using Artificial Intelligence Models

• Louis Martin

  Author

• Fatima Al Mansoori

  Author

The accelerating digital transformation of enterprises has expanded the attack surface for cyber threats, making traditional risk assessment methods increasingly inadequate due to their reliance on static rules and historical signatures. This paper explores the integration of artificial intelligence models into enterprise cybersecurity risk assessment frameworks to enable dynamic, predictive, and adaptive evaluation of vulnerabilities, threats, and potential impacts. Unlike conventional approaches that often fail to detect novel or zero-day attacks, AI-driven systems leverage machine learning, deep learning, and natural language processing to analyze vast amounts of network telemetry, user behavior logs, and threat intelligence in real time. The study proposes a hybrid model that combines unsupervised learning for anomaly detection with supervised classification for risk scoring, validated using a dataset of simulated enterprise network traffic. Results indicate that AI models significantly reduce false positive rates, improve detection latency, and provide probabilistic risk estimates that align closely with actual breach outcomes. Furthermore, the paper addresses challenges such as model interpretability, adversarial attacks on AI, and computational overhead, offering mitigation strategies like explainable AI layers and federated learning. The findings underscore that while AI cannot eliminate all risks, it substantially enhances an enterprise’s ability to prioritize remediation efforts, comply with regulatory standards, and maintain operational resilience. Practical implementation guidelines are discussed for small to large enterprises, including data preprocessing requirements, model retraining cycles, and integration with existing security orchestration platforms.

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