AI-enhanced predictive analytics for identifying and mitigating critical cybersecurity vulnerabilities

Akinyemi, Adeyemi Mobolaji and Sims, Sherry (2025) AI-enhanced predictive analytics for identifying and mitigating critical cybersecurity vulnerabilities. World Journal of Advanced Research and Reviews, 26 (2). pp. 1585-1606. ISSN 2581-9615

Introduction: Predictive analytics using artificial intelligence tools has become an important part in making the process of cybersecurity vulnerability manageable and more effective. The machine learning algorithms, which have been developed for auditing the historical breaches, show 94% accuracy to detect the new threats that are yet unobserved while, the deep learning algorithms in terms of neural networks have 97% precision to discover out the anomalous traffic in the network. Combining predictive functionalities with automatic reactions lowers the mean incident response time to half an hour from three hundred and twenty-seven for an approximate cut of 92.5%. Methodology: A thorough assessment of various AI based predictive analytics were made on 131 enterprise networks and over 50,000 end points, following a well laid out evaluation criteria. The methodology focused on supervised and unsupervised learning approaches and is based on the analysis of 2.5 petabytes of the historical security data by applying gradient boosting of 96.3% precision, random forests of 94.8% recall, and deep neural networks of 95.6% F1-score. Vulnerability assessment metrics focused on how accurately the indicators were diagnosed, number and percentage of false alarms, and the times when predictions were made relative to actual downtimes and how effectively they were avoided. Benchmarking performance with reference datasets that had recorded 1.2 million security incidents, and attack simulations were also employed into the test. Outcome: AI-driven predictive analytics for IT security produced measurable benefits: successful breach attempts declined to 3.6%, mean time to detect (MTTD) reduced to 9.9 hours from the previous 96 hours (-89.7%), and finally, it also reduced the mean time to respond (MTTR) to 4.9 hours from 72 (-93.2%). The effectiveness of the system is measured at 98.3%, mean miss percentage is extremely low at 1.7% while the false positive ratio is at 0.7%. The performance of predictive models brought about a lead time of 15.6 days before suggesting an exploitation, thereby allowing preventive measures to be taken. Based on the cost analysis, the savings on incident expenses were estimated to have been slashed by a proportion of 76% in the same year and the organizational in the set project earned 4.3 times its cost within one year. Discourse: This justifies the use of ensemble learning techniques of AI that incorporates several models to improve results of forecasts than using a single model. By combining deep learning with statistical models, which were traditional in this case, it was possible to achieve better results, namely, the increase in vulnerability detection made up 27 percent than in the case of the use of only one algorithm. The companies utilizing AI-Predictive analysis in their organizations have attributed a higher overall efficiency of their security teams at 82%, while the impact on critical events was a reduction by 91%. Conclusion: The efficiency of both AI and big data in relation to KPIs related to cybersecurity vulnerability management cannot be overstated and has influenced positively throughout. There has been a documented achievement of 96.4% of successful breaches which has been accompanied by a similar dramatic reduction of the time it took to detect and respond to these breaches. Thus, the proven ability of the system to forecast the risks 15.6 days before they may be exploited with a minimal number of false positives at 0.7% confirms their efficiency within the context of the contemporary cybersecurity paradigms.