Advancing Database Security with AI: Leveraging Machine Learning for Anomaly Detection and Automated Threat Mitigation

Abstract

AI?and autonomous systems are having a profound effect on a number of sectors, and software engineering is no different. The aim of this paper?is to gather available research results on the potential advantages of AI with respect to autonomous agents to enhance software engineering processes, especially regarding intelligent code generation and system maintenance. This paper?presents a framework for autonomous code generation, system monitoring, and system maintenance using machine learning and natural language processing, decision making models to write code, to monitor the system for abnormal or anomalous patterns, and to work without human assistance. The framework applicability is also evidenced through a set of case studies and results showing that the productivity is improved, human error is decreased?and the time of elaboration of software products reduces considerably. Furthermore, the paper highlights the potential of AI-powered?agents in automating repetitive tasks and ensuring code quality, timely conductance and reliability of software. A great panoramic view of challenges?including model interpretability, ethics issues and robust validation needs is also given. This work contributes to the ongoing discussion regarding the role of AI in software engineering by outlining a scalable, pragmatic, and self-sufficient method to create and sustain code, and by making AI agents?with this capability the primary agent of future software paradigms.

Introduction

The rise of digital technologies and the exponential growth of data have made database security critical, as sensitive information—including company IP, finances, and personal data—is increasingly targeted by cyberattacks. Traditional security measures such as access control, encryption, firewalls, and antivirus software are insufficient against evolving threats, zero-day vulnerabilities, insider attacks, and advanced social engineering.

AI and Machine Learning (ML) offer a promising solution by enabling proactive, adaptive, and real-time database protection. ML models, including supervised, unsupervised, and deep learning approaches, can detect anomalies in database behavior, such as unauthorized access, SQL injection, privilege escalation, and data leakage. These models learn from historical transactions to identify both known and novel threats with high accuracy, low false positives, and continuous real-time updating.

Beyond detection, AI-driven systems can automatically respond to threats—blocking users, terminating malicious sessions, and alerting administrators—reducing response time and human error. However, challenges remain, including the need for high-quality labeled data, managing false positives, integration with legacy systems, system performance overhead, and privacy/ethical concerns.

Current research focuses on enhancing anomaly detection with AI, including deep learning and explainable AI (XAI), as well as reinforcement learning for self-adaptive security systems. The methodology for AI integration includes data collection and preprocessing, feature extraction, model training, anomaly detection, and automated response, forming a dynamic and intelligent approach to database security.

Conclusion

In conclusion, comparative analysis between different techniques for detection of anomaly and threat prevention had showed that Autoencoder?has the edge over K-means Clustering and traditional Rule-based Methods. Besides, the Autoencoder model consistently outperforms the other compared model in accuracy, precision, recall and F1-score, which demonstrates its potential for effectively recognizing the?new anomalies. In addition, AI-driven response is far more rapid than human intervention and automatically ramp up real-time defenses against?threats. Results show that the utilization of AI model based solutions?is important for database security and such solutions can gain more precise detection with fast responding threat to better guard against new threats.

References

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Copyright

Copyright © 2025 Rajendra Varma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.