Advanced Predictive AI Frameworks for Secure Site Reliability Engineering in Enterprise Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I2P123Keywords:
Artificial Intelligence, Site Reliability Engineering, Predictive Analytics, Enterprise Systems, Cybersecurity, Machine Learning, Autonomous Remediation, Cloud Computing, Reliability Engineering, Intelligent Automation, AI-driven SRE, Predictive MaintenanceAbstract
The rapid digital transformation of enterprise systems has significantly increased the complexity of maintaining highly available, secure, and resilient infrastructures. Modern enterprises rely on distributed cloud-native architectures, microservices, containerized applications, and hybrid multi-cloud ecosystems that demand advanced Site Reliability Engineering (SRE) practices. Traditional SRE approaches, while effective in static environments, struggle to address the growing challenges of predictive fault management, cybersecurity threats, automated incident response, and dynamic workload optimization. In response to these challenges, Artificial Intelligence (AI) and Machine Learning (ML) technologies have emerged as transformative tools capable of enhancing predictive reliability, operational intelligence, and secure automation in enterprise environments.This research article investigates advanced predictive AI frameworks designed for secure Site Reliability Engineering in enterprise systems. The study explores the integration of AI-driven anomaly detection, predictive analytics, reinforcement learning, deep learning, and autonomous remediation mechanisms within modern SRE pipelines. The article critically examines the limitations of traditional reliability engineering methodologies and evaluates how predictive AI enhances system observability, threat detection, fault prediction, and infrastructure resilience. Furthermore, the study presents a comparative analysis of AI-powered SRE frameworks, emphasizing their capabilities in proactive incident management, security intelligence, and adaptive scalability.The research methodology adopts a qualitative and analytical framework supported by literature review, comparative architectural evaluation, and case-based analysis from enterprise cloud platforms. Results indicate that predictive AI frameworks significantly improve system uptime, reduce Mean Time to Detect (MTTD) and Mean Time to Recover (MTTR), enhance cybersecurity resilience, and optimize operational efficiency. However, challenges such as model explainability, data privacy, computational overhead, algorithmic bias, and integration complexity remain critical concerns.The study concludes that AI-enabled secure SRE frameworks represent the future of intelligent enterprise infrastructure management. By integrating predictive intelligence with security-aware automation, organizations can achieve self-healing systems capable of maintaining operational continuity under dynamic and hostile digital environments.
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