Autonomous AI-Based Cyber Defense and Secure API Integration for Mission-Critical Cloud Enterprise Applications
DOI:
https://doi.org/10.15662/IJARCST.2025.0806034Keywords:
Autonomous Artificial Intelligence, Cyber Defense, Secure API Integration, Mission-Critical Applications, Cloud Security, Machine Learning, Zero Trust, API Security, Threat Detection, Automated Incident ResponseAbstract
Mission-critical enterprise applications increasingly depend on cloud computing, microservices, distributed architectures, and application programming interfaces (APIs) to deliver scalable and continuously available digital services. However, this transformation has created complex attack surfaces involving cloud workloads, identities, APIs, containers, databases, third-party services, and inter-service communication. Conventional cybersecurity mechanisms based primarily on predefined rules and manual security operations are often insufficient for detecting sophisticated, rapidly changing, and coordinated attacks. This paper proposes an autonomous artificial intelligence (AI)-based cyber defense architecture for securing mission-critical cloud enterprise applications and their API ecosystems. The proposed architecture integrates machine learning, behavioral analytics, zero-trust security, API gateways, identity and access management, continuous monitoring, threat intelligence, automated incident response, and secure service-to-service communication. AI models analyze authentication events, API traffic, application behavior, network telemetry, cloud audit logs, and resource consumption to detect anomalies and generate contextual risk scores. Based on these scores, autonomous response mechanisms can dynamically enforce controls such as authentication escalation, rate limiting, token revocation, workload isolation, and API blocking. The research adopts a design-science and experimental methodology involving threat modeling, architecture development, simulation, and performance evaluation. The proposed approach aims to improve detection accuracy, reduce response time, strengthen API security, minimize operational workload, and provide adaptive protection for highly available enterprise environments.
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