Adaptive Cybersecurity Architecture for Digital Product Ecosystems Using Agentic AI
It addresses cybersecurity challenges for complex digital infrastructure like cloud services and edge devices, offering a scalable blueprint that is incremental in applying agentic AI to known bottlenecks.
This paper tackles the problem of static cybersecurity models struggling with scalability and real-time detection in digital product ecosystems by introducing an adaptive architecture using agentic AI, resulting in demonstrated improvements in adaptability, reduced response latency, and enhanced detection accuracy through cloud simulations.
Traditional static cybersecurity models often struggle with scalability, real-time detection, and contextual responsiveness in the current digital product ecosystems which include cloud services, application programming interfaces (APIs), mobile platforms, and edge devices. This study introduces autonomous goal driven agents capable of dynamic learning and context-aware decision making as part of an adaptive cybersecurity architecture driven by agentic artificial intelligence (AI). To facilitate autonomous threat mitigation, proactive policy enforcement, and real-time anomaly detection, this framework integrates agentic AI across the key ecosystem layers. Behavioral baselining, decentralized risk scoring, and federated threat intelligence sharing are important features. The capacity of the system to identify zero-day attacks and dynamically modify access policies was demonstrated through native cloud simulations. The evaluation results show increased adaptability, decreased response latency, and improved detection accuracy. The architecture provides an intelligent and scalable blueprint for safeguarding complex digital infrastructure and is compatible with zero-trust models, thereby supporting the adherence to international cybersecurity regulations.