Deep learning-enabled zero trust architecture for intelligent identity and access management in cloud ecosystems

The explosion of extended cloud ecosystems and multi-cloud deployments has drastically increased the complexities of digital identity management, making access control a growing challenge. Modern cyber threats are rendering a perimeter-based security model ineffective, which is why there is an urgent call for an evolution to Zero Trust Architecture (ZTA), whereby always-on verification of user identity and access request is the foundation of the framework on the premise that “never trust, always verify.” We also introduce the Integrated API for adaptive IAM, which will be used as part of our deep learning based Zero Trust architecture.

Using deep neural networks for continuous authentication, behavioral analysis, and anomaly detection. With adjustable access permissions and extensive knowledge of numerous contextual features like device health, user behavior, and access patterns, the system proactively adjusts fine-grained access policies in real time to minimize unauthorized access. This architecture leverages micro-segmentation, policy enforcement points along with continuous monitoring to minimize lateral movement and enhance containment for threats.

Additionally, the combination of deep learning models improves detection precision and provides opportunity for preventive threat mitigation via detection of unauthorized access patterns not previously seen in the DB as well as unknown insider threats. The performance of newly proposed model exhibited enhanced scalability, adjustability and response ability over traditional IAM systems.

It plays a vital role in the evolution of next-generation cloud security frameworks that ensure dynamic, adaptive and resilient identity and access management based on cutting-edge data-driven decision-making forged into structures infused with Zero Trust ethos.