AI-Native Cybersecurity: Developing Adaptive Security Architectures for Autonomous Digital Environments

Rob Dunbar

AI-Native Cybersecurity: Developing Adaptive Security Architectures for Autonomous Digital Environments

Author(s)	Rob Dunbar
Country	United States
Abstract	The rapid development of artificial intelligence, autonomous systems, cloud computing, edge platforms, Internet of Things devices, and software-defined infrastructure is transforming the cybersecurity landscape. Traditional security architectures, which often depend on static rules, predefined signatures, and human-driven incident response, are increasingly challenged by dynamic and highly distributed digital environments. AI-native cybersecurity represents a paradigm in which artificial intelligence is embedded directly into security architecture to enable continuous monitoring, adaptive risk assessment, autonomous detection, and context-aware response. This paper examines the foundations, architecture, applications, challenges, and future directions of AI-native cybersecurity for autonomous digital environments. A qualitative and conceptual methodology is adopted to analyse AI-enabled threat detection, behavioural analytics, automated response, zero-trust architectures, federated learning, adversarial machine learning, explainable AI, and autonomous security operations. An integrated adaptive security framework is proposed consisting of continuous sensing, contextual intelligence, risk scoring, automated decision-making, response orchestration, and continuous learning. The study identifies significant benefits, including faster threat detection, reduced response time, improved scalability, and enhanced capability against previously unknown threats. However, AI-native cybersecurity also introduces challenges related to adversarial attacks, model manipulation, data poisoning, false positives, explainability, privacy, computational requirements, and excessive automation. The paper concludes that AI-native security should not be viewed as complete replacement of human cybersecurity professionals. Instead, future security architectures should combine autonomous intelligence with human oversight, robust governance, secure AI development, continuous validation, and resilient system design.
Keywords	AI-Native Cybersecurity, Artificial Intelligence, Adaptive Security, Autonomous Systems, Zero Trust, Threat Detection, Machine Learning, Cyber Defence, Adversarial AI, Digital Security.
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-03-10

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Native Cybersecurity: Developing Adaptive Security Architectures for Autonomous Digital Environments

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