Green Cybersecurity: Leveraging AI, ML, and LLMs to Optimize Energy, Threat Detection, and Sustainability Frameworks

The rapid advancement of digital technologies has significantly increased energy consumption and carbon emissions, raising critical concerns about environmental sustainability. Green cybersecurity, which integrates environmental sustainability goals into security processes, is becoming increasingly vital as AI systems contribute to growing energy use and carbon dioxide emissions. This paper introduces the concept of green cybersecurity by examining how the integration of Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) can simultaneously enhance cybersecurity practices while reducing their environmental impact. Through a systematic review of peer-reviewed literature, technical reports, and case studies from 2019 to 2025, we explore the potential of AI and ML to optimize energy usage, minimize electronic waste, and support the development of environmentally sustainable cybersecurity frameworks. Our findings indicate that AI and ML can drive advancements in resource management, predictive threat detection, automated response, and real-time monitoring. To assess these opportunities, we draw upon multiple theoretical frameworks, including the Systems Theory Model, Cybersecurity Maturity Model (CMM), and the UN Sustainable Development Goals (SDG) Framework, which collectively emphasize the socio-technical and ethical dimensions of AI-powered cybersecurity. We also address key challenges such as high computational demands, integration complexity, lifecycle environmental impacts, and ethical concerns related to data privacy and algorithmic transparency. This study underscores the need for interdisciplinary collaboration to develop security solutions that are both technologically robust and environmentally responsible.