## 1 Introduction
It encompasses the need for robust authorisation and authentication mechanisms, data integrity and confidentiality, and protection against both physical and virtual threats, which require solutions that are lightweight and not computationally hungry. The distributed nature of IoT networks, combined with the often resource-constrained nature of IoT devices, necessitates novel approaches to security that can operate efficiently in this complex environment. As the IoT continues to evolve and expand, addressing these challenges becomes increasingly critical. The analysis of existing IoT security approaches, presented in Section 2, reveals several critical gaps in current IoT solutions:  
* Lack of integrated security orchestration: While individual solutions exist for specific security aspects, there is no comprehensive framework that coordinates multiple security mechanisms in real time. * Resource constraints management: Current solutions often impose unsustainable computational and energy demands on resource-limited IoT devices. * Adaptability limitations: Existing frameworks struggle to dynamically adjust security measures in response to emerging threats and changing device contexts. * Quantum vulnerability: Many current IoT security solutions rely on cryptographic approaches that will become vulnerable to quantum computing attacks. Privacy-security balance: There is insufficient integration between privacy-preservation mechanisms and security measures in existing solutions. To address these gaps, this paper proposes the Integrated Adaptive Security Framework for IoT (IASF-IoT), a comprehensive framework designed to tackle the multifaceted security challenges of the IoT. At the core of IASF-IoT is an AI-driven security orchestration engine that coordinates all other security components of the framework. This AI engine employs a reinforcement learning model to continuously optimise security measures across the IoT network, learning from past security incidents and adapting its strategies in real time. By integrating advanced technologies such as artificial intelligence, blockchain, and adaptive security measures, IASF-IoT aims to provide a robust, scalable, and flexible solution for securing the Internet of Things. The framework addresses various aspects of IoT security, including identity and access management, quantum-resistant cryptography, edge-centric security, and privacy-preserving data processing. Additionally, IASF-IoT employs Digital Twins, leveraging the concept of symmetry to create virtual replicas of physical IoT devices for predictive threat analysis and proactive security measures. Adaptive content authentication further enhances the security of the proposed framework against emerging threats. To evaluate the effectiveness of the proposed framework, the paper presents a performance model that assesses the framework's impact on the IoT devices' performance, network latency, and energy consumption. This model ensures that the security enhancements do not compromise the operational efficiency of IoT devices. This paper presents a theoretical framework and architectural design for integrated IoT security, validated through comprehensive simulation. While the full implementation of all components (particularly quantum-resistant cryptography and blockchain integration) requires significant resources and mature toolsets that are not yet widely available, this work establishes the conceptual foundation and demonstrates feasibility through careful modeling. The contribution lies in the novel integration architecture and the demonstration that such integration can theoretically maintain low resource overhead while providing comprehensive security coverage. To this end, this paper makes the following key contributions:  
1. The development of an AI-driven security orchestration mechanism that coordinates multiple adaptive components through reinforcement learning, enabling the continuous optimization of security responses in dynamic IoT environments. 2.