This paper presents a novel methodology to enhance Autonomous Cybersecurity Knowledge Graphs (ACKGs) by incorporating distributed invariants, ensuring robust consistency in data integrity, access control, and threat detection. The proposed framework integrates Graph Neural Networks (GNNs) and Large Language Models (LLMs), facilitating real-time validation, automated threat mitigation, and continuous system updates as evolving threats are detected. By embedding these invariants into the cybersecurity architecture, the approach offers a scalable, dynamic, and self-sustaining solution, significantly improving the resilience, adaptability, and operational efficiency of cybersecurity systems in complex, large-scale environments.