This paper exploits the rise of artificial intelligence (AI) and deep learning (DL) to improve the use of digital forensic evidence analysis, specifically criminal identification from facial images despite disguise and makeup. Our approach leverages the VGG16 architecture for face recognition and identification, coupled with the LIME framework (Local Interpretable Model-Agnostic Explanations) to explain model recognition. This combination enables interpretation and verification of results with enhanced trust and confidence in forensic analysis. We follow a "watch and iterate" procedure, utilizing the insights generated from LIME to curate the training dataset, improving the model’s performance iteratively. The efficacy of this procedure is reflected in the remarkable outcomes: our model has an accuracy of 98.10%, precision of 98.16%, recall of 98.10%, F1-score of 98.11%, AUC of 100%. This development in forensic technology has great potential to enhance the precision and speed of criminal identification, thus leading to safer and fairer societies.

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.