Cloud-based insurance systems face cumulative challenges from cyber vulnerabilities, regulatory non-compliance, and active threat landscapes that cooperate data integrity and operational resilience. Established security and risk evaluation methods lack adaptive intelligence and automation to manage such increasing risks effectively. This research proposes an Adaptive Security Framework for AI-Driven Risk Assessment and Compliance Automation in Cloud Security and Vulnerability Administration within the Insurance Domain. The proposed method integrates TensorFlow Extended (TFX) for computerized ML orchestration, TensorFlow for deep learning-based risk prediction, and TF-Agents for adaptive reinforcement learning to progress real-time response and compliance adherence. This outline integrates PCA-fueled feature extraction, Z-score normalization, and reinforcement characteristic coverage to resource available, privacy-preserving emulators of data-driven persistence. Experimental calculation using the Cyber-attack insurance dataset Shows higher performance than standard models like Random Forest, XGBoost and CNN-LSTM with Metrics of (Accuracy 0.992; Precision 0.987; Recall 0.989, F1 Score 0.988; AUC-ROC 0.995) The proposed design ensures automated and real-time compliance management for better-insured cyber security infrastructures.
This study investigates an implemented smart grid communication stack based on the Open Smart Grid Protocol (OSGP), an up-to-date architecture used to explicitly address interoperability, cybersecurity, resilience and market integration issues in today’s power systems. The paper builds on the IEC 61850 standard, which is in widespread use for power substation automation, by defining an architecture that leverages IEC 61850 capabilities to cover distributed energy resources (DERs), electric vehicles (EVs) and microgrids. Their relevance has been demonstrated through various technologies, such as Time-Sensitive Networking (TSN) for low latency, AI-based intrusion detection in cybersecurity context, blockchain in green energy market participation context and edge computing with high resilience. The modular architecture is designed to simply grow and evolve along with the rapidly increasing complexity of smart grids. The report summarises a concept of operation and provides a technical description of an integrated prototype for interprotocol interoperability, enhanced cybersecurity and self-healing capabilities. The performance of the designed system is validated through test scenarios which include a cyberattacks simulation and protocol translation. At the end, we emphasized that this type of architecture can evolve smart grid communication systems to a high performance, security and scalability level.
The use of digital payments has made buying and selling easier, but it has raised the likelihood of fraud in transactions. This study reviews how effective AI-based transaction monitoring is at detecting and preventing fraud as it happens. The study used Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) which were assessed with a synthetic digital payment dataset. To evaluate the models, we checked their accuracy, precision, recall and F1-score. The model with the best accuracy was LSTM at 96.7%, followed by CNN with 95.3%, Random Forest with 92.1% and SVM with 89.4%. Moreover, when it came to detecting fraud, LSTM obtained the top score by recalling 97.5% of all instances. The report further demonstrates that AI-based systems can adapt and function better than traditional systems. Overall, the study demonstrates that AI boosts the speed, accuracy and reliability of transaction monitoring systems in today’s financial industry. This research suggests using AI to increase security in digital transactions and overcome challenges from cybercrime.
