Bank of America Corporation

Pushkar Taneja

Durga Prasad Kutthumolu

Shailendra Singh

Sakshi Bakshi

Suryanarayana Adivi

Subhasree Baskaran

Govinda Ramam Gonna Satya Sai

Systems, computer program products, and methods are described herein for AI-based adaptive security parameter calculation in license key generation with quantum-resistant protections. The present disclosure is configured to utilize advanced cryptographic techniques, including elliptic curve cryptography and SHA-3 hashing, to produce secure digital signatures for license keys. An AI-driven engine analyzes historical security data to adaptively adjust key generation parameters, ensuring enhanced protection against current and emerging threats. This adaptive approach allows for continuous learning and improvement of security measures, making the system robust against both classical and quantum cryptographic attacks. The disclosed methods integrate the generation, assessment, and validation of keys into a streamlined process that promotes high security while maintaining efficiency.

System and method for AI-based adaptive security parameter calculation in license key generation with quantum-resistant protections

Arrangements for an intelligent monitoring platform using a cybersecurity mesh and graph neural networks (GNNs) are provided. A platform may train multiple machine learning models (e.g., a GNN model, a cybersecurity engine, and a monitoring model). The platform may generate, using a GNN model, a suspicion score for a received event processing request. Based on determining the suspicion score satisfies a threshold, the platform may generate a threat score using a cybersecurity engine. The platform may generate an anomaly record for the event processing request based on the threat score and using a monitoring model. The platform may determine a preferred node of a cybersecurity mesh for routing the event processing request based on the anomaly record. The platform may determine a threat prevention response based on the preferred node. The platform may initiate one or more security actions based on the threat prevention response.

Intelligent monitoring platform using graph neural networks with a cybersecurity mesh and associated cybersecurity applications

Innovative distributed tracing system(s) and process(es) integrate automated code instrumentation, AI-driven quantum computing, particularly Photonic Quantum Computing, and real-time analysis algorithms. This system autonomously identifies and monitors network components such as gateways, service meshes, message queues, databases, and proxy servers, seamlessly capturing trace data across various communication protocols. It features dynamic scaling capabilities to handle fluctuating network loads and employs automatic tagging for precise identification of each network element and transaction. Utilizing advanced machine learning algorithms, the system efficiently analyzes trace data, identifying patterns, dependencies, and anomalies. It is equipped to intervene in operations by halting transactions under specific conditions. The incorporation of Photonic Quantum Computing allows for unparalleled anomaly detection and data analysis speed and accuracy. This system enhances trace data analysis, improves anomaly detection, and optimizes overall system performance, offering a robust, secure, and efficient approach for managing complex distributed systems.

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Hyderabad, India

Pushkar Taneja