Peer-reviewed research, applied frameworks, and thought leadership across AI, cybersecurity, governance, and enterprise technology.
10 results
ENT_PLATFORM
Journal2025
Designing scalable enterprise data platforms to ensure security and regulatory compliance in payments and financial systems
In my research paper, I evaluate the structural frameworks needed to process massive financial transaction volumes at scale. The study details the strategic convergence of cloud-native microservices, containerization, and robust encryption protocols to optimize system reliability. Additionally, it highlights how integrating artificial intelligence transforms platform observability—driving real-time anomaly detection and predictive scaling to confidently navigate stringent global regulatory compliance frameworks. View Full Journal Article on ResearchGate | Read Online via WJAETS Journal
World Journal of Advanced Engineering Technology and Sciences (WJAETS)Read
CYBERSEC
Journal2025
Securing payment systems: The role of encryption, tokenization, and real-time fraud detection
In my peer-reviewed paper, I dissect the architectural pillars underpinning contemporary digital transaction safety. The study outlines a multi-layered security blueprint that merges traditional encryption and tokenization with zero-trust topologies and cloud-native security frameworks. Crucially, it highlights how the integration of machine learning and natural language processing (NLP) transforms transaction monitoring from reactive flagging into predictive, real-time threat neutralization across global digital markets. Download Full Paper via ResearchGate | View Official Index on DOI/GJETA
Global Journal of Engineering and Technology Advances (GJETA)Read
APPLIED_AI
Journal2025
Enhancing Payment Ecosystems with AI/ML: Real-Time Analytics for Fraud Prevention and User Insights
In my peer-reviewed paper, I dissect the duality of modern financial platforms: maximizing security without breaking the user experience. The study analyzes the deployment of deep neural networks and ensemble classification systems across containerized microservices architectures. The resulting model delivers a remarkable 99.2% fraud detection accuracy rate and an immediate 60% reduction in false positives, while simultaneously leveraging contextual behavioral analytics to drive a 41% increase in transaction completion. Download via ResearchGate | View Official Article Index on WJARR Journal
World Journal of Advanced Research and Reviews (WJARR)Read
TRUSTED_AI
Article2025
Security Is No Longer Enough: Why Enterprises Must Build Trust
Argues that security compliance alone is insufficient for modern enterprises — organizations must build systemic trust across AI systems, data practices, and stakeholder relationships to remain competitive and resilient in a trust-deficit environment.
AI Compliance Agent: Building an Intelligent Eligibility Framework for Global Commerce and Cross-Border Governance
Proposes an AI-powered Compliance Agent and Intelligent Eligibility Framework to replace manual compliance checks when marketplaces expand into new countries, product categories, seller segments, or fulfillment models—helping enterprises navigate legal, trade, product safety, privacy, and governance requirements faster and at scale.
With the introduction of AI agents capable of accessing sensitive data, calling APIs, invoking tools, making decisions, and initiating actions, a critical question arises:
Should an AI agent ever receive more trust than a human user?
The next cybersecurity platform will not win by finding more vulnerabilities. It will win by proving which risks matter—and closing them safely.This is where AI agents can create meaningful impact.
Product-Led Growth(PLG) for Agentic AI in CyberSecurity
A unique growth challenge for Agentic AI in Cybersecurity: How can customers experience meaningful value before they are ready to grant an AI agent meaningful authority?
AI security is evolving rapidly. But I believe the bigger enterprise challenge is no longer simply understanding AI risks — it is turning those risks into controls that can actually guide decisions. As AI systems become more autonomous, observability and auditability become just as important as prevention. Organizations need to understand not only what an AI system did, but why it did it, what it accessed, and how to respond when behavior deviates from intent.
