Machine Learning

The generative AI revolution sparked by ChatGPT’s public release has transformed enterprise technology adoption faster than any innovation since the internet. Organizations across industries integrate large language models (LLMs) into customer service, content creation, code generation, and decision-making processes. However, this rapid adoption introduces unprecedented security risks that traditional cybersecurity frameworks struggle to address. As enterprises deploy AI systems handling sensitive data and critical business functions, understanding and mitigating these emerging threats becomes paramount for maintaining security posture while capturing AI’s transformative benefits. ...

Financial fraud has become a $5.1 trillion global problem, with traditional rule-based detection systems struggling against increasingly sophisticated criminal networks. Artificial intelligence emerges as the decisive technology in this arms race, enabling real-time fraud detection that adapts faster than criminals can evolve their techniques. However, implementing AI fraud detection requires careful balance between security effectiveness and customer experience preservation. The Evolution of Fraud Detection Systems Traditional fraud detection relied on static rules and signature-based pattern matching. A transaction flagged if it exceeded predetermined thresholds or matched known fraud patterns. While effective against basic fraud schemes, rule-based systems struggled with sophisticated attacks that exploit their predictable logic. ...