AI-Driven Security Architecture in Smart Cities: Balancing Safety and Privacy (Published)
Smart cities integrate interconnected technologies to enhance urban living through efficient infrastructure and services, yet this technological evolution introduces significant cybersecurity vulnerabilities that threaten critical urban systems. AI-driven security architectures emerge as sophisticated solutions, utilizing machine learning algorithms and predictive analytics to provide real-time threat detection, automated incident response, and proactive defense mechanisms against cyber-attacks. These intelligent systems process vast amounts of data from sensors, cameras, traffic networks, and utility systems to maintain the integrity and availability of essential urban services. While AI-driven security delivers substantial benefits, including enhanced public safety, service continuity, and economic protection, it raises profound privacy concerns and ethical challenges related to surveillance, algorithmic bias, and data misuse. Implementing privacy-preserving technologies such as federated learning and differential privacy, with transparent governance frameworks and public engagement initiatives, offers pathways to balance security effectiveness with individual rights protection. Future developments in explainable AI, quantum-resistant algorithms, and interdisciplinary collaboration will be crucial for creating equitable and trustworthy AI-driven security systems that serve urban communities while preserving democratic values and social equity.
Keywords: AI-driven security, Cybersecurity, IoT networks, privacy preservation, smart cities
The Evolution of Identity and Access Management (IAM) in Financial Services: From Legacy Systems to Modern Authentication (Published)
This paper examines the evolution of Identity and Access Management (IAM) systems in financial services, focusing on the transition from legacy architectures to modern authentication frameworks. Through a detailed analysis of ETRADE’s transformation as a primary case study, the article explores the challenges and solutions in implementing contemporary authentication methods, including OAuth 2.0, OpenID Connect, and Multi-Factor Authentication. The study investigates the impact of emerging technologies such as AI-driven authentication, blockchain-based identity solutions, and passwordless authentication on security effectiveness and user experience. By analyzing implementation strategies, security-usability trade-offs, and regulatory compliance requirements, this article provides insights into successful IAM modernization approaches while highlighting future trends in financial services authentication.
Keywords: AI-driven security, blockchain identity, financial authentication, identity and access management (IAM), multi-factor authentication