Protecting Intelligent Information Systems: Security Threats, Privacy Risks, and Defense Mechanisms

Abstract

Intelligent Information Systems (IIS) have taken center stage in the modern digital infrastructures and have allowed sophisticated decision-making in healthcare, finance, transportation, smart cities, and in cyber-physical systems. Their increasing reliance on big data, machine learning models, and distributed systems, however, leads to a major security and privacy concerns. This survey provides a comprehensive analysis of emerging security vulnerabilities and privacy risks in IIS, such as adversarial ML attacks, data breach, model poisoning, re-identification threat, and privacy leakage in AI pipelines. It also discusses models of security and privacy protection like the cryptographic methods, privacy preserving ML, differential privacy, federated learning, zero-trust architectures, blockchain security, and secure semantic models. The paper examines the recent trends, such as quantumresistant AI security, cognitive security, and AI-assisted privacy surveillance as well as the legal and ethical implications associated with regulatory frameworks. Nevertheless, issues related to scalability, trustworthy AI, interoperability, and realtime threat detection remain problematic. Future research has been directed toward integrated security-by-design and privacy-by-design as approaches for developing robust, transparent, and ethically consistent intelligent systems. In general, this survey indicates that there is a critical need for effective security and privacy models to guarantee the safe and reliable functioning of next generation intelligent information systems.