Role of Artificial Intelligence in Cybersecurity: Innovations and Challenges

Prabhjeet Kaur, Amandeep Kaur, Ramandeep Sandhu, Deepika Ghai, Veer P. Gangwar and Lokesh Jasrai

Chapter 9 in AI in Finance:Shaping the Future of Intelligent Automation and Financial Services, 2026, pp 185-232 from World Scientific Publishing Co. Pte. Ltd.

Abstract: In cybersecurity, artificial intelligence (AI) has widely transformed financial systems, enhancing automated security measures against cyber threats. This chapter examines how AI builds a more secure financial system. Identifying weaknesses, halting cyber threats, and preventing automated financial processes from grinding to a halt. Machine learning (ML), natural language processing (NLP), and predictive analytics help financial institutions detect unusual activities. This will prevent fraud and quickly detect threats when AI is used. The major discussion in this chapter is on how AI methods can be used in threat intelligence, compliance, and risk management. The discussion includes real-world examples of how these innovations help solve difficult cybersecurity problems using an AI-based fraud detection system and a secure blockchain transaction platform. This chapter also highlights the common challenges faced by cyber-automation systems: the use of AI, its technical, ethical, and regulatory considerations, and their impact on its use. We also consider significant examples of how AI protects financial systems. With these examples in mind, one can better understand the real-world advantages and disadvantages of today’s AI solutions. The chapter discusses upcoming developments (such as improvements in quantum computing and smarter AI systems) and provides us with different perspectives to examine how AI might transform its role in cybersecurity. This chapter provides a complete picture of how AI can secure financial systems by helping researchers, professionals, and decision-makers realize both the potential of AI and the challenges that exist in terms of using AI to deal with new cybersecurity threats. The chapter also discusses some noteworthy examples of applying AI to protect financial systems and reveals the practical advantages and disadvantages of existing solutions. It also examines future trends related to quantum computers and adaptive AI systems.

Keywords: Artificial Intelligence; AI in Finance; Financial Technology; FinTech; Machine Learning; Deep Learning; Neural Networks; Automation; Robotics; Intelligent Automation; Algorithmic Trading; Robo-Advisors; Predictive Analytics; Data Science; Big Data; Risk Management; Credit Scoring; Fraud Detection; AI Ethics; Responsible AI; AI Governance; Regulatory Compliance; Financial Regulations; Cybersecurity; Blockchain; Cryptocurrencies; Bitcoin; Ethereum; InsurTech; Digital Banking; AI in Banking; AI in Investments; AI in Insurance; AI in Wealth Management; AI in Payments; Natural Language Processing; AI Chatbots; Virtual Assistants; Customer Experience; Personalization; Sentiment Analysis; Credit Risk Modeling; Financial Forecasting; AI-powered Decision Making; Quantitative Finance; Trading Algorithms; High-Frequency Trading; AI in Hedge Funds; AI-driven Market Analysis; Automated Financial Services; Smart Contracts; Digital Assets; AI-driven Portfolio Management; Financial Planning; AI in Asset Management; AI in Lending; AI in Mortgage Industry; Financial Inclusion; Alternative Data; Explainable AI; Model Interpretability; AI and Human Collaboration; AI-driven Credit Analysis; Robo-Trading; Supervised Learning; Unsupervised Learning; Reinforcement Learning; AI-driven Customer Insights; Data-driven Decision Making; Financial Market Predictions; Behavioral Finance; Smart Finance; AI-based Anomaly Detection; Computational Finance; Financial Data Analytics; Financial Fraud Prevention; Future of Work in Finance; AI Strategy in Financial Firms; Financial Risk Analytics; Digital Transformation in Finance (search for similar items in EconPapers)
JEL-codes: C45 D81 G17 G21 O33 (search for similar items in EconPapers)
Date: 2026
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