A Comprehensive Survey on AI in Counter-Terrorism and Cybersecurity: Challenges and Ethical Dimensions

The advanced capabilities in threat detection and mitigation brought about by the rapid development of Artificial Intelligence (AI) significantly impact the fight against terrorism and cybersecurity threats. However, critical concerns such as algorithmic bias, data quality limitations, and governance challenges introduce significant obstacles to their deployment. This paper provides a comprehensive overview of AI methodologies, such as predictive analytics, Natural Language Processing (NLP), and machine learning architectures (e.g., Support Vector Machines – SVM and Long Short-Term Memory – LSTM), and optimization algorithms (e.g., Particle Swarm Optimization – PSO), assessing their effectiveness in security applications. Additionally, AI-based approaches to surveillance, misinformation management, and anomaly detection are explored, focusing on their impacts on national security. Beyond the technical aspects, the paper highlights ethical concerns and policy issues, putting forward frameworks such as Explainable Artificial Intelligence (XAI) and crowdsourced intelligence (Crosint) to ensure the responsible and transparent deployment of AI. The integration of the technical, ethical, and operational perspectives addressed in this research contributes to a holistic understanding of the potential of AI in cybersecurity, while also ensuring adherence to AI governance standards.