Artificial Intelligence is revolutionizing cybersecurity threat detection, but it introduces new privacy concerns. Modern AI systems can analyze network traffic patterns, identify malicious behavior, and predict attack vectors in real-time. However, the data required to train these models often includes sensitive user information, raising questions about consent, data retention, and cross-border data transfers. This article explores how organizations can implement AI-driven security solutions while maintaining strong privacy governance. Key considerations include data minimization techniques, federated learning approaches that keep data local, differential privacy mechanisms, and transparent model decision-making. As AI becomes more integral to cybersecurity operations, the balance between effective threat detection and privacy preservation will be a critical factor in adoption decisions.
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