The growing use of data-driven software systems has further widened the existing gap between software security and data privacy to subject organizations to increased technical, legal, and ethical hazards. Whereas a traditional security policy focuses on system integrity, availability and threat mitigation, privacy policies focus on minimizing data, processing data legally and giving it to users, which can lead to a more fragmented implementation and at times conflicting implementation. This paper will analyse how artificial intelligence can be used as an integrative mechanism in sealing this gap using intelligent, adaptive and automated defence strategies. The article is a synthesis of the recent studies on AI-driven security measures, such as machine learning-based vulnerability detection, behavioral anomaly detection, and automated incident response, as well as privacy-preserving methods, such as the use of differential privacy, federated learning, and secure multi-party computation. The following is a cohesive architectural view showing how AI can be used at the same time to improve threat intelligence and data governance throughout the software lifecycle. The discussion also covers such critical issues as model transparency, algorithmic bias, privacy leakage, and regulatory compliance, which limit large-scale use. Offering a conceptual framework of the convergence of software security and data privacy by defining AI as a convergence layer, the present work aids in designing resilient systems, governing them with risk awareness, and holding automation accountable. The results have practical implications to the researchers, system architects and policymakers who want to actualize intelligent defenses within the complex digital ecosystems.