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The article discusses the increasing security and privacy challenges posed by interconnected systems, cloud platforms, and IoT devices. It highlights how traditional security measures are becoming inadequate against sophisticated cyber threats, necessitating innovative AI-driven solutions. AI's capabilities in pattern recognition and predictive analytics are emphasized as transformative forces in enhancing cybersecurity and privacy preservation.
AI applications in security include anomaly detection, predictive analytics for vulnerability management, and fraud detection across various sectors. The article also addresses ethical considerations, such as bias in AI models and the risks of over-surveillance, while advocating for robust governance frameworks to ensure ethical AI deployment. Future directions in AI for security focus on deep learning, federated learning, and adversarial resilience.
• AI enhances cybersecurity through predictive analytics and anomaly detection.
• Ethical concerns in AI include bias, over-surveillance, and governance challenges.
This technique integrates statistical noise into datasets to preserve user anonymity while retaining data utility.
This method decentralizes data training, keeping sensitive information on local devices to minimize exposure risks.
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