The integration of artificial intelligence (AI) with cybersecurity represents a transformative shift in defending digital assets against evolving threats. This review explores AI's role in enhancing threat detection, vulnerability analysis, and incident response, while addressing ethical and privacy concerns. AI, through machine learning and deep learning, enables automated, intelligent, and adaptive cybersecurity solutions. Traditional methods of threat detection, such as signature-based and rule-based approaches, are increasingly inadequate against sophisticated attacks, making AI-driven detection more effective. AI can analyze vast datasets in real-time, identify anomalies, and prioritize threats with high accuracy. In vulnerability analysis, AI automates scanning, prioritizes critical issues, and improves accuracy by learning from historical data. In incident response, AI-driven automation enhances efficiency, reduces human error, and enables faster, more effective threat mitigation. However, challenges such as data privacy, algorithmic bias, and the "black box" problem in AI models must be addressed. Ethical considerations, including transparency, fairness, and accountability, are crucial for responsible AI deployment. Emerging trends like adversarial machine learning and zero trust security offer opportunities for further innovation. The future of AI in cybersecurity depends on balancing technological advancement with ethical principles and privacy protection to ensure a safer digital environment.The integration of artificial intelligence (AI) with cybersecurity represents a transformative shift in defending digital assets against evolving threats. This review explores AI's role in enhancing threat detection, vulnerability analysis, and incident response, while addressing ethical and privacy concerns. AI, through machine learning and deep learning, enables automated, intelligent, and adaptive cybersecurity solutions. Traditional methods of threat detection, such as signature-based and rule-based approaches, are increasingly inadequate against sophisticated attacks, making AI-driven detection more effective. AI can analyze vast datasets in real-time, identify anomalies, and prioritize threats with high accuracy. In vulnerability analysis, AI automates scanning, prioritizes critical issues, and improves accuracy by learning from historical data. In incident response, AI-driven automation enhances efficiency, reduces human error, and enables faster, more effective threat mitigation. However, challenges such as data privacy, algorithmic bias, and the "black box" problem in AI models must be addressed. Ethical considerations, including transparency, fairness, and accountability, are crucial for responsible AI deployment. Emerging trends like adversarial machine learning and zero trust security offer opportunities for further innovation. The future of AI in cybersecurity depends on balancing technological advancement with ethical principles and privacy protection to ensure a safer digital environment.