Aim: Cyberattacks continue to evolve and AI has the potential to detect these threats and respond in real time. The focus of this research paper will be to understand how AI is being applied to this end, addressing how AI assists in threat detection and incident response. The work focuses on the efficiency of different add-in AI techniques which are applied for identifying anomalies, automating incidents response and providing intelligent decision support.

Method: The research procedure incorporates an all-encompassing survey called literacy review and it is aimed at gathering existing information from both academic databases and industry reports. Quantitative and qualitative select human intelligence attributes are discussed by analysing case studies and real-world examples of AI powered cybersecurity systems. This approach applies numeric data processing and open-ended exploration in order to highlight the positive impact, negative aspects, and the most common emerging trends.

Results: The paper suggests that the AI-based, cybersecurity systems can highly facilitate threat detection accuracy, diminish response time and help in identifying the emerging phenomenon to some extent. The deep learning model, from particular research, was able to detect network intrusions more than 98% precisely, while a novel unsupervised machine learning algorithm has been successfully used for detecting up to 90% of undetected malware samples. Quantitative data gives us perspective on both the benefits like increased efficiency, a scalable platform, proactive threat detection, and continuous learning, and the obstacles, including the question of quality of the data, bias of models, and the necessity of the human factor (Using Artificial Intelligence in Cybersecurity | Balbix, 2016).

Conclusion: The incorporation of AI technologies in cybersecurity techniques might able to actually be a game-changer for the manner we spot out threats and react to incidents. Although quantitative and qualitative outcomes highlight the pros of the AI-based cybersecurity systems, one should account for challenges and disadvantages that might disrupt its responsible and useful use. In conclusion, platform provides recommendations on future research, covering issues of longitudinal studies, adversarial machine learning, explainable AI, and human-AI collaboration.