Tashkent, Uzbekistan (UzDaily.com) — Artificial intelligence (AI) is becoming a key tool in the operations of Security Operations Centers (SOCs), significantly improving incident response efficiency. This trend is highlighted by Mustafa Isabaev, Senior Security Engineer at Veeam Software, an international leader in data backup and protection.

"Integrating AI into security operations is not just a technological update; it is a fundamental shift in the approach to cybersecurity," says Isabaev. According to him, AI systems can analyze vast amounts of data in real time, identifying anomalies and potential threats that might go unnoticed by humans.

Isabaev’s experience in the financial sector, particularly with cryptocurrency exchanges, emphasizes the critical importance of rapid incident response. "At Bitstamp, protecting digital assets from sophisticated threats was the top priority. This included constant monitoring for signs of APT (Advanced Persistent Threat) activity, which typically involves long-term, targeted attacks aimed at compromising secure systems without immediate detection," the expert explains.

He highlights that the introduction of AI in SOCs has significantly reduced incident response times. The automation of analysis and decision-making processes allows security specialists to identify and address threats more quickly. "One of the most promising aspects of AI in cybersecurity is the potential for automating incident response. I plan to explore how AI can help security services rapidly assess and mitigate incidents, thereby reducing response times and minimizing the impact of cyberattacks," Isabaev adds.

Particular attention is paid to AI’s integration into SOC workflows. The deployment of AI-driven analytics and automation enables organizations to strengthen their overall security posture and make more informed decisions. This is especially critical in the face of the constantly growing volume and complexity of cyber threats.

The expert also notes the growing role of AI in detecting and preventing complex attacks, such as supply chain attacks. "Machine learning algorithms can analyze massive amounts of data in real time, detecting anomalies and potential threats that might be overlooked by traditional security tools," Isabaev explains.

However, the expert cautions that AI is not a silver bullet and requires proper implementation. Hybrid models, where AI and humans work together complementing each other’s strengths, have proven to be the most effective. This approach allows AI to handle large-scale data analysis, while human experts interpret the results and make strategic decisions.

Isabaev points out new challenges associated with the use of AI in cybersecurity. "As AI becomes more widespread in security applications, it also becomes a target for cybercriminals. I am particularly interested in researching machine learning, especially when attackers exploit vulnerabilities in AI models to deceive them," he adds.

Continuous learning and development in AI and cybersecurity are becoming key factors for success in the industry. The rapid advancement of machine learning technologies presents new opportunities to create more efficient threat detection and incident response systems. At the same time, ethical and practical challenges arise with the implementation of AI in cybersecurity. Addressing these issues requires a comprehensive approach, combining technical innovation with careful analysis of potential risks and the consequences of applying AI in data protection.

The integration of AI into security operations has substantially improved incident response efficiency by accelerating threat detection and neutralization processes. Ongoing research and development in this field are focused on further improving AI technologies to tackle complex cybersecurity challenges. These efforts are expected to lead to the creation of more advanced protection systems capable of countering evolving cyber threats and adapting to the changing landscape of digital risks.