Integrating AI Into Enterprise Security Operations

AI is gradually becoming an essential component of enterprise cybersecurity operations. According to Kaspersky’s 2026 global study, nearly 100% of organizations in the Asia-Pacific region (APAC), including companies in Singapore, Indonesia and Vietnam, plan to apply AI to their security operations.

This trend also reflects how security solution providers are integrating AI into operational workflows to accelerate threat detection, reduce the workload of analysts and respond to attacks that unfold faster than humans can handle. At the same time, cybercriminals are also using AI to automatically gather information about targets, create increasingly convincing phishing content and scale attacks that previously required significant resources and technical expertise.

This is the biggest challenge. Almost every AI capability that cybersecurity companies can use can also be exploited or adapted by cybercriminals for offensive purposes.

According to Kaspersky data, 21% of organizations believe cybercriminals are gaining the upper hand in the technology race, while 43% say malicious actors can quickly adopt new technologies such as AI to increase the effectiveness of their attacks.

Therefore, cybersecurity managers need to clearly understand how cybercriminals are abusing AI to support attacks. At the same time, companies should prioritize security solutions that integrate AI into daily system protection and treat AI deployment as a strategic decision, similar to investing in core technology infrastructure.

Deploying AI in Cybersecurity Infrastructure: Challenges and Key Steps

According to Kaspersky’s 2026 global survey, almost every company in Southeast Asia planning to build a Security Operations Center (SOC) within the next two years also intends to integrate AI into its operations. However, bringing AI into cybersecurity infrastructure is not only a technological issue but also creates organizational and technical challenges.

Data quality and data collection scope: The effectiveness of AI in detecting and analyzing threats depends heavily on the quality of input data. If data is stored separately across different systems and is not connected, AI will have an incomplete view of the entire environment, reducing its ability to detect and analyze threats.

Therefore, companies need to prioritize centralizing data from endpoints, identity management systems, cloud environments and network infrastructure into a unified platform before using AI to analyze relationships between abnormal signals and detect attacks.

Integration capability and total cost of ownership: When evaluating an AI-powered security solution, companies should not rely only on the number of features. More importantly, they should assess how well the platform can centralize data from multiple sources, reduce manual work across different tools and lower the operational workload for security teams.

Skills gaps and change management: If AI tools require security specialists to perform many complex technical configurations, they may create additional difficulties for under-resourced cybersecurity teams instead of helping close capability gaps. The most effective AI solutions are those integrated directly into the daily workflow of security professionals.

Conclusion:
AI can significantly improve threat detection, response speed and analyst productivity, but only when it is built into a unified cybersecurity infrastructure. Businesses should focus on data quality, integration, total cost of ownership and practical workflows so AI becomes a strategic security asset rather than another complex tool.