Information technology (IT) administrators struggle when they face many operational logs and plenty of false alarms. With the help of artificial intelligence (AI), log management systems easily manage IT infrastructure and report false errors. Log management tools with AI-enabled features provide enough functionality to reduce the amount of collected operational data into a common, manageable format and then into useful reports, alarms, and warnings. Now, features including AI easily manage large volumes of data in a critical situation where the rate of cyberattacks is increasing. Bringing AI features into log management systems opens up a range of opportunities to data-rich IT operators. Adding AI into log detection security allows actual-time detection of vulnerabilities and attacks, which can effectively turn log analysis into an actual-time monitoring system.
Moreover, AI is effective at minimizing trivial entries in log management systems. It can detect suspicious patterns and deviations from norms, including malware signatures, unscheduled network scanning, and unusual login patterns. AI-enhanced log management tools can form the part of the arsenal of security detection and response tools in enterprise IT departments. A range of tools previously unavailable or too slow to generate reports can now offer actual-time log monitoring and reporting built using AI. Thus, as AI integration improves, the range of features and functionality also evolves.
Further, machine learning (ML) application in log management helps create intelligent algorithms with information gathered in a log analysis tool. These intelligent log algorithms are used to detect and identify patterns resulting in timesaving as time spent sifting through logs is reduced. The log intelligence powered by AI and ML analyzes logs automatically, finds the root cause of issues, and surfaces anomalies that exist within log data. It also helps organizations prevent an issue even before its occurrence. Thus, the surge in the integration of AI and ML technologies into log management would create lucrative opportunities for the log management platform developers due to its high acceptance rate among large and small global enterprises.
With the new features and technologies, vendors can attract new customers and expand their footprints in emerging markets. This factor is likely to drive the APAC log management market in the coming years. The market is expected to grow at a good CAGR during the forecast period.
APAC Log Management Market Segmentation
The APAC log management market is segmented on the basis of component, deployment, organization size, industry, and country. The market, based on component, is segmented into solution and services. The solution segment accounted for a larger market share in 2020. By deployment, the market is segmented into cloud-based and on premise. In 2020, the cloud-based segment accounted for a larger market share. The APAC log management market, by organization size, is segmented into small and medium-sized enterprises and large enterprises. In 2020, the large enterprises segment accounted for a larger market share in 2020. Based on industry, the market is segmented into IT & telecom, BFSI, healthcare, retail & ecommerce, telecom, education, and others. The IT & telecom segment accounted for the largest market share in 2020. Based on country, the APAC log management market is segmented into Australia, China, India, Japan, South Korea, and the Rest of APAC.
AT&T Inc; Cisco Systems, Inc.; Datadog; IBM Corporation; LogRhythm, Inc.; ManageEngine; SolarWinds Worldwide, LLC; and Splunk, Inc. are among the leading companies in the APAC log management market.