Due to the increase in organizational data, IT processes are getting more complex. ITOps teams are finding methods that can increase their productivity. The recent COVID-19 pandemic has also added the pressure of managing remote work infrastructure. The rise of new-age technologies like AI and ML has helped ITOps teams to boost their productivity. AIOps (Artificial Intelligence for IT Operations) strategy is being implemented by many organizations to reap benefits. Read on to know about five use cases that break down AIOps capabilities.
Monitoring of software systems
There are various software systems in an organization used for providing essential system services. Ill-performing software systems will adversely affect the service availability and ROI (Return on Investment). Let’s find out how AI for application monitoring can help an organization:
- Due to a lack of performance monitoring tools, firms cannot predict the exhaustive capacity of their software systems. Application performance management solutions by AIOps can perform continuous monitoring of software systems without any manual efforts.
- AIOps monitors the entire IT infrastructure including virtual desktop infrastructure solutions, cloud resources, and other distributed IT architectures. The performance data collected is then analyzed via relationship mapping to derive insights.
- As new resources are added to the IT infrastructure, you must upgrade your application performance monitoring tools. Contrary to traditional monitoring tools, AIOps is scalable and can automatically adjust its capabilities according to the IT infrastructure.
Many organizations fix an anomaly in their service applications only when a user reports about it. However, this will have a serious impact on service reliability as you will have to fix anomalies in real-time. Predictive analytics models used by AIOps can help you in predicting future anomalies. AIOps based analytics platforms compare the real-time performance metrics with the historical data to predict anomalies within the IT infrastructure.
AIOps sets predefined performance levels and when the performance of a software system goes below that level, it immediately informs the concerned IT teams. Predictive analytics using AI applications can help you in setting automated alarms on the detection of an anomaly. It will help ITOps teams in fixing anomalies before they leave their impact on the service availability.
Root cause analysis with AIOps
AIOps is an AI automated root cause analysis solution that finds the underlying cause of an IT issue/anomaly. How AIOps helps with root cause analysis are as follows:
- Complex software systems make it hard to find the root cause of the issue/anomaly. CXOs have to analyze large data pools from different software systems to find the source of an IT issue. It is time-consuming and tedious for system administrators. AI data analytics monitoring tools can significantly decrease the MTTD (Mean Time to Detect).
- AIOps based platforms help in event correlation that identifies similar IT issues. If an event has occurred before, AIOps platforms will quickly inform that the same event has occurred again. One can follow the same procedure that was used to resolve the IT anomaly earlier and decrease MTTR (Mean Time to Resolve).
Reducing alert noise
The IT infrastructure of organizations is increasing due to a higher demand for their services. However, with more software systems, the number of alerts generated for anomalies can be significantly higher. IT teams often find themselves in the middle of numerous alerts for anomalies and can’t decide which one should be solved first.
With AIOps, you can define the range of normalcy and can proactively take steps when an event comes near that range. Real-time user monitoring tools offered by AIOps uses an inference model to select the most impactful alerts from a group of overloaded alerts. With AIOps, you can determine the critical alerts and can solve them first. Critical alerts are those which have the most adverse impact on essential software systems.
Security use cases
With the increasing complexity of IT infrastructure, ensuring security and compliance is a headache for ITOps teams. The top security use cases of AIOps platforms for organizations are as follows:
- IT operations analytics platform service powered by AI can help you with intelligent threat analysis. Besides finding behavioral anomalies, AIOps can help you with identifying the attacker sources.
- When a security issue is detected, AIOps-based platforms provide actionable insights to reduce the MTTR.
- Besides identifying external attacks on your IT infrastructure, you can also identify internal frauds with the help of real-time user monitoring tools. If a system software shows anomalous behavior, AIOps platforms can quickly identify it.
- AI data analytics monitoring tools can identify malware or ransomware in software systems without any manual efforts.
In a nutshell
The global AIOps industry worth will be more than USD 23 billion by the end of 2027. Application performance management solutions powered by AI/ML algorithms can reduce the manual load on your employees. You can handle security issues within your IT infrastructure in real-time via AIOps platforms.