Operations | Monitoring | ITSM | DevOps | Cloud

Logs can provide vital insights to help you monitor system health, pinpoint and resolve issues, and improve cybersecurity. They capture real-time errors and record information about events and other system activities, shedding light on everything from application performance to security threats. However, managing logs can be overwhelming. To get the most out of your logs, you need to aggregate them into a centralized system where they can be organized, searched, and analyzed effectively.

If you need to monitor your application’s health, troubleshoot issues quickly, and ensure compliance with various security policies, application logging is compulsory. Without proper logging, identifying the root cause of failures, tracking suspicious activity, or optimizing application performance will become significantly more challenging, if not impossible.

In the hybrid era, it’s simply not feasible for humans to manage complex IT environments without intelligent automation. Cloud computing and big data have led to larger fleets of servers, more storage systems, and more complicated networks than ever before. But there are solutions. The latest step in the evolution of IT operations is applying machine learning (ML) to manage workflows, infrastructure, and services with minimal human intervention.

There’s been no shortage of hype around the transformative potential of artificial intelligence (AI) in recent years. Our new blog series identifies three approaches to AIOps—diagnostic, assistive, and automated—and helps you to decide if they’re the right choice for your organization. First, let’s look at different kinds of diagnostic AIOps.