What is log analysis with AI?

Log data is detailed records of events that occur in a computer system, application or network. AI and ML tools trained on large language models (LLMs) help automate log analysis, identify patterns and anomalies in datasets and deliver real-time insights into how a system or application is functioning.

With the rise of data-rich technologies like generative AI (gen AI), the number of data organizations need to collect and process is increasing exponentially. According to a recent report, data logs requiring analysis at the enterprise level have grown as much as 250% year-over-year in the last 5 years.¹

With the development of AI tools and solutions, many IT operations teams rely heavily on AI and ML tools to collect, process and analyze log files and data. Today, some of the largest organizations in the world offer AI-enhanced log analytics tools, including Microsoft through its AI-powered Azure Monitor Log, AWS through CloudWatch and IBM through the IBM Watson® AIOps solution.

Log analysis is the process of examining log data to gain deeper insights into system performance, optimization and security. Log analysis is closely related to log management the process IT teams rely on to collect, process and store log data. Both log analysis and log management deal with three log types: access logs, error logs and event logs. 

• Access logs: Access logs are logs that record common application server requests (for example, IP addresses with timestamps) and a user’s requested destination. Access logs are essential for tracking user behavior and detecting potential security threats during system monitoring.

• Error logs: Error logs contain data pertaining to a specific security incident, such as when a user or application tries to connect to a database and is denied access to it. Error logs help teams with troubleshooting efforts when they need to restore normal business operations after a data breach. Also, studying error logs after a security incident can help minimize the likelihood of downtime in the future.

• Event logs: Event logs provide IT teams with insight into a system’s activity over a specific period. They capture key events, such as system startups and shutdowns, user logins and unexpected failures, helping teams understand what occurred and when.

IT operations (ITOps) teams and DevOps engineers use AI in their log analysis workflows, from ingesting data and organizing it to applying complex data analysis and visualization techniques enhanced by AI.

In today’s fast-paced, data-rich IT environments, traditional log analysis tools often fall short in delivering the kind of insights into system performance modern enterprises need. The exponential growth in data volumes brought on by the proliferation of data-rich technologies like generative AI and hybrid cloud are often too much for traditional approaches to log analysis to handle.

AI-powered tools are transforming log analysis by automating and speeding many of the processes that used to require human input. Here are some of the most realized benefits of using AI for log analysis.

• Improved cybersecurity: Every year, data breaches cost businesses millions. According to a recent IBM Institute of Business Value report, the average cost of a data breach rose 10% last year to USD 4.8 million. AI for log analysis helps teams improve their incident response capabilities and deploy proactive threat management. AI algorithms help automate resource-intensive tasks like anomaly detection, root cause analysis and pattern recognition that used to take human teams days, even months.

• Better compliance: As enterprises struggle to keep up with changing rules and regulations in territories where they collect, store and process data, AI for log analysis helps by automating some compliance tasks. Log analysis can automate the monitoring of logs according to specific sets of rules or data governance that are applicable.

• Increased observability: AI for log analysis helps increase the observability of the systems and applications that businesses rely on for core operations. AI algorithms can be programmed to learn what normal data logs look like when a system is functioning and immediately alert ITOps team members of any anomalies. Kubernetes, a popular open source platform used to automate code deployment, relies on AI-enhanced log analysis for its advanced observability and troubleshooting capabilities.

• Predictive analytics: AI for log analysis helps companies better understand how users interact with their products by ingesting massive data logs and identifying patterns in customer behavior. This helps predict future customer behavior and identify opportunities to create new products or tweak existing ones in strategic ways.

Modern DevOps teams rely on AI to streamline processes and improve awareness of how systems and applications are functioning. For example, during the final testing and debugging phase, AI can aggregate data and flag anomalies and patterns in code so developers can adjust it before it is released to the market.

AI for log analysis helps protect systems, applications and people from a wide range of cyberthreats, including phishing, ransomware and malware. AI for log analysis increases the visibility cybersecurity teams have over their systems and applications by scouring data in real-time for patterns that might indicate a cyberattack or a data breach. According to a recent report, organizations that used AI security and automation extensively in their cybersecurity solutions saved on average, USD 2.2 million.

IT operations (ITOps) teams rely on effective log analysis tools to access and observe large amounts of data and identify performance issues. AI for log analysis helps centralize teams’ strategic approach, automating many of the resource-intensive tasks that previously required their attention.

For example, many of the “alerts” IT teams receive from traditional log analysis tools aren’t important and don’t require any action to be taken. AI can be trained to sort through these alerts and just raise the critical ones to a team’s attention.

As AI capabilities expand, AI for log analysis is depending more on a type of AI known as autonomous AI or agentic AI. In autonomous and agentic AI, AI-driven tools are built with a singular purpose to accomplish a specific goal in a complex business setting.

Unlike traditional AI models that required constant human supervision, AI agents exhibit autonomy in the way that they diagnose problems and recommend solutions. Here are a few examples of how the technology is pushing the boundaries of how AI can be used in log analysis.

AI agents not only scour large datasets for anomalies and patterns, but they can also be trained to provide a response, adapting and learning from the data they are constantly ingesting.

For example, whereas a traditional “passive” or “rules-based” AI tool might spot a pattern in a data log, an AI agent can interpret what it means and even take corrective action.

Predictive analytics is a branch of advanced analytics that makes predictions about the future by using historical data. Agentic and autonomous AI tools supercharge this process by detecting, locating and solving problems in an application before they cause a disruption.

For example, by identifying a trend in log data and comparing it to historical data from the same application, an AI agent can automate a response, like the scaling up or down of servers or virtual machines (VMs), to avoid downtime or a potential disruption.

Perhaps the most transformative capability of autonomous AI in log analysis is the generation of synthetic log data, based on existing patterns that an AI agent has analyzed. This tool allows DevOps teams to simulate a wide range of scenarios to test code against before it goes live. Previously, software testing at this level required manual input and massive amounts of resources.

For example, with autonomous AI, a DevOps team starting a new financial services app might test their code against various attacks including brute-force attempts, malware or denial-of-service, all without any manual input. Autonomous AI learns from studying log data from real incidents, so it can accurately generate synthetic log data to simulate the incident and test existing code.

Autonomous and agentic AI use natural language processing (NLP), enabling analysts to interact with them through familiar, conversational queries. NLP improves the user experience with AI agents and streamlines and speeds critical processes.

For example, rather than examining log data summaries for insights, an IT Ops team member could simply type: Any unusual activity today? And the AI agent would respond to them like a human.