How AIOps can optimize incident management teams

What is IT incident management?

IT incident management is a systematic approach to handling incidents in IT systems. An incident can be any sudden problem that interrupts normal business operations or degrades the normal performance of IT systems. IT incidents have unique challenges and require different response procedures:

• Software failure. Includes all problems caused by software, such as bugs, application errors, misconfiguration problems in cloud environments, operating system crashes and incompatibility issues.
• Hardware problems. Includes all hardware defects such as server or endpoint device hardware crashes, network device (router, switch, modem) crashes, hard drive crashes and problems with network printers.
• Computer networking failure. Any problem that affects normal network operations, such as network bandwidth congestion or internet connectivity issues, and any problems in configuring network devices and security solutions such as firewalls and intrusion detection systems that result in network interruptions.
• Power failure. Power failures, whether caused by natural disasters, human errors or problems with electrical equipment and wires, can shut down the entire IT infrastructure and make IT systems and data inaccessible.
• Third-party errors. Most organizations utilize services from third-party providers, such as cloud providers and managed security service providers. A service interruption on their end will disrupt the normal business operations of their clients.
• Security incidents. Cyberattacks against IT systems, such as DDoS, phishing and unauthorized access attacks, ransomware and malware.

Artificial intelligence for IT operations

The emergence of AI and related technologies has introduced radical changes to how organizations manage and handle their IT operations processes. The use of AIOps is not limited to incident management alone; it spans other areas such as change, release, configuration security, capacity and availability management.

AIOps can introduce significant advantages to how teams manage incidents:

• In a typical IT environment, security tools and network devices generate large volumes of log data. AIOps can process and analyze this data in real time which gives operations teams immediate insight into what is happening in their environment.
• AIOps can analyze previous systems' logs and normal usage behaviors of users and compare them with the current behaviors to detect anomalies and suspicious patterns in real time. This task is impossible to achieve manually and prevents some types of security incidents.
• You can configure AIOps to respond automatically and take corrective measures for specific incidents. On the other hand, the AIOps tool powered by ML models can learn from previous incidents and adopt mitigation activities accordingly.

How do AI technologies facilitate IT incident management?

Standard AI technologies used in incident management tools include ML, natural language processing (NLP), computer vision and data analytics. When combined with incident management tools, these AI technologies can facilitate operational team efforts in IT incident management in the following areas.

Faster resolution time

AIOps can detect suspicious patterns and other anomalies across IT environments quicker than traditional tools that rely on human intervention. The ability to rapidly identify incidents will result in faster incident resolution.

Automatic notification

To streamline the monitoring process, AIOps tools gather data to identify the most valued alerts. AI's ability to analyze alerts gives these tools an enhanced ability to detect actual incidents and strip false positive alerts.

Interprets incident reports

By using NLP algorithms, AIOps tools can categorize and classify incident reports based on their textual descriptions. This automates the process of escalating incidents based on their severity and type and helps teams prioritize incidents automatically.

Proactive recommendations

NLP algorithms can suggest remediation steps for current incidents which significantly reduces the time to resolve them.

Reduce IT team costs

AI-powered chatbots can interact with users to collect incident details and guide them through a predefined process to solve problems. This reduces tech support costs and allows organizations to allocate their resources to focus on other urgent tasks.

Monitoring IT infrastructure

Computer vision, a subtype of AI technology, can monitor IT infrastructure. For instance, it can monitor server rooms to detect temperature fluctuations and alert operation teams about potential failures. Computer vision can also detect and alert about unauthorized access to protected areas like data centers, which helps secure physical infrastructure from theft or sabotage.

Root cause analysis

AI tools can create a visual graph that demonstrates how different components interact in your IT environment. The graph shows how a specific problem relates to other components or IT services in your IT environment. This helps the operation team detect the root cause of the problem and associated dependencies more efficiently.

Challenges of adopting AIOps tools in organizations

Adopting AIOps tools within your organization comes with some challenges:

• Privacy issues. AI-powered tools depend on ML models that are trained on a large volume of data. Keeping such sensitive data confidential is a major challenge for organizations when using AIOps tools.

• ML model data quality. The performance and output accuracy of AIOps tools heavily rely on the data quality used for training the underlying ML models. ML models suffer from different cyberattacks, such as data poisoning, data manipulation, backdoors and software supply chain attacks. Utilizing compromised ML models in your AIOps will lead to inaccurate recommendations.
• Integration issues. Integrating AIOps tools with existing IT infrastructure is a big challenge that you must address before using AIOps in your IT environment.

Nihad A. Hassan is an independent cybersecurity consultant, an expert in digital forensics and cyber open source intelligence, and a blogger and book author. Hassan has been actively researching various areas of information security for more than 15 years and has developed numerous cybersecurity education courses and technical guides.