In any IT environment, there are vulnerabilities and threats that a business needs to watch out for to prevent security breaches or ensure the availability of key IT services. Also, because no defense is perfect, organizations need to have an incident response plan (IRP) ready to go to minimize the damage that a cybersecurity breach can cause.
To this end, organizations of all sizes and industries use a variety of cybersecurity tools and resources to identify potential vulnerabilities, remediate them, and prepare for future breaches. One potential tool for improving vulnerability management and incident response that your own organization could benefit from is AIOps.
What is AIOps? How does it work? What can it do to improve IT operations management (ITOM) and business resilience?
What Is AIOps?
AIOps is the application of artificial intelligence (AI) to IT operations. IBM states that “AIOps uses big data, analytics, and machine learning capabilities” to accomplish goals like:
- Collect and aggregate data;
- Sort out signals (i.e. useful data) from noise (i.e. junk/useless data); and
- Diagnose root causes for issues to report them to IT.
One of the primary goals of AIOps is to automate processes using AI to save time and respond faster to incidents affecting IT operations management.
The 5 Core Components of AIOps (and How It Works)
AIOps platforms tend to have the following key components:
- Machine Learning. A solution for AI programs that helps the program learn from collected data to improve accuracy over time. This can be extremely useful for automating responses to potential cyber threats by helping improve threat identification over time. Machine learning is crucial for AIOps since it helps the AI solution establish what the baseline for network performance is and benchmarking that data against new data.
- Performance Baselining. In IT circles, performance baselining is a method for analyzing computer network performance. By measuring performance over time, a baseline level of performance can be established to compare future performance against. This is useful for anomaly detection.
- Anomaly Detection. After capturing baseline performance data, AIOps tools can compare new data sets to the baseline data to identify potential anomalies. This is useful for rapidly identifying potential problems in the network so they can be remediated.
- Automated Root Cause Analysis. An application of anomaly detection and machine learning that an AI solution can use to investigate network problems and identify their root cause. Can be crucial for incident response plans to keep those in charge of the IRP aware of issues and their causes—or to automate response to more proactively resolve issues.
- Predictive Analytics/Insights. Predictive analytics takes past data and uses it to establish the chances of a particular event or trend in the future. In AIOps, it can be used to anticipate when network outages are likely to happen (and their cause) based on past outages. Useful for IT risk management because it helps assess the likelihood of specific risks.
AIOps platforms work by leveraging analytical data for an assortment of use cases. Data collection and machine learning algorithms form the “backbone” of AIOps, supporting all of the functions of the platform.
How AIOps Supports Proactive Digital Operations
There are a few key ways that AIOps supports proactive digital/IT operations:
1. Predicting Possible IT Events
Using predictive analytics, AIOps solutions can help IT managers and CIOs more accurately determine when certain issues are likely to happen. For example, by looking at past analytics data for network outages and their root causes, AIOps solutions can help predict when future outages are more likely to occur.
2. Preventing IT Network Failures
Downtime on a company’s network can be expensive. For example, according to data from Atlassian, “a 12-hour Apple store outage cost the company $25 million.” Meanwhile, a “five-hour power outage in an operation center caused 2,000 cancelled flights and an estimated loss of $150 million for Delta Airlines.” These examples show that the costs of a network outage can vary based on things like:
- Company size;
- Product/service type; and
- Length of network/service downtime.
Apple’s outage may have cost the company less because the Apple store sells products and services that customers can easily wait for. Meanwhile, Delta Airlines’ outage was for time-sensitive services—meaning customers would quickly reschedule their flights with other airlines, so more sales were lost in the five-hour period without a chance for recovery.
Data cited by Data Foundry stated that the average cost of unplanned downtime was roughly $8,850 per minute. By identifying potential outage risks early, steps can be taken to prevent them—potentially saving thousands of dollars per minute of prevented downtime. This, in turn, helps to increase business resilience.
3. Automating Incident Response
Automation is a key factor in many modern AI platforms. AIOps automation tools help improve incident management for key IRP processes by streamlining workflows and reducing the need for manual intervention.
In some cases, automated incident response can fix issues so quickly that end users never realize that anything happened. This creates a smoother user experience that is more satisfying for both internal and external users alike.
4 Benefits of Using an AIOps Platform for ITOM and IT Risk Management
So, what are the benefits of using an AIOps platform for IT operations management? Some key benefits include:
Faster Mean Time to Resolution (MTTR)
One of the biggest benefits of using an AI-powered solution for IT risk management and ITOM is that it can drastically reduce the average time it takes to resolve performance and security issues. Because network outages cost more money the longer they go on, a faster mean time to resolution means greater savings in the long run.
By automating responses using AIOps platform solutions, MTTR is vastly improved over manual processes—saving time (and thus, money) when incidents happen.
Proactive and Predictive Risk Management
Knowing that an incident is likely to happen can help an organization improve its response plan for that incident. With predictive analytics, AIOps tools can identify when specific types of IT incidents are likely to happen and help the IT operations team prepare for them ahead of time—improving incident response planning.
Reducing the Time Needed for Root Cause Analysis
Investigating IT incidents manually can be a time-consuming task for businesses of any size. Larger organizations that have more data events to log in a day may find it nigh impossible to accurately trace the root cause of a network failure or other IT issue manually.
AIOps solutions can automate the root cause analysis process—providing actionable data for CIOs and their IT teams in seconds. This can be invaluable for investigating IT incidents quickly and enacting solutions that prevent future incidents from the same root cause.
Automating Remediation Workflows Across Teams
If an IT incident happens that brings down a critical service or enterprise app, does every team know what they need to do to protect the business’ interests? Waiting for manual responses to IT incidents can lead to delays in remediation.
Automating incident response to minimize the need for manual intervention helps to ensure that problems are solved quickly. This helps protect the business’ interests and minimize costs by cutting down the time it takes to resolve incidents.
ServiceNow refers to this as running IT “operations at digital speed.” Their AIOps solution provides intelligent identification of issues, automated incident creation and categorization, and intelligent analysis and remediation of issues to help companies eliminate expensive IT outages and improve visibility into IT operations.
Are you ready to transform your IT operations management? Reach out to Contender Solutions to learn more about ITOM, AIOps, and ITSM!
