How Combining AI and IT Operations makes ‘AIOps’

Artificial intelligence (AI) replicates human brain processes to help make our daily lives more automated. AI has been effectively used in industries such as cybersecurity to help filter out false positives and predict future threat variants.

But AI can also be applied to broader areas of Information Technology, which combining the two is now referred to as AIOps – “Artificial Intelligence for IT Operations”.

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What is AIOps and how does it work?

AIOps uses AI to enhance IT operations via machine learning, big data, and analytics. Its primary role is to cleanse datasets so the information it provides is used more efficiently. 

For example, the main functions in which AIOps is used are:

  • Collecting and separating large amounts of data it is fed from different IT and Network Infrastructures;
  • Filtering out any data that is apparently skewed, or does not appear to be “normal” by nature;
  • Discovering hidden trends that cannot be detected by human analysis quickly. 

Cyber Threat Researchers make heavy use of these AIOps functions to model what potential threat variants could look like in a short period of time.

The Top AIOps Use Cases

While there are many applications in which AIOps can be utilized, the following are the most commonly used in IT:

1)  Data Ingestion:

AI systems deal with varying data depending on the type of AI model and algorithms in use. For example, some will only allow for quantitative datasets to be ingested, whereas others will allow for qualitative datasets. But, with AIOps, just about any type of data can be digested, processed, and analyzed.

This is because it uses an open data framework, where both types of datasets can be used, as well as others like pictures and videos. This is great for businesses that use Hybrid environments, where part of the IT/Network Infrastructure is still On-Premises, and the remaining in a Private or Public Cloud, like AWS or Microsoft Azure.

2)  The auto-discovery:

When it comes to data being drilled into AI systems, normally historic sets will come to mind. While this is a big part of its initial learning process, AIOps can also collect data from just about any device, and anywhere else from where that data came from. 

This includes information captured on a “Contact Us” page, network security devices, various software applications running on a real-time basis, and data submitted to the Cloud. AIOps can automatically identify the source of where data is collected and use it for categorization purposes.

3)  Correlation:

Apart from quickly discovering hidden trends, AIOps can also be used to find statistical correlations between them. These discoveries can be used for data collected both On-Premises and in the Cloud.  An example is the networking connections that a company uses. Some of it will be physical in nature, whereas the rest could be virtual. By associating both, a Threat Researcher can build up a signature profile for those data packets that are malformed or look possibly malicious in nature.

4)  Making information easy to understand:

AIOps has various methods for how you want your processed information to be displayed. A popular option is to have it represented visually. This can be all viewed in one dashboard, and also multiple to map the flows between to help discover unknown correlations. You can also easily create Topology Maps, Application Maps, or other visual formats best suited to your IT requirements.

5)  Creating dependency maps:

In the digital world, just about everything is now connected and interlinked amongst one another. A lot of this has been driven by the world of the Internet of Things, or IoT.  As a result, there is not just one layer of dependency, but multiple containing data flows going in many different directions. This could take a person days to map out, but with AIOps, these multilayered relationships can be visually represented in minutes, and in the formats just described.

6)  Making use of Machine Learning (ML):

As machine learning is a subcomponent of Artificial Intelligence, AIOps can also benefit from it. Through machine learning, it can use both supervised and unsupervised learning techniques to determine patterns of events from time-series data. With this, it can help predict how a network system could potentially degrade over time. This can help IT departments by making sure all devices are optimized to their fullest extent possible.

What to take away on AIOps

Overall, this article was aimed at providing a digestible insight into what AIOps is all about, some of its technologies, and the IT applications in which it is most used. 

The use of AIOps has the potential to grow exponentially into the future, especially with the growing demands of the remote workforce needing shared resources available in real-time.

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