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Understanding AIOps and Its Impact on Cybersecurity Management

AI’s ubiquitous influence is revered across all industries, especially the gigantic cyberspace where every minute, you are not just practically consuming a mind-boggling number of data but also producing around 2.5 quintillion bytes of data every single day. The rapid expansion of AI has resulted in a massive tech revolution where billions of users are protected, data is safe, and organizations are constantly improving their systems and getting things done more swiftly and efficiently. A large part of this can be credited to AI and its stunning array of machine learning tools and analytics which is helping users and companies to manage, organize, and analyze volleys of data, incorporate software changes, send alerts and incorporate proactive, uniform monitoring of the unknowns.

What is AIOps?

AIOps is an amalgamation of two terminologies – artificial intelligence and operations. It is the process of managing big data, and analytics, through AI, natural language processing, and machine learning tools to run a business smoothly and take better and more economical decisions. When intertwined correctly, AI and operations help businesses to automate and streamline a host of IT operations such as anomaly detection, event correlation, and causality determination.

The term AIOps first came into existence in 2016. It was introduced by Gartner- an IT research and Consultancy Company. The term defines the use of artificial intelligence in any organization’s existing IT operations to obtain optimal results at minimal costs.

Why AIOps is the need of the hour

In the current scenario, AIOps are the need of the hour, especially as the pandemic ushered in a lot of changes in the working style. Today, some companies are completely remote and some are following the hybrid working model, in either case, businesses are under tremendous pressure to detect data anomalies, critical issues, and multiple service interruptions.

In a remote setup, AIOps are proving to be more helpful as the sudden shift in the work environment created many unexpected and uncontrollable in the realm of IT infrastructure.

Recommended: How AI is Playing Catalyst in Strengthening the Hybrid Working Environment

What is AIOps’ main function?

The primary role of AIOps is to streamline and automate IT operations and processes and identify and offer faster and more efficient solutions. AIOps helps any organization’s existing IT infrastructure to find network performance issues, detect threats and perform a variety of basic, essential tasks.

There are 4 stages in incorporating AIOps. To get started, let’s take a look at the key stages AIOps consists of.


The first and foremost stage in AIOps is quite simple. You need to identify the problems, and their downtime, and figure out the assistance you will need from AIOps. Remember the 3 whys while looking at the problem:

  • What’s the problem?
  • Why is it occurring?
  • Which AIOps capabilities can help and offer solutions


After identifying the problem, understanding your data and factors like where the workflow abruptly slows down or halts and which AIOps will fit aptly, the second most obvious step is to closely assess the current environment, understand which team is the most affected and how quickly the AIOps offer solutions.


Here, this stage refers to the success rate of the deployment of AIOps and it doesn’t necessarily mean an instant win. Do not look at the quickest solution, instead go for a solution that offers not just a solution but also gradual improvement and whether it is a value for time or not.


After carefully observing the situation and the potential role AIOps will play, this stage further includes the process of determining the right place to start, i.e. deployment of AIOps across the organization. So choose wisely.

AIOps – The Power of Automation in IT

Collecting Data

It collects the ever-increasing amounts of data generated by a variety of IT infrastructures, performance monitoring tools, and service ticketing systems.  With an all-in-one tool, AIOps smartly enables you to proactively work on sudden system slowdowns and outages with maximum transparency.

Recommended: Decoding AI: Types of AI and Machine Learning Models

Filtering Signals from Noise

AIOps cleverly distinguishes signal from noise to find significant events and patterns. No matter how skilled and experienced you may be, it is a little too ambitious to assume that a human mind can always extract significant signals from noise.

Finding the root cause

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Identifies the root cause of the problem and promptly informs the IT for resolution, or in some cases, finds a solution on its own. Rapid remediation is the key here.

Benefits of AIOps

AIOps enables businesses and their IT operations to observe, identify and address issues including slowdowns, and outages way faster than manually looking through the alerts from a variety of IT operational tools. Let’s take a look at the benefits of AIOps.

Quicker Mean Time to Resolution (MTTR)

AIOps empower organizations to cut through the IT operations noise and operations data and identify root causes and offer quick solutions that are way more accurate than the human minds. This helps the organization not just to set but also achieve nearly impossible MTTR goals.

Reducing Operational Costs

With AIOps, companies can automatically detect operational issues and reprogrammed resource scripts enabling reduced operational costs and simplifying resource allocation. This way, team members assigned to this task can be allocated to more innovative work further enhancing the employee experience.

Better Cross-Team Collaboration

The integrations available within the AIOps monitoring tools enable better team collaboration and a more cohesive working environment across DevOps, ITops, and security functions. The improved visibility, communications, and transparency help the team members to enhance their decision-making process and respond to various issues more swiftly.

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A More Predictive Management

Endowed with in-built predictive analytics skills, AIOps continually learn to prioritize important alerts, enabling IT teams to deal with potential problems before deciding on slowdowns and outages.

The Impact of AIOps on CyberSecurity

The tech landscape is constantly evolving which means that the threats are more complex and have drastically increased in numbers. Today, most financial, government, and healthcare organization has suffered some kind of data breach or ransomware attack. Today, with the help of AIOps, companies are in a better shape to tackle data volumes, detect potential threats, protect data, distinguish true signals from noise, and manage event velocities. Considering all of these activities, it could be nearly impossible and terrifically overwhelming for the human team to perform these tasks constantly and without any error.

Spam filtering

Almost a decade ago, tech giants like Microsoft and Google were vying for new-age tools to fight unwanted messages and prevent them from reaching the inbox. These top companies were passionately looking for advanced technology to ace the spam-battling techniques. In recent years, with the help of AI, Gmail spam filters automatically move spam email messages (sometimes called junk mail) into users’ spam folders. Spam filters block unwanted, unsolicited, or dangerous messages from popping into inboxes and ensure users receive relevant content.

The company successfully managed to drop the spam rate ‘down to 0.1 percent, and its false positive rate has dipped to 0.05 percent.

Neil Kumaran, Group Product Manager, Gmail Security & Trust, while throwing light on Gmail’s spam fighting mechanism, stated that with the help of several AI-driven filters, Gmail determines what is marked as spam. The filter considers various signals such as IP address characteristics, domains/subdomains, etc.

User feedback, such as when a user marks a certain email as spam or signals they want a sender’s emails in their inbox, is key to this filtering process, and our filters learn from user actions.’

Fraud detection  

MasterCard implemented Decision Intelligence, an AI-based fraud detection that uses algorithms based on predictable customer behavior. It assesses customers’ typical spending habits, the vendor, the location of the purchase, and a variety of other sophisticated algorithms, to assess whether a purchase is out of the ordinary.

The comprehensive decision and fraud detection service use artificial intelligence technology to help financial institutions increase the accuracy of real-time approvals of genuine transactions and reduce false declines. Unlike the traditional decision-making approach that is focused more on risk management,  Decision Intelligence uses a new approach to take a broader view in assessing, scoring, and learning from each transaction. That score then enables the card issuer to apply the intelligence to the next trade.

Ajay Bhalla, president of enterprise risk and security, at Mastercard explained that the company was looking at a major pain point of a false decline while trying to make a purchase. With the use of AI-driven technology on the global network, the company is enhancing the approval rates for financial institutions and merchants while working on the overall consumer experience.

Botnet detection

This complex field heavily relies on recognizing patterns and timings in network requests. Typically controlled by a master script of commands, a large-scale attack involves multiple ‘users’ replicating similar requests on a website. This could be failed logins (a botnet brute force attack), scanning for network vulnerabilities, and other exploits.

In a nutshell

In the dynamic yet awe-inspiring journey of technological advancements and data consumption, AIOps is the next natural step in automating the most simplest or complex of tasks. To make better decisions, reduce costs; delve into deeper insights, and enhance data privacy and overall security in the IT infrastructure, AIOps could be the major driving force. In a nutshell, AIOps are the future of IT operations and management offering a holistic view across applications and networks while intelligently bridging the gap between the diverse technological landscape and human minds.

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