• December 4, 2021

Using AIOps Tools to Enhance Cybersecurity

Artificial intelligence has grown past its buzzword status, and it is part of the digital transformation of organizations and their departments. It has a lot of potential in IT operations, from monitoring network performance to enhancing cybersecurity. AIOps provides a way to get excellent visibility of security threats, from observation to taking automated action.

The foundation of good cybersecurity is related to device identification and fast response. When dealing with security breaches, if you know where it happened and when you stand a much better chance of minimizing damage and fighting against the attacker. AIOps platforms use real-time data analytics scanned on the spot to monitor the system's health by keeping an eye on the existing devices and their activity.

Using AIOps for better Cybersecurity in Your Organization

There is more than one way that AIOps platforms can help security teams to identify, isolate, and mitigate threats.

Visibility and speed

The best cybersecurity tools offer a timely response; speed and accuracy are essential. One way to do that is to keep a clear record of all the devices accessing the network and their permissions. Since modern networks comprise a wide range of items from laptops to IoT sensors, infrastructure monitoring is done automatically in real-time; it would be overwhelming for the security team members to keep an eye on this and do their job in the meantime.

An AIOps platform for security should list and classify all connected devices, including those connected by wireless technologies or those which are present in the cloud. For all these items the system has optimal parameters defined. Any notable deviation can be the sign of abnormal activity caused by an inflow of users, system downtime, or other cases like a hacker’s attack.

If any of the devices got abnormal activity, the platform either escalates the problem to a sysadmin or takes the necessary actions to prevent damage on the spot.

Telemetry & Threat Intelligence Analysis

Security is all about being aware of what happens in the system at any given moment. Telemetry data means gathering info and sending it to a remote IT system to perform analysis on it.

To ensure cybersecurity, the data analysis includes deep packet inspection. This is a way to scan information going into a network on a much deeper level than just looking at the headings. This helps prevent malicious files like viruses, trojans, and eavesdropping. It is like reading the content of a letter instead of just looking at the envelope.

This is just one of the possible tools for threat analysis and to boost security. Any organization should strive to put all the necessary measures like firewalls, VPNs, and more. These can then be combined with the AIOps platform for in-depth analysis to identify the abnormal patterns. The advantage of using AIOps is that each step speeds up the process by making some decisions for the IT team.

Alert triage

With AI and machine learning, such a system can help IT administrators investigate incoming alerts and decide which are worthy of further action and which are just noise or false-positives automatically. Artificial intelligence uses historical records, real-time streams, and external data sources to identify possible malicious actions.

The importance of alert triage comes from managing time. In a setup without AIOps, every security alert that appears requires a team member needs to take time and handle it: create a ticket, gather data, analyze data, report, propose a course of action, supervise implementation, check if the problem was solved, test the system and write the final report to close the ticket.

Implementing an alert triage system powered by AI and machine learning means that most if not all of the previously mentioned steps are handled automatically by the AIOps platform. The platform checks the potential alerts against known threats in existing databases to establish the harm potential and only escalates those that can't be stopped automatically. This could result in a decrease of alerts by more than 90%.

Implementing AIOps: tips and tricks

Since AIOps is a very new technology and most organizations don't have the necessary experience, it is normal for CIOs to be reluctant or just cautious about adopting it.

Here are a few common ways to overcome obstacles that could stop an organization from successfully implementing AIOps platforms.

Make a top-down audit of your organization

Make a list of processes, applications, and list the security vulnerabilities for each. To identify the best use cases for AI, you need those tasks to have enough past data to use as training material. Not all processes are good candidates for AI, only those with the three characteristics of Big Data: volume, velocity, and variety.

One way to start is to look at past problems and identify the data streams that can lead you to the root cause or find good proxies for those.

Identify data sources

Since we have mentioned data, this detail could make or break the entire AI-powered initiative. Machine learning algorithms depend heavily on accurate and clean data. It would help if you had a good pipeline of data with similar frequency, consistent naming, and no missing values. If your current data handling system does not offer these facilities, you could look for a suitable AIOps provider that can help you organize your current logs to extract value from them.

Contextualize data

Not only does AIOPs need clean data, but it also helps if the data comes with meta-data or context. If your current data architecture does not offer this warranty, an AIOps provider could help through system engineering. Context is critical for performing root cause analysis and identifying event correlation. Although statistically, two sets of data could seem to have a high correlation, if there is no logical link between them, it could be just a coincidence.

AIOps for Cybersecurity: Standing up to Tomorrow's Threats

A recent article lists tomorrow's cybersecurity threats as follows: cloud vulnerabilities, adaptive malicious software, machine learning poisoning, smart contract hacking, zero-day attacks, deepfake, and more.

AI can leverage insights for an organization, but also comes with some risks which require proper mitigation. Enterprise security needs to incorporate data and machine learning into its risk management strategy to fight future threats with adequate tools. IT specialists will have an overview role, but data volume and speed will make it impossible for the security team to handle matters one by one.

As IoT becomes more prevalent, cyber threats will grow exponentially, and the public sector will need new ways to offer all the benefits of a smart city while reducing the risks of hacking.

External partner's article

Share :

Read more


XTRF is a tool that supports process management in a company. It was established in 2004 and has been developing...