Common ways to manage networks through AIOps

NetOps teams in enterprises are faced with the challenge of sifting through large amounts of incoming data to identify technical, performance, and security issues that occur on the network. This has traditionally been a manual, time-consuming process. Today, NetOps teams are prioritizing solutions that help identify problems and fix them quickly—AIOps is one of the solutions.

AIOps uses artificial intelligence to discover and understand patterns and identify anomalies in large, complex data sets. According to Gartner, "AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination."

While AIOps can do many things, recent research shows that enterprises are prioritizing use cases that help identify potential network issues quickly (such as anomaly detection/intelligent alerts and escalations) and fix them as quickly as possible (such as automated remediation of security incidents and IT service issues).

To explore this topic further, let’s dive into some recent EMA research, assessing AIOps usage and perceptions, and look at how an AIOps-driven approach can benefit NetOps teams.

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Research: Prioritize Use Cases

When it comes to AIOps, EMA's research shows that enterprises are clearly prioritizing scenarios that focus directly on keeping the network secure and running efficiently. For example, 56% of enterprises are prioritizing or implementing anomaly detection, which involves exposing unusual activity or operations outside of normal parameters, making it a top choice for AIOps.

Additionally, artificial intelligence (AI) can be trained to quickly distinguish anomalies from true threats to network operations, helping teams focus their efforts where they are most needed.

For example, when there are unusual spikes in bandwidth consumption, the enterprise needs to define policies to detect anomalies from the usual monthly trends, which can be tracked and narrowed down to certain network services or applications known or unknown to the enterprise space. This usually happens during unplanned server or data backups, or BW usage by certain applications such as large file transfers or streams.

When it comes to security incidents, the goal is to eliminate the threat as quickly as possible. According to EMA, much of what is involved in the initial response to a security incident can be easily automated, given the right rules, and such automated security incident remediation is the second-highest priority for enterprises (55%).

Automating initial security response not only speeds up problem resolution, but also allows teams to focus more closely on areas that require direct human intervention. A common scenario around automated security incidents and remediation is when an unknown application or host/IP is flagged and uses network resources, services, or corporate bandwidth. Hosts outside the enterprise can be flagged and put on a fallback list and quarantined using access lists in the process.

Handling a large number of alerts

As mentioned earlier, NetOps and SecOps teams face a large number of alerts every day, which may hide serious operational or security issues. Because AI excels at pattern recognition, intelligent alerts/escalations (53%) are the third priority use case for enterprises.

Depending on the type and level of network security breaches, service policies can be set to alert or escalate these issues. Teams can also configure basic alerts and blacklists for future analysis of simple network anomalies, which can block unidentified traffic patterns defined through service policies.

Similar to the remediation of security incidents, automating the process of mitigating issues in IT services will speed up MTTR and ensure operational efficiency. This makes automated IT service problem remediation (52%) the fourth most prioritized AIOps use case for enterprises.

To address this, teams can customize strong incident management strategies based on service-level or application-level events with appropriate alerting mechanisms (which is becoming a key priority for enterprises). At the same time, policies for recording, tracking, and managing different incidents need to be properly planned for correct remediation.

Consistent with the above findings, most organizations tend to start their AIOps deployment and integration around network security infrastructure, such as firewalls or intrusion detection and protection solutions, to better detect anomalies, escalate alerts and remediate security issues. That said, application infrastructure, including data center switching, cloud networking and application delivery network solutions, is a strong secondary priority. The last area of ​​focus for AIOps solution deployment is Wi-Fi and WAN infrastructure.

AIOps is about data

Given these AIOps priorities, and the fact that as with anything AI/ML related, AIOps is all about data, it’s no surprise that enterprises found data management (48%) to be the top skill needed by network teams. In fact, earlier research found that poor data quality is the primary technical challenge to successfully applying AIOps for network and security management. In addition to a data background, enterprises ranked general AI and infrastructure knowledge (42%) as the second-highest priority skill.

This suggests that some organizations may be developing internal AIOps capabilities or looking to modify commercial solutions. Likewise, algorithm development and API skills (39%) are both high on the priority list, again suggesting that organizations are building or fine-tuning underlying algorithms and working to integrate software and tools more broadly into the AIOps space.

The overall view is that enterprises looking to succeed with AIOps are looking to supplement their network or security teams with specific data, AI, algorithm and integration skills.

Enterprises want to answer complex questions efficiently to speed up resolution. AIOps allows enterprises to adopt AI/ML to supplement the IT team's ability to quickly identify and mitigate threats to overall network performance or security, including issues such as anomaly detection, automated security and incident remediation.

As new tools emerge, NetOps teams need to learn new skills, such as data management, AI knowledge, and algorithm development. Ultimately, this can help these teams and companies streamline workflows, better interpret data, and manage networks efficiently and securely.