What is AIOps
AIOps stands for 'artificial intelligence for IT operations'. It refers to platforms that leverage machine learning (ML) and analytics to automate IT operations. AIOps harnesses big data from operational appliances and has the unique ability to detect and respond to issues instantaneously. Using the power of ML, AIOps strategizes using the various forms of data it compiles to yield automated insights that work to refine and iterate continually. AIOps seeks to address a quickly evolving IT landscape using the convenience of machine learning, automation and big data. The video gives a brief overview of what AIOps is and how it works.
AIOps products have a standardized approach to functionality. The first step in the process involves the extraction of data. Tools must collect data coming from various systems and then cluster it in an appropriate manner that makes the next step in the process most efficient. Then, thorough analysis of the aggregated data is conducted. Using ML algorithms, these tools detect patterns and relationships between pieces of data while identifying root problems and focal points within a system. In the next stage, AIOps looks to apply its “critical thinking skills” to react to the findings of the previous analysis. This entails deploying an automated optimization of IT operations, while also using the patterns it has detected, to learn and funnel closer to potential pain points. This technology is generally paired with the ability to provide comprehensive analytical reports that help people make more intelligent, data-driven decisions.
Tools must have certain operational competencies to be AIOps solutions. First, they must be able to normalize data from different sources, applications and infrastructures such that they can perform an accurate analysis. Next, the tools have to be able to understand the logic flows connecting different IT assets within an organization. Finding associations and merging events is equally as important because it reduces the need for human interference, as is the nature of artificial intelligence (AI). The main functionality of AIOps platforms is being able to use telemetry – data collected from remote points and directed to an IT system for analysis – to predict, prevent or detect issues, and then use machine learning to adapt and refine the process.
Figure 1: AIOps Event Correlation and Analysis
AIOps provides real-time analysis and detection of IT issues while optimizing its approach using machine learning. With the growing adoption of the cloud, AIOps will become more necessary to optimize IT operations. The value of AIOps platforms lies in its core purpose of recognizing patterns, learning and then improving its approach to detecting IT problems all through the use of machine learning frameworks that do not require human intervention. AIOps does not just stop at alerting though; it handles the burden of also taking action on the infrastructure problems it detects.
One of AIOps’ strongest alignment is with the growing efforts to improve cloud security. Given the integration with threat intelligence data sources, AIOps has the capability to predict and even avoid attacks on cloud frameworks. AIOps can also play a major role in the automation of security event management, which is the process of identifying and compiling security events in an IT environment. Through the benefits of ML, AIOps can evolve the process of event management such that observational and alerting approaches can be reformed. Fraud detection is certainly a use case for AIOps as well, since this traditionally requires the tedious process of sifting through data and using predictive analytics to form a proper detection of fraud. Automating the numerous inputs and sources of data required in this process would save time and cost for an organization. In one of its simplest automation use cases, AIOps can monitor and “tag” data based on a specific set of rules and categories that are defined for it.
This demo dives deep into how AIOps works and can provide AIOps use cases and training for those ready to implement.
Of the many benefits that AIOps has to offer, perhaps the clearest is the aggregation of several different monitoring tool functionalities in one place. As the monitoring landscape becomes more complex, one of the biggest challenges has been having to search across five-to-ten monitoring tools just to identify root causes. AIOps provides a single platform where all the data between heterogeneous sources is normalized and correlated such that it makes more logical sense to display everything on one dashboard.
One of the biggest concerns is the growing number of alerts across monitoring tools and how to manage them. This is where AIOps comes into play. Having a tool driven by ML algorithms that continually adapts and builds on its knowledge is helpful in organizing these alerts and saving organizations the time and human capital needed to do this effectively. AIOps helps to reduce downtime while also identifying and prioritizing issues and alerts.
AIOps also has one specific capability that humans do not have: predictive analytics. As mentioned earlier, one of the initial stages of the AIOps process is compiling and analyzing data. This technology is able to make informed, automated decisions using the data it is presented. Going one step further, AIOps is able to predict future issues and correct them before they have a negative impact on performance.
All in all, these benefits and use cases justify the broad adoption of AIOps to improve IT operational efficiency.
SD-WAN and AIOps
SD-WAN, or software-defined wide area networking, has brought a lot to the table in recent years, adding agility, resilience and lower costs to the WAN architecture. The adoption of this valuable mechanism was even further accelerated by the COVID-19 pandemic, as network connectivity became nothing short of an utmost priority for businesses. While this has lessened the need for expensive IT labor in the deployment process, there remains the issue of detecting and resolving WAN outages. This is where AIOps benefits in the SD-WAN landscape. Having automated event correlation integrated with SD-WAN will help pinpoint network issues in an environment that, by nature, tends to conceal outages due to the elevated resiliency. Systems leveraging artificial intelligence can handle large volumes of data and identify the most intricate red flags through predictive analytics. AIOps is definitely the means of expanding the range of SD-WAN’s capabilities and effectiveness.
Palo Alto Networks has made meaningful strides with AIOps through Prisma SD-WAN . Traditionally, legacy SD-WAN focuses on enabling the shift away from Multiprotocol Label Switching (MPLS) in order to cut costs, but Palo Alto Networks believes in a next-generation solution that delivers automation, lower total cost of ownership, greater application performance, and a rich set of security and network services from the cloud. The recently released powerful new AIOps enhancements for Prisma SD-WAN include event correlation and analysis, improved dashboard views, and telemetry exporting to third-party collectors. With organizations scaling at a merciless rate, the simplicity and automation of network operations have never mattered more.
Gartner has a Market Guide for AIOps Platforms that evaluates vendors and provides insights for leaders into how AI-driven technologies with ML and predictive analytics can benefit an organization's IT operations and in turn save costs. Gartner also provides trends and key findings as the growth of AIOps platforms continues to grow. Prisma SD-WAN has AIOps capabilities to help reduce and automate tedious network ops. Prisma SD-WAN was recently rated as a Leader in the 2021 Gartner Magic Quadrant for WAN Edge Infrastructure report.
Learn more about how to simplify network operations with Prisma SD-WAN.