What is AIOps?
What Is AIOps?
AIOps (Artificial intelligence for IT operations) involves applying AI solutions and other similar technologies, such as natural language processing (NLP) and machine learning (ML), to IT operations. This allows IT teams to automate essential IT tasks and activities.
Digital transformation has impacted all facets of operations for modern organizations. Recent statistics show that some 70% of organizations currently have a digital transformation strategy or are developing one. The burden of implementing the strategy and ensuring its success falls largely on each organization’s IT department. While digital transformation can drive changes in IT operations, the workload typically increases during the transition. This is where artificial intelligence (AI) for IT operations can be leveraged, a strategy referred to as AIOps.
AIOps Meaning: How Does AIOps Work?
AIOps works by collecting, analyzing, and reporting on massive amounts of data from resources across the network, providing centralized, automated controls. AIOps uses AI to provide insights into how to manage organizational policies. In this way, AIOps makes IT operations more efficient and effective.
In traditional IT operations, including automation not powered by AI, IT staff initiate processes and then rely on system alerts regarding technological or security-related issues. This introduces two types of difficulties:
- The information may come from several different systems—such as legacy systems, edge devices, cloud infrastructure, and user accounts—all possibly operating on different platforms and reporting in different formats.
- The vast quantity of alerts can cause alert fatigue, which can result in staff ignoring urgent alerts that should be prioritized.
AIOps addresses these two challenges and more. It gathers data from various network sources, including but not limited to storage devices, servers, user devices, IT management systems, performance tools, etc. It then aggregates this data into actionable insights that increase visibility across the entire infrastructure. To address alert fatigue, it correlates and prioritizes alerts, so IT staff can promptly mitigate issues and threats.
AIOps for Impactful Business Decisions
To understand how AIOps impacts business decisions, it is helpful to understand how AIOps is different from other technologies that gather, analyze, and report on data. Tools that work on data, such as business intelligence platforms, will generally not recommend a certain decision. Decision-makers still need to decide what to do with the graphs and charts provided.
With AIOps, AI and ML will learn to mimic human thinking and decision-making. Therefore, AIOps can equip computers to make decisions regarding IT operations and execute them without human intervention. AIOps is also different from DevOps—although the two can work hand in hand. DevOps streamlines collaboration between software development and IT operations teams, while AIOps is the use of AI technologies to improve IT operations.
Besides giving computer systems decision-making capabilities, AIOps improves decision-making for IT teams and management because it:
- Simplifies IT solutions
- Exposes the source of technological and security issues
- Identifies patterns and their significance to operational performance
- Provides actionable insights to drive policy-making and decisions
Adoption of AIOps: A Paradigm Shift in IT Operations
The fast pace of digital transformation can create what is sometimes referred to as IT lag. This happens when digital transformation outpaces IT performance, overwhelming IT resources and causing a gap that may result in unnecessary downtime, security breaches, and a drop in overall performance and productivity.
AIOps is a solution to this gap. By combining machine learning and data science into a single solution, IT operations become more efficient and able to evolve at scale. Additionally, AIOps increases visibility by eliminating silos. AI capabilities include the ability to ingest data from a variety of sources and formats.
AIOps Definition: Components and Features of AIOps
There is no single AIOps application. Rather, AIOps is a platform that uses various technologies to leverage the benefits of AI in IT operations. Therefore, you can expect to see these features or components in an AIOps solution:
- Data aggregation: AIOps software must be able to collect data from various sources and in various formats. By eliminating data silos, AIOps increases the visibility of data across the system.
- Real-time data processing: The massive amount of data most organizations need to process would overwhelm traditional IT operations. AIOps leverages AI technologies to process data in real time, so decision-makers can respond to issues promptly before security incidents result in unnecessary downtime.
- AI-driven automation: Many programs can automate processes, but AI-driven automation mimics human decision-making, reducing the IT team's workload while providing deeper insights.
- AI and ML technologies: AI and ML solutions can distinguish patterns, spot abnormalities, automate testing, provide actionable data insights, and even predict possible outcomes through analytics.
How to Choose Between Different Types of AIOps Tools
AIOps falls into two categories: domain-centric and domain-agnostic. Which type to use depends on the type of IT operations you run and the depth of the solutions required.
Domain-centric solutions are designed to focus on a single domain within the system—monitoring and optimizing network performance or managing application performance, for example. IT teams looking to optimize a specific part of the organization's network or system may choose a domain-centric solution.
Domain-agnostic solutions are designed to collect and analyze data from anywhere across the system and solve any issue they find. They serve as a comprehensive management tool for all aspects of your IT operations. Domain-agnostic solutions are a good choice for IT teams looking for a solution that can be integrated across the system.
Top Five Use Cases for AIOps
AIOps can benefit enterprises in various ways. Here are some of the most popular AIOps use cases:
- Anomaly detection: Because AI technologies can scan and recognize patterns faster than human operators, they can recognize and categorize anomalies and their root causes within moments.
- Dynamic infrastructure topology: A topology is a map of the resources across a network and how they are connected. Many companies document their topologies, but in most cases, the resulting maps are static. AIOps provides a dynamic topology that evolves with your organization, ensuring visibility. This makes risk assessment and management more dynamic and adaptable.
- Noise reduction: AIOps can sift through an overwhelming amount of alerts to verify relevance and prioritize response. Through intelligent analysis, low-priority alerts can be suppressed and redundant alerts grouped, increasing efficiency and productivity.
- Performance monitoring: AIOps streamlines performance monitoring, effectively changing the approach from reactive to proactive. Performance issues can be resolved in real time, saving your IT team valuable time.
- Process automation: Because process automation can replace certain time-consuming and labor-intensive operational functions, it frees up IT staff for more critical functions and saves time and money.
Six Trends Defining the Future of AIOps for Businesses
Although there is no way to accurately predict the future, trends surrounding AI technologies and the evolving requirements of IT operations can help to indicate where the future of AIOps is headed.
- Improvement of data quality: The data AI feeds on and learns from must be as high quality as possible. Data quality must continually improve for organizations to continue leveraging AIOps solutions. Data annotation and proper data management are key.
- Integration of AIOps into other security solutions: Since AIOps uses ML technologies, in time it can provide deeper insights and make more comprehensive solution recommendations. There will be a need to integrate AIOps with existing security solutions to allow it to grow, evolve, and improve the security posture of organizations that rely on it.
- The ability of AIOps to predict and prepare for zero-day attacks: As AI and ML solutions grow and evolve, they will be used to create more-complex attacks. Adversarial machine learning is a possible trend that IT teams must prepare for, and AIOps solutions must be up to the task.
- Changing best practices: As AIOps solutions are implemented and their abilities improve, traditional best practices for IT operations will likely change.
- Exponentially increasing AI technology adoption: AI is here to stay, and it is expected to surpass human intelligence. Whether or not that happens, the increased use of AI solutions will change the landscape for businesses globally.
- AI as a necessity for IT operations: It is likely that AI solutions will become intrinsically linked to IT operations, so IT teams that begin leveraging AIOps solutions now may have the upper hand moving forward.
How Fortinet Can Help
FortiAIOps by Fortinet delivers proactive visibility and streamlines IT operations. It is powered by AI and ML to ensure quick data collection and identification of network anomalies, saving time and resources for IT operations.
What is meant by AIOps?
Artificial intelligence for IT operations (AIOps) involves applying AI solutions and other similar technologies—such as natural language processing (NLP) and machine learning (ML)—to IT operations.
Is AIOps equivalent to DevOps?
AIOps is different from DevOps, although the two can work hand in hand. DevOps is an approach that streamlines collaboration between software development and IT operations teams. AIOps refers to the use of AI technologies to improve IT operations.
What are the four key stages of AIOps?
The four stages of AIOps are collection of raw data, data aggregation to produce alerts, data analysis, and recommended remediation.
What are the key benefits of AIOps?
AIOps provides anomaly detection, automation, a dynamic infrastructure topology, alert noise reduction, and performance monitoring.