With increasingly complex IT architecture exacerbating maintenance and operation challenges, the demand for IT agility is driving the growth of the AIOps market.
Artificial Intelligence (AI) has entered our daily lives at various levels and has become a key technology in business operations. From product recommendations, smart life devices to self-driving cars, there is an inseparable relationship with AI. It is estimated that global artificial intelligence spending will increase rapidly at a compound annual growth rate of 28.5% from 2019, reaching a scale of 98.4 billion US dollars in 2023.
However, the changes brought about by AI are not only in the surrounding areas. With the outbreak of COVID-19 and remote work becoming the new normal, IT maintenance is facing many challenges and pressures. More and more enterprises are strengthening automation, agility, and flexibility. The rapid rise of IT has also led to the rapid rise of intelligent maintenance operations (AIOps). It is estimated that 70% of enterprises will actively adopt AIOps in 2021 to reduce costs, improve IT agility and accelerate innovation.
AIOps platform market size:
The global IT application AI (AIOps) platform market size is expected to grow at a CAGR of 21.2% during the forecast period to reach a size of USD 27.26 billion by 2028.
The cloud deployment model in the AIOps market is expected to show a 25% growth rate through 2023 due to the increasing acceptance of virtual work environments by enterprises. Organizations are increasingly focused on modernizing their technology backbone with scalable cloud infrastructure. The AIOps market has received a strong boost due to the impact of COVID-19. Industry acceleration has been dramatic as AI-based IT operations solutions become mainstream in unleashing enterprise resources and focusing on innovation.
The rapid digital transformation of businesses around the world has resulted in increasingly complex data sets, and businesses spend a lot of time, money, and effort processing large amounts of data. Furthermore, the decentralized architecture and the dynamic nature of business applications and services have significantly increased the data load over the past few years. As companies demand IT agility, AI Ops is emerging as a technology to address business needs and digitize IT infrastructure.
What are the application areas of AIOps?
AIOps is the application of artificial intelligence in IT maintenance, and the technologies involved also include big data and machine learning. Over time, AIOps has become more and more mature, and more and more solution providers have entered the market, whether it is from application performance management (APM), security information and event management (SIEM), event correlation and Analysis (ECA), or IT operations management (ITOM), IT operations analysis (ITOA) and other aspects, there are relative application solutions.
With the evolution of AIOps technology, many solutions do not focus on a single aspect, and some even combine two to three different applications. While some solutions are single application areas, they are developed from the perspective of large frameworks.
AIOps platform is a software system that combines big data, artificial intelligence, or machine learning functions. It is used to enhance and partially replace a wide range of IT maintenance processes and tasks, including availability and performance monitoring, event correlation and analysis, and IT service management. and automation. IDC defines AIOps as tools that use big data and analytics (BDA) and AI technologies to support, automate and enhance IT operations (ITOps). The AIOps platform mainly collects various data through ITOps tools and equipment to detect problems and respond in real-time. In addition, AIOps systems can also automate software maintenance and orchestrate many IT system layers, making them increasingly autonomous and self-tuning.
AIOps has the following capabilities:
- Diverse datasets
- Large-scale platform that aggregates data and event information powered by big data
- Machine learning algorithms and analytical processing
- API and automation capabilities
- Granular reporting
How can AIOps applications optimize enterprise cloud security operations?
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Threat Intelligence Analysis:
Threat intelligence provides a view of the source of attacks, showing all behavioral trends related to the use of cloud accounts and attacks against various cloud services. Threat intelligence feeds can be collected and analyzed at scale using machine learning engines in the cloud that can be processed against predictability models. AIOps has extensive IT operational data as part of AIOps, as well as additional threat intelligence from external providers. These attributes will help security operations teams predict and assist during attacks on cloud infrastructure, especially in the case of account hijacking.
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Security Incident Management:
Businesses that have digitized their processes are flooded with data, and security teams need to be constantly vigilant to identify specific indicators, event patterns, and discovery events in cloud systems. With machine learning and artificial intelligence capabilities, AIOps can augment massive data processing techniques to provide more robust intelligent detection and alert action plans.
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Fraud Detection:
For companies dealing with financial services and insurance, fraud detection requires many input and data types and intensive types of processing. Text mining, database search, social network analysis, and anomaly detection are part of the system. These attributes, combined with predictive models, help detect fraud quickly.
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Malware detection:
With the help of machine learning and artificial intelligence techniques, large-scale event processing of data and files can help detect ransomware and malware, especially those data points whose signatures are unknown. While AIOps supports this application, custom malware detection requires security professionals with high skills in the field.
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Data classification and monitoring:
AIOPs Analytical Engineers, also known as Content Types and Schemas, process the entire data uploaded. It is classified and tagged according to predefined policies and then monitored for access. Data-specific monitoring relies on operations teams managing multiple types of data with the help of security and risk teams to flag and track data types or patterns.
Advantages of AIOps: Save man-power and time to create more revenue
The benefits of AIOps are the reduction of repetitive and low-value work, resulting in increased productivity, faster root cause analysis for fast problem remediation, and better infrastructure performance. With AIOps tools, the value will be created from proactive IT operations and more resilient hybrid infrastructures.
Although different industries consider AI differently, the purpose is very similar. In terms of time-saving, the medical industry hopes to use AI to quickly identify diseases, but in the field of the Internet, it hopes to use AI to quickly check and repair. For enterprises, AIOps can help save manpower and time, thereby creating greater revenue.
Automation will drive AIOps maturity:
Although the AIOps platform is still in its early stage, the challenges faced by enterprises will also drive the future development of AIOps. Especially with the development of digitalization, enterprises are becoming more and more dependent on IT, but the infrastructure is becoming more and more complex. Many enterprises embrace the cloud and build a hybrid IT architecture, only to find that they cannot effectively control the cloud. With the diversification of business models, the requirements for service standards are getting higher and higher, which will also drive the growth of demand. Most enterprises need centralized monitoring, but the data is somewhat scattered, or they place more emphasis on information security while ignoring the importance of overall IT maintenance. However, under the trend of automation, the method of IT maintenance must be to remain diligent.
To respond to unknown changes more quickly, enterprises accelerate digital transformation, introduce new workflows and technologies, and introduce the thinking and framework of agile development operations (DevOps), to meet the needs of users in a more timely manner and obtain better results. Good business innovation results. However, after the massive use of cloud computing, containers, and microservices to create a decentralized and hybrid IT architecture, maintenance and operation management have become one of the pain points of IT. Users' requirements for service quality are increasing day by day, and sudden problems of IT architecture need to be solved faster and faster. Even the introduction of DevOps is a factor that deepens the maintenance challenges. The purpose of implementing AIOps is to alleviate the pain of IT maintenance and operation. It is achieved through AI machine learning and data analysis in a similar way to automation.
Challenges of AIOps development:
meet the needs of users in a timelier manner and obtain better results. Good business innovation results. The purpose of implementing AIOps is to alleviate the pain of IT maintenance and operation. It is achieved through AI machine learning and data analysis in a similar way to automation.
- Since the platform is completely data-dependent, the quality of the data plays a crucial role. Many organizations realize that data may be limited, may be of poor quality, may be missing or incomplete, and may be inconsistent. Data problems will affect the inaccurate decision-making basis of AIOps, which will affect the difficulty of importing the AIOps platform.
- As AIOps caters to the need to automatically optimize IT operations, having good data alone will not serve the purpose. A high degree of industry domain knowledge is required for better discrimination. Knowledge about business models and how those patterns affect IT decisions has a lot to do with how AIOps works but having domain knowledge is difficult and therefore a challenge that AIOps often faces.
- Another challenge for companies is that AIOps detects anomalies that the company itself is not ready to deal with at a given point in time.
Outlook for AIOps:
Companies are turning to AIOps to find quick solutions to existing problems. A key issue addressed is reducing their troubleshooting time. It's worth watching how AIOps can be used strategically and how AIOps can help fundamentally redesign operations. With AIOps, a higher level of automation can be achieved in network management. Solutions will be able to identify problem areas, match problems to solutions, ultimately select solutions based on probability of success, and even monitor and evaluate outcomes.
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