Brad Christian

Brad Christian

Senior Search Engine Optimization Specialist

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Understanding Artificial Intelligence for IT Operations

Artificial Intelligence for IT Operations, also known as AIOps, is a practice that combines human and technological applications of artificial intelligence and machine learning (AI/ML), advanced analytics, and operational practices for business and operations data. This practice enhances human judgment by providing alerts of known scenarios, predicting likely events, and recommending corrective actions. AIOps also allows for automation to be leveraged to improve response times for several network performance and security issues.

ITOps, NetOps, DevOps, and SecOps can all use AIOps to modernize and streamline their operations. This is thanks to the growing use and advancements in AI/ML. Solving complicated problems quickly is paramount to maintaining positive user experiences, network and application performance, and enables robust cybersecurity responses. The data aggregation and automation capabilities of AIOps help IT and security teams respond faster with more intelligence-driven strategies than ever before.

AI/ML will be integral components of IT operations moving forward. In fact, recent developments make these technologies more accessible than ever. Over time, the goal of AIOps is to bring the power of AI/ML to the forefront of IT operations, providing advanced automation capabilities to streamline processes and make better data-driven decisions.

AIOps also has several roles in digital transformation. These include:

  • Visibility
  • Automating Manual Tasks
  • Simplifying Complex Data
  • Improving Customer Experience
  • Reduced Operational Costs

Finally, Artificial Intelligence for IT Operations can be leveraged for security efficiencies. By removing manual detection of known threats, AIOps can enable security teams to expedite the removal of bad actors and help streamline operations.

What is the Importance of AIOps in Streamlining IT Operations?

AIOps helps IT teams overcome the limitations of traditional IT operations. The human element can create errors in data analysis and inefficiencies if the data is not sliced properly. When the right data is fed into an AIOps platform, it can detect opportunities to help streamline decision-making and automate several processes in IT operations, security, and other areas of the network.

So, how does Artificial Intelligence for IT Operations enhance application performance, operations, and security team efficiency? It pulls out the insights that teams need to understand what is occurring on the network and in applications to help make faster, easier decisions by providing a more thorough understanding of performance degradations and/or outages. For example, security teams can use this intelligence to hunt cyber threats, identify known bad actors, and trace where they have been within the network to track them down and oust them from the network.

It is no secret that network and application performance monitoring create a lot of data that teams need to sift through. With the power of AI, much of the data analysis can be automated, providing teams with plausible behavioral signs of performance issues and outages. This reduces mean time to resolution (MTTR) and allows teams to work on remedying issues instead of finding them. AIOps can also aid security teams in identifying common cyberattack behaviors, as outlined by the MITRE ATT&CK framework, and automate defenses against these threats the prevent infiltration or identify and trace them quickly, minimizing damage should they penetrate the network.

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What are Some of the Key Components and Features of AIOps?

Advanced analytics is one key component of AIOps. It harnesses actionable data provided by the AIOps platform. This helps create automation to reduce manual tasks, especially when actions can be repeated. Machine learning helps uncover patterns that can be used to feed automation engines.

Another component of AIOps is real-time event correlation and root cause analysis. When it comes to performance degradations, availability disruptions, or cyberattacks, time is of the essence. Catching these issues in real-time can help discover who or what is causing the disruption, where it is at, and how it can be fixed in a shorter time period than manually performing these tasks. Once an issue is discovered and located, root cause analysis can begin, providing a systemic approach to problem resolution that can have steps automated by AIOps to help restore performance, availability, or security faster than ever.

Predictive Analytics helps teams stay one step ahead of the adversary by using common patterns to predict where a threat will go next. This helps stop them in their tracks faster and more easily than traditional methods. This cyber threat intelligence feeds automated decision-making by predicting the most likely next step in a given scenario, using historical data to reduce MTTR and showcases the benefits of AIOps for security.

What Feeds the Technological Backbone of AIOps?

At its core, AIOps requires detailed, reliable data to fuel its engine. This comes from a powerful data source that can feed the AIOps solution, allowing it to process and construct actionable insights, automation, and other information. The more reliable the data source, the better outcomes AIOps can relay to IT teams.

AIOps platforms leverage the most important elements from interaction data, which is the purest form of data that can be fed into them. This allows businesses to respond to issues, such as performance degradations and breaches, in record time. Powerful data is the key to teaching platforms important patterns of network and application occurrences, allowing them to provide deeper, more actionable insights and automate processes to improve efficiency.

What are the Similarities and Differences Between AIOps and DevOps?

AIOps and DevOps are related but operate in fundamentally different ways. They are both rooted in IT Operations to formalize and create efficiencies around ITOps processes.

DevOps is intended to break down barriers and improve collaboration among operations and development teams, expediting software delivery. AIOps optimizes IT operations by using AI/ML insights. AIOps also helps you maintain and manage all the applications you use, including network infrastructure, business analytics, cybersecurity, and more. On the other hand, DevOps focuses on applications you write or create, limiting their scope.

AIOps and Security

Artificial Intelligence for IT Operations also has benefits for cybersecurity teams. By automating the detection of common threats using known adversary information, teams can identify and remove threats more quickly. This allows teams to leverage AIOps for security purposes as well as for performance by leveraging a common dataset, improving return on investment for enterprises and service providers of all sizes.

What are Some Use Cases of AIOps?

AIOps has several use cases for both enterprises and service providers. These use cases offer different benefits to improve efficiency and automation. With the right data, curated for specific use cases, and delivered at the right time, IT can correlate sources of information and solve problems faster and more efficiently than ever before.

Use cases for enterprises include, but are certainly not limited to:

  • Application and Service Performance: Assure strong application and business service performance to provide a positive user experience by leveraging the actionable data on your network.
  • Cloud Migration Projects: Identify and troubleshoot issues quickly to ensure a smooth migration to the cloud and understand underlying dependencies to ensure everything was moved appropriately.
  • Asset Management: Receive and maintain lists and data of IT infrastructure and inventory in real time. I.e., know when a device has been added or removed from the network, potentially causing performance or security risks.
  • Service Level Objectives: AIOps can help triage performance degradations in record time to meet service level objectives for application performance, availability, uptimes, and more.
  • Application Certificates Management: Automatically track certificates that may be about to expire to ensure performance and security proactively.
  • Unified Communications: Troubleshoot unified communications issues faster than ever with actionable insight and automation into performance issues to expedite MTTR.
  • Cybersecurity: Use already known adversary information to detect, find, and oust cyber threats quickly and efficiently.

Use cases for Service Providers include:

  • Heavy User Detection: Automatic detection of heavy users that are consuming a disproportionate amount of network resources. In the RAN heavy users can consume so much bandwidth on radio channels that other users are blocked from cellular access. This detection determines who the CSP needs to Block / Throttle and sends Mitigation Messages to Policy Control engine to take action.
  • Problematic Users: Automatic detection of heavy users that are consuming a disproportionate amount of network resources especially at unusual times. This activity may include cyber security threats. This detection determines who the CSP needs to Block / Throttle and sends Mitigation Messages to Policy Control to take action. With FWA (Fixed Wireless Access) on 5G there is an example of improper use of fake SIM cards turned into hot spots and the problematic users selling that access. The problem, besides it being illegal, is not so much the bandwidth usage in the core network but, again, the overconsumption of RAN (radio) resources that keep legitimate users off the network.
  • Venues: Automatic detection of service usage and experience. Given the large number of subscribers in a venue certain, high use/high value services may become stressed. Detection of service degradations alerts the CSP to take proactive measures to mitigate service issues.
  • White Glove Accounts: Customers are automatically identified (by IMSI / IP identification) from CRM tables and demographics, providing analytical information on service usage that enables the CSP to perform SLA Management by Corporate Account.
  • Network Personalization: Customers are automatically identified (by IMSI / IP identification) from CRM tables and demographics, providing analytical information on service usage that enables the CSP to design Web and Service Personalization.
  • Automated VIP Detection and Evidence Gathering: VIPs are automatically identified (by Customer IMSI / IP translation) from CRM Customers are tagged as VIP User Experience by VIP. This enables CSPs to provide overall VIP Management for Internal and External VIPs.
  • Closed-Loop VIP Configuration: Omnis integrates with CSP CRM tables that are utilized to identify VIPs. With that data enhancement Omnis automatically starts tracking VIPs, service usage and experience.

What is the Connection Between NETSCOUT and AIOps?

NETSCOUT provides the crucial backbone to any AIOps solution: powerful data. NETSCOUT's dataset enhances the data you are already receiving in your AIOps platform with rich network context to drive further actionability and utility. This enables additional automation to expedite the streamlining of processes and increase efficiency. With the Omnis AI Insights solution, powered by the Omnis AI Sensor and Omnis AI Streamer, you can experience innovation at scale to take your IT Operations to the next level. Omnis AI Insights integrates with popular AIOps providers, including Splunk, ServiceNow, Elastic ELK Stack, and DataDog.