What Is IT Operations?
AI and Machine Learning for Secure IT Operations (ITOps)
Information technology operations (ITOps) consists of the services and processes that an IT department runs within an organization or business. Even though the jobs of those in an IT operations department can be diverse and cover a range of activities, they are not the entire IT department. The operations division is just one out of the four parts of the Information Technology Infrastructure Library (ITIL) system. The other three are application management, service and technical management, and the service desk.
In this way, IT operations may have its own distinct crew of professionals, and they are often under the direction of the IT operations manager.
Key Elements of IT Operations
Managing the hardware and software an organization uses to accomplish business-critical objectives is a primary IT operations role. It includes providing support for hardware and software, administering network structure and functions, and managing the devices that can connect to the network.
In addition, the IT operations division is in charge of defending the assets it uses and distributes. This involves a combination of information security techniques and technologies, as well as ensuring the business maintains resiliency through backups and continuity plans.
For example, if a customer of the business experiences a problem with an app, they may discuss the issue with the service desk. If it is a minor problem, the service desk may be able to take care of it. On the other hand, if a change needs to be made to the application itself, the IT operations department takes care of it.
Role of AI and ML in IT Operations
The roles of machine learning (ML) and artificial intelligence (AI) in IT operations focus more around supporting humans than replacing them. In a way, the term “artificial intelligence” is somewhat misleading. There is little, if any, genuine “intelligence” involved and certainly very little creative thinking and problem solving, which are still largely up to humans.
Where AI and ML shine are in the way they can be programmed to mimic the kinds of intelligence humans often use to solve problems. For instance, it takes a massive amount of cognitive power to discover a single file with a virus embedded in it among 10,000 innocent ones. The process can be done without AI, but it will take weeks or even months.
A machine learning system, however, can be programmed to differentiate between benevolent and malicious files and even figure out how to mitigate the threats that different kinds of malware represent. While a human that knows what malware looks like or how it behaves will have a hard time finding it in a massive stew of good and dangerous code, the same human can write a machine learning algorithm that can do so in a matter of moments.
Artificial intelligence also plays a key role in automation. When dealing with huge amounts of data, as well as many different users, devices, and applications, there are many repetitive tasks that can consume enormous amounts of time.
For example, if a single person is in charge of checking login credentials to ensure the validity of each individual trying to connect to a network, the backlog of requests will be overwhelming within a matter of minutes. But you can use an artificial intelligence system to:
- Check the accuracy of the username and password information entered.
- Figure out the probability of a hacker trying to penetrate the system based on when and how someone tries to log in.
- Ascertain patterns that may indicate suspicious behavior, such as someone logging in with the same credentials from two different countries within a relatively short period of time.
- Examine the behavior of endpoints, including Internet-of-Things (IoT) devices, to identify abnormalities that can indicate a threat.
A machine learning system can also examine login behaviors over a period of time and figure out approximately how many represent threats and how many are legitimate. Any digital behavior that occurs repeatedly can be used as a factor within a machine learning algorithm. In this way, the IT operations team can reduce the amount of repetitive, mundane work they have to do, allowing algorithms to do it for them—and giving themselves more time to solve business-critical problems.
4 Types of Security Threats Associated With IT Operations
As mentioned, IT operations also has to mitigate threats posed by malware and hackers looking to exfiltrate data or otherwise compromise their system. Some of the primary dangers to network security include insider threats, phishing attacks, distributed denial-of-service (DDoS) attacks, and ransomware.
Insider threats happen as a result of people associated with the organization and who have access to the company’s network purposefully or accidentally misusing or mismanaging that access. Someone who can log in to a database of user payment information, for example, may be able to steal or publish that sensitive information. This can be an employee who actively sought to obtain this information after months or even years of planning. It can also be someone who was paid by a hacker to divulge sensitive information that allows the attacker to access your system.
On the other hand, someone on the inside of your organization can simply slip up and allow someone else to access their credentials. For instance, if they are logged in to an internal application via a secure virtual private network (VPN) while at a coffee shop, they may get up to use the bathroom, order some food, or talk to a friend, leaving their computer unprotected. Even though your company’s VPN prevents eavesdroppers from benefitting from stolen information, a relatively innocent mistake like this can put your system at risk. A key IT operations role is to ensure that users know how to avoid exposing the organization to threats through carelessness.
A phishing attack uses social engineering to fool users into giving up sensitive information such as:
- Login credentials
- Credit card information
- Social security numbers
- Financial data
To execute a phishing attack, a hacker will send out an email that makes it seem as if they are a legitimate, respected company or person. For instance, one of your users may get an email that seems like it comes from PayPal, and it may even include the correct fonts, graphic design elements, and color schemes. But these can be easily mimicked by someone launching a phishing attack. The email may tell the user that their login information needs to be changed and then provide them with a link to do so. When they click on the link and go to a site to enter their information, their login credentials get sent to a hacker.
A DDoS attack involves using several machines that have been compromised by a hacker to attack a server, a website, or another element of your network that handles internet requests. The number of requests is so great that the resource cannot handle them all without sacrificing operational efficiency. In some cases, it may have to crash, shut down, or deny real users from accessing important services.
For instance, if your company has an ecommerce solution that works using a web portal, a hacker can inundate your web server with fake requests. When legitimate users try to make a purchase, they cannot use the site because your server is busy trying to manage all of the fake requests.
A ransomware attack involves a hacker locking their target’s computer so they cannot access data stored within or the device itself. The user is then prompted to pay a ransom to regain access to their information or device. Often, a hacker will demand payment using a cryptocurrency like ETH or bitcoin. This is one of their go-to options because in cryptocurrency transactions, users’ identities are protected.
Ransomware, or other malware, can be spread using a variety of tactics and technologies, including:
- Attachments in emails
- Compromised applications with the malware embedded inside
- External storage devices like thumb drives
- Hacked or malicious websites that implant ransomware on users' devices
The Future of IT Operations
Artificial intelligence for IT operations (AIOps) is the future of information technology operations. It is the use of AI tools, which leverage natural language processing (NL) and machine learning models, to automate, streamline, and enhance operational procedures.
In addition to offering automation and efficiency possibilities, AIOps directly addresses a major problem that currently faces IT teams: As businesses go through digital transformations and adopt cloud computing and hybrid cloud environments, their various IT infrastructure components, applications, and performance monitoring tools produce huge amounts of data.
With AIOps, IT teams are depending less on human monitoring and intervention to manage their data and make sense of it—they are using AI-powered, data-driven automation instead.