AIOps (AI for IT Operations) within IT can greatly help all departments, from network operators to security teams. Although AIOps capabilities can be leveraged by a wide variety of departments, one of its significant applications is in cybersecurity.
AIOps assists the cybersecurity department by helping it gain a significant amount of speed, visibility and intelligence when it comes to data security and threat detection. These tools accomplish various tasks – from observation to actions on threats – details of which will be discussed below:-
How AIOps Helps Cybersecurity
Two of the main aspects of cybersecurity are speed and device visibility. If one can pinpoint the location of the source of a cyberattack and when it happened, it can give a significant edge in catching cybercriminals. AIOps platforms use collected streaming telemetry (a real-time data collection service where network devices continuously push data related to network’s health to a centralized location) data to inventory, auto-discover and classify the devices.
Most AIOps platforms, in addition to inventory network infrastructure components, also assess all the wired, wireless and IoT devices communicating in the corporate network or the cloud.
Another area where AIOps can help is in network segmentation. AIOps can use device classification to ensure that business devices are connected to an appropriate virtual LAN or a wireless set identifier. This network segmentation is essential when it comes to edge security and having tools (AIOps) that can quickly help spot problems of connection, etc, that are in high demand.
Whatever DPIs and other telemetry data are collected, these can be used to plot device communication behavior over time. In the event of an abnormal occurrence, an alert is triggered to investigate the potential threat to the security.
A lot of AIOps also include threat intelligence analysis services. These are services where reports are produced about various threats for the security control system. Most AIOps integrate with other security tools, including SIEM, network firewalls, security orchestration, automation and response. These tools, along with AIOps traffic behavioral analysis, can monitor better security threats.
Need For Human Intervention
AIOps, as much as it involves AI that promises complete automation, still needs some human involvement. The AI within AIOps needs to be told about which apps, services and other resources are business-critical. This includes identifying essential data flows to ensure that AIOps platforms understand the priority in which the security events should be dealt with.
AIOps provides detailed information when there is an alert triggered. A network administrator comes in to investigate the alert and make decisions about the suggestions given by the AIOps. While some AIOps automate the responses, an administrator is still needed to intervene.
Some Common Pitfalls Faced By AIOps
A successful adaption of AIOps needs awareness of the potential problems associated with it. Below, we have mentioned some of the top concerns when it comes to AIOps implementation:
#1: Identifying use cases, not processes
Each new AI and ML feature may seem like a perfect opportunity to increase efficiency for an organization. But all features may not end up benefiting their AIOps needs. To avoid inefficient piecemeal adoption lifecycle, enterprises should start with a top-down assessment of all the applications, systems, and processes to where AIOps can contribute the most.
#2: Not enough data and poor quality
Not having enough data may lead even the most powerful AI tools with unpredicted errors. The AI/ML algorithms have always been data-hungry, and the more data one gives them, the more accurate the results are, the same is the case with AIOps tools and the algorithms involved.
Another data-related problem that AIOps encounter is the poor quality of the data. Once an adequate amount of data is collected, the next step is always to assess its quality. The organization should avoid giving noisy data, inconsistent or insufficient frequencies, and inconsistent naming across applications or centers, etc. Organizations should develop standard procedures and also identifying the types of data that are most valuable for their specific priorities.
#3: The Meaning of data
Data that lacks semantic consistencies are less valuable for both operators and AIOps. Organizations that collect an abundant amount of high-quality data without the right context is almost useless. Teams should emphasize the importance of linking data so that relationships are recorded and are easily identifiable.
Using AIOps when it comes to cybersecurity means analyzing data related to threats to the extent that the exact nature of the threat can be identified with suggestions on how to contain it. While AI promises complete automation, AIOps applications will still need humans. However, the knowledge about AIOps and the various cybersecurity threats should not only be familiar to the company’s security team, but also other departments. If an organization is using AIOps for cybersecurity, opening up a platform for AIOps will improve communication between various departments, which will in turn, enhance the organization’s cybersecurity.