6 Ways AIOps Optimizes Cloud Security

By Veritis

6 Ways AIOps Optimizes Cloud Security

Now more than ever, IT businesses are prioritizing a ‘cloud first’ approach to transform with agility at scale. Simply put, the pandemic has served as a force multiplier for organization’s spending on the cloud.

According to Gartner, the global end-user spending on public cloud services is estimated to reach USD 332.3 billion in 2021, up from USD 270 billion in 2020.

While the cloud offers the much-needed agility and scalability to navigate the crises, security and compliance risks remain the biggest inhibitor to cloud adoption. Buttressing this is a 2019 Cloud Security Report that found 93% of organizations are moderate to extremely concerned about cloud security vulnerabilities.

Cloud has become so complex, that it’s often an uphill task for companies to mold the cloud as per the evolving security demands. Fortunately, AIOps, or Artificial Intelligence (AI) for IT Operations (Ops), comes to the rescue.

Also Read: How Artificial Intelligence is Transforming DevOps?

How AIOps Bolsters Cloud Security?

AIOps is the application of artificial intelligence on IT operations data sources to automatically solve IT operational challenges in the distributed cloud environments. It has a myriad of use cases in the cloud to enhance cloud operations and security.

Six Ways Businesses can Leverage AIOps for more Effective Cloud Security

Here are the six ways businesses can leverage AIOps for more effective cloud security:

1) Threat Intelligence

By ingesting data from any part of the cloud environment, AIOps analyzes, prioritizes, and provides valuable insights into security incidents as they emerge in near real-time. These insights can be analyzed using the Machine Learning and automation capabilities of AIOps to develop predictability models.

By correlating all these relevant, real-time insights, the cloud security team can discover the source of an attack, indicators of compromise, and behavioral trends pertaining to cloud account use.

Moreover, with threat intelligence derived from AIOps and as well as from external providers like Veritis, your security team can take swift and more informed security decisions specific to their cloud environment.

2) Incident Response and Management

AIOps enables cloud security teams to respond rapidly by providing all the relevant data regarding an incident, which typically includes incident nature, severity, and impacted assets. ML and AI capabilities of AIOps can help the security team develop and deploy robust intelligence detection and alerting strategies.

3) Behavioral Analysis

Behavior analysis is a promising use case of AIOps for cloud security. Analyzing endpoint and network behavior patterns helps security teams to swiftly identify the subtle indicators of compromise. This enables them to detect attacks and respond quickly to prevent breaches from occurring or restrict damages by confining attacks in earlier stages.

Also Read: How Cloud Automation is Driving DevOps Efficiency?

4) Fraud Detection

Typically, fraud detection requires deep-diving into an avalanche of raw data logs, a process that is very complex and resource-intensive. It escalates the need for extensive text mining, social network analysis, database searches, and anomaly detection to pinpoint fraud. Cloud AIOps can alleviate this tedious task and allow security teams to remediate the fraud quickly.

5) Malware Detection

Embedding machine learning and AI capabilities of AIOps into cloud security can help security teams detect any abnormalities or threats to routine management tasks. The teams can leverage AIOps to monitor performance activity against external threat intelligence feeds, including vital information on malware, rogue code, ransomware, and suspicious internet protocol addresses across their cloud environments.

6) Data Classification and Monitoring

AIOps analyses all structured and unstructured data stored in all cloud environments, including public, private, or hybrid cloud, and classify it into various categories based on content type, pattern, and other metadata. It helps organizations to monitor complex streaming data in real-time.

Final words:

A well-executed AIOps strategy can help make a cloud-first journey be secure by design and deliver high-performance business outcomes. However, shortage of cloud and security talent, data migration costs, and complexity of aligning AIOps with business outcomes can be significant roadblocks to cloud AIOps success.

It is vital to strategize and deliberate before laying the cornerstone of AIOps. Harness your experience and employ the expertise of proven AI players to elicit the optimal strategy for your company.

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