Now more than ever, IT businesses prioritize 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, 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 crises, security and compliance risks remain the biggest inhibitors to cloud adoption. A 2019 Cloud Security Report, for example, found that 93% of organizations are moderate to extremely concerned about cloud security vulnerabilities.
The cloud has become so complex that it’s often an uphill task for companies to mold it according to evolving security demands. Fortunately, AIOps solutions, or Artificial Intelligence (AI) for IT Operations (Ops), come to the rescue.
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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 solve IT operational challenges in distributed cloud environments automatically. AI for IT operations plays a crucial role in leveraging machine learning and advanced analytics to improve IT performance, detect anomalies, and automate incident management. It has many use cases in the cloud to enhance cloud operations and 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 AIOps’ Machine Learning and automation capabilities 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 about cloud account use.
Moreover, with threat intelligence derived from AIOps and external providers like Veritis, your security team can make swifter 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 the incident’s nature, severity, and impacted assets. The AIOps ML and AI capabilities 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 very complex and resource-intensive process. This 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 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 classifies it into various categories based on content type, pattern, and other metadata. It helps organizations monitor complex streaming data in real-time.
Case Study: Strengthening Cloud Operations with AIOps in Automotive
A global automotive company partnered with Veritis to enhance its cloud environment using AIOps, aiming to boost performance, visibility, and security.
Challenge: The organization faced fragmented monitoring systems, slow incident resolution, and increasing security concerns within its expanding cloud infrastructure.
Solution: Veritis implemented an AIOps-driven framework that integrates AI and machine learning to enable proactive monitoring, real-time threat detection, and automated responses across the cloud ecosystem.
Results:
- Faster anomaly detection and incident response
- Improved cloud security posture through predictive insights
- Streamlined IT operations with reduced manual effort
This case illustrates how AIOps plays a crucial role in optimizing cloud environments, enhancing both operational efficiency and security resilience.
Read the Full Case Study: Transforming Automotive Operations with AIOps
Final words:
A well-executed AIOps strategy can help ensure that a cloud-first journey is secure by design and delivers high-performance business outcomes. However, a shortage of cloud and security talent, data migration costs, and the 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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