Cloud Security Posture Management with AIOps.

CSPM(Cloud security posture management) is a critical component of cloud data security because it scours cloud environments and alerts employees to cloud service configuration vulnerabilities and compliance issues, the majority of which are caused by human mistakes. Gartner created a new category of solutions in its Innovation Insight for Cloud Security Posture Management study, dubbed Cloud Security Posture Management that automates security and compliance assurance and addresses the requirement for adequate management over cloud infrastructure settings (CSPM). As a result, businesses are beginning to recognize that this is a "must-have" cloud security product.
What is AIOPS, and how does it work? 
AIOps is an acronym for artificial intelligence and operations (Ops). More precisely, it is the fusion of AI and ITOps, referring to multi-layer technology platforms that automatically use machine learning, analytics, and data science to discover and handle IT operational problems. Gartner invented the term AIOps in 2016 due to the digital transformation transition away from centralized IT toward anywhere operations with workloads in the cloud and on-premises globally. As the rate of invention grew, the sophistication of technology rose as well. As a result, it imposed an enormous burden on IT operations, which were now tasked with monitoring and supporting a diverse array of new systems and devices.

How does AIOps work?
AIOps is most effective when implemented independently to collect and analyze data from all available IT monitoring sources, creating a centralized engagement system. It employs a substantially identical technique to that of the human cognitive function. 
The following are the five critical algorithms at work:
Data selection: Combining the massive quantity of accessible IT data, assessing it, and finding key data items, AIOps must detect the critical 'needles' buried in terabyte-sized data 'haystacks' using predefined selection prioritization criteria.
Pattern discovery: AIOps examines pertinent data, identifying correlations between data items and putting them together for further analysis.
Inference: In-depth analysis enables AIOps systems to identify the underlying causes of issues, events, and trends, resulting in actionable insights.
Collaboration: Additionally, AIOps must operate as a collaboration platform, informing the appropriate teams and people, presenting them with pertinent information, and promoting successful communication despite operator location.
Automation: Finally, AIOps is meant to automatically detect and resolve errors, dramatically speeding up and improving the accuracy of IT operations.
How is AIOPS used for cloud security management?
AIOps uses artificial intelligence to data sources related to IT operations to automatically solve IT operational difficulties in dispersed cloud settings. It has many applications in the cloud, including enhancing cloud operations and security.

Businesses may employ AIOps to improve their cloud security effectiveness:
1) Threat Intelligence
AIOps analyzes, prioritizes, and gives important insights into security issues as they develop in near real-time by ingesting data from any component of the cloud environment. We can evaluate these observations to construct predictive models utilizing AIOps' Machine Learning and automation capabilities. Additionally, with threat data provided by AIOps, your security team can make more educated and timely cloud security choices.
2) Incident Response and Management
AIOps helps cloud security teams to react quickly by supplying all pertinent information about an event, which often includes the problem's type, severity, and affected assets. In addition, AIOps' machine learning and artificial intelligence skills may assist the security team in developing and deploying comprehensive intelligence detection and alerting techniques.
3) Behavioral Analysis
Behavior analysis is an exciting use of AIOps in the context of cloud security. By analyzing endpoint and network activity patterns, security teams may spot minor intrusion symptoms more quickly. In addition, it helps them identify and react swiftly to assaults, preventing breaches or limiting damage by containing attacks in their early phases.
4) Detection of Fraud
It requires substantial text mining, ops data analysis, database searches, and anomaly detection to identify fraud. Cloud AIOps can ease this time-consuming job and enable security teams to rectify the fraud immediately.
5) Detection of Malware
By incorporating AIOps' machine learning and artificial intelligence capabilities into cloud security, security teams can identify irregularities or risks to routine administrative activities. In addition, the teams may use AIOps to evaluate performance against external threat intelligence feeds, which provide critical information on malware, rogue code, ransomware, and suspicious internet protocol addresses in their cloud environments.
Conclusion
Lack of cloud and security skills, the high cost of data transfer, and the difficulty of aligning AIOps with business results may be major impediments to cloud AIOps success. Therefore, before creating the groundwork for AIOps, it is critical to plan and deliberate. Utilize your skills and the expertise of established AI players to extract the ideal approach for your business. Please visit our AIOps page