Cloud Security Analytics has emerged as a critical solution to combat the ever-evolving landscape of cyber threats in the cloud. In this article, Doshared.com delve into the realm of cloud security analytics and explore six compelling use cases that demonstrate its effectiveness in identifying vulnerabilities, detecting anomalies, and fortifying the overall security posture of organizations operating in the cloud.
- 1 Top 6 cloud security analytics use cases
What is Cloud Data Analytics?
This refers to the process of analyzing data using cloud-based tools and services. This can apply to data that is transmitted to the cloud for analysis as well as data that is kept there. Data from sensors, mobile devices, and on-premises applications may be included.
Analytical algorithms are used to analyze data in the cloud using cloud analytics. It mixes scalable cloud computing with potent analytics software in order to find patterns in data and derive new insights. Over the past two years or so, a growing number of firms have turned to cloud-based data analysis.
The data that is being examined is frequently created in the cloud or even on the internal systems of an organization before being transmitted to the cloud for analysis. This can include images from surveillance cameras, posts on social media, logs, and more. It is reasonable that more and more tools will be created to evaluate data as its role in modern computing grows.
Another outside service might examine cloud data. This could involve a service provider or the internal analytics infrastructure of an organization.
4 Types of Cloud Analytics:
- Analytics in the public cloud:
The public cloud is the best option for businesses with limited resources and a pressing need to respond swiftly. It can handle large data sets, is economical, and makes use of cutting-edge technology.
- Analytics in the private cloud:
This makes data analysis for your company tighter and even more secure.
- Hybrid cloud:
Systems both on-premises and in the cloud may be used to store data. These data can be examined using cloud-based analysis tools. A mix of public and private infrastructure is used to supply services in an on-demand cloud analytics solution.
Information is secured, and secure contact with end users is made possible by using private hardware and software.
- Edge cloud:
The edge cloud is where data is being examined as it comes in, rather than at a later time. It could consist of a sensor, a server system, a handheld device, or other mobile devices.
Top 6 cloud security analytics use cases
Threat intelligence analysis
Threat intelligence data, for instance, provides insight into attacker origins, breach signs, and behavioral trends relating to the use of cloud accounts and assaults against different kinds of cloud services. Machine learning engines in the cloud can be used to aggregate and analyze threat intelligence inputs on a large scale.
Additionally, feeds for probability or predictability models can be processed. This security analytics use case would be a welcome addition to a cloud security program given the rise in cloud assaults, notably account hijacking. Two examples of vendor products are Amazon Guard Duty and Microsoft’s Advanced Threat Analytics. Security teams cannot, however, customize them.
Massive amounts of log data and other events are produced. To fight against these threats, security teams must be able to swiftly discern patterns, identify specific signs, and spot events occurring in cloud settings. To create more sophisticated detection and alerting strategies, the cloud offers vast event data processing capabilities. For instance, Microsoft’s Azure Sentinel is a cloud-based, AI-powered SIEM service.
Endpoint and network behavior modeling
Since the majority of endpoints are not yet in the cloud, cloud-specific endpoint behavior modeling is likely to gain popularity in the future. However, network flow modeling is a security analytics use case that should be taken into account for in-cloud AI processing. Massive amounts of communication are exchanged between systems and the control plane of the cloud provider, and these volumes should be established as “normal” baselines for monitoring. Cloud-based capabilities for detection and response are also used by several endpoint security technology companies.
Fraud detection calls on financial services companies and insurers to use a staggering amount of inputs, data types, and intensive processing. Predictive models are combined at scale with text mining, database searches, social network analysis, and anomaly detection. Engines for cloud-based security analytics can be of great assistance here. They might also include the fraudulent use of cloud services, such as phishing attacks using Microsoft 365 that originate from compromised accounts.
Ransomware and other malware variants can now be found using cloud-native event processing of data and file properties, especially those with unknown signatures. Leading endpoint detection and response companies, like Carbon Black and CrowdStrike, leverage cloud data processing in their services for organizations interested in implementing this security analytics use case. However, there is also a case to be made for internal sandbox processing engines that leverage cloud data analytics.
Data classification and monitoring
All data transferred to and created in cloud environments can be processed by cloud analysis engines, which can then classify and tag the data in accordance with specified policies and check for access. The analysis is based on recognized patterns and content types. Although there is still a long way to go until this kind of service is more widely available and versatile, one vendor choice for data analysis and monitoring is Amazon Macie.
Cloud security analytics offers a powerful arsenal of tools and techniques to protect organizations’ digital assets in the cloud. In this article, we explored six compelling use cases that demonstrate the effectiveness of cloud security analytics in identifying threats, detecting anomalies, and enhancing overall security posture. By leveraging these use cases, businesses can proactively mitigate risks, strengthen their defense against cyber threats, and ensure the integrity and confidentiality of their cloud environments.
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