Overcoming SOC Challenges in Multi-Cloud and Hybrid Cloud Scenarios

Multi-cloud and hybrid cloud architectures have become increasingly prevalent among enterprises. This approach involves distributing infrastructure across multiple cloud service providers, such as Azure and AWS, to enhance redundancy and mitigate the risk of downtime. While leveraging the cloud offers numerous benefits including enhanced agility and collaboration, organizations face the critical challenge of ensuring visibility and control over their data’s whereabouts.

SOC Hurdles in the Cloud

Security Operations Centers (SOCs) face multifaceted challenges, particularly concerning the analysis of data from various sources, compounded by the complexity of multi-cloud environments. Integrating data from multiple cloud providers, alongside network traffic logs, endpoint telemetry, and user activity logs, presents a formidable task. The dispersed nature of data across different cloud platforms adds another layer of complexity, making it challenging for SOC analysts to gain a cohesive view of potential security and insider threats. 

Moreover, the dynamic and scalable nature of multi-cloud environments exacerbates the volume and velocity of data, overwhelming traditional security tools and hindering timely threat detection. To overcome these obstacles, SOCs require advanced analytics solutions capable of aggregating and correlating data from diverse sources across multi-cloud environments. These solutions should automate the analysis of anomalous behavior, prioritize high-risk threat alerts, and facilitate rapid response and remediation actions, ensuring robust security posture across the entire cloud infrastructure. Many organizations still have on-premises or private cloud infrastructures, creating a complex hybrid multi-cloud environment, which further compounds the challenges faced by security operation centers. 

The adoption of hybrid cloud and multi-cloud strategies introduces a range of security threats, with 39% of breaches targeting cloud environments. Alarmingly, 80% of companies experienced at least one cloud security incident last year. One significant issue stems from difficulties in identifying users, monitoring their access to applications and resources, and ensuring appropriate access permissions.  While the cloud has many advantages and supports greater agility and collaboration, organizations are well aware they need better visibility into and more consistent control over where their data is going. 

Managing security in multi-cloud environments presents formidable challenges. The diverse controls, authorizations, and logging mechanisms employed by different cloud providers make it difficult for security teams to gain a unified view of their data. Additionally, the disparity in cloud IaaS (Infrastructure as a Service) vendor expertise across security operations teams, coupled with the complexity of managing various cloud applications and resources, increases the risk of exposure to threats.  There’s different users that have access across clouds, with different privileges and rights to different resources and it increases the complexity around being able to track who’s doing what in which cloud environments and where the actual data that you have to monitor and protect resides.

Cloud architectures are increasing in complexity which introduces new challenges for security operations

Ensuring Compliance in Multi-Cloud Environments

Compliance with regulations such as GDPR adds another layer of complexity to multi-cloud architectures. Organizations must navigate data residency requirements and privacy regulations while maintaining effective threat detection capabilities. However, the fragmented nature of data across multiple cloud providers complicates monitoring efforts and increases storage costs. Developing multi-cloud architectures entails numerous considerations, including balancing security observability with data privacy concerns. Effectively monitoring for threats becomes challenging when access to all data is limited. In scenarios where data cannot be relocated, the ability to search data where it resides, and employing data masking becomes essential to extract metadata for security analysis. Gurucul offers built-in federated search and robust data masking solutions to address these needs.

Shortfalls of Traditional SIEMs

In terms of cloud solutions, most traditional SIEMS are based on on-premises architectures, and the attempt to lift and shift these architectures into the cloud hasn’t worked out so well. We’re seeing that on-premises solutions that try to operate in the cloud tend to have blind spots and don’t necessarily pull in all the data required for being able to monitor cloud environments properly. They have trouble with data, migration and scaling. The challenge of traditional SIEMs is they don’t perform in the cloud too well either. There is a lack of feature parity with their cloud-native counterparts when moved to the cloud which can result in lack of functionality leading to gaps in visibility and poor detection performance.. Unless the data for traditional SIEMs comes from one central location, you are not able to analyze and identify threats effectively.

Mitigating Advanced Attacks in Multi-Cloud Environments

Multi-cloud environments are vulnerable to a wide range of advanced attacks, like cross-cloud platform campaigns and cloud misconfiguration attacks. Gurucul’s sophisticated security analytics platform REVEAL, enables organizations to detect and respond to these threats effectively, mitigating the risk of data breaches and unauthorized access. These types of advanced attacks are layered in their approach and can be hard for any security solution to find, but Gurucul not only finds them, it can predict them as well.

Targeted Attacks Required Advanced Analytics

Addressing Security Gaps with Gurucul’s Next-Gen SIEM

Traditional SIEM solutions struggle to adapt to the dynamic nature of cloud environments, often resulting in an influx of false positive alerts. Gurucul’s Next-Gen SIEM offers advanced features tailored to address the unique challenges of multi-cloud and hybrid cloud security. By leveraging data science and machine learning, Gurucul can uncover hidden threats across disparate cloud environments and provide real-time insights into security risks. Gurucul’s Next-Gen SIEM works in any cloud environment, can pull data from anywhere and it can even store data in a cost-effective data lake of your choice. It allows for public cloud integration and supports customer private clouds. It is able to support hybrid cloud, multi-cloud and on-premises infrastructures and unifies security operations

With advanced artificial intelligence (AI) and machine learning (ML) behavioral analytics Gurucul can reveal threats that are hidden in the layers. Gurucul’s dynamic ML-powered intelligent data fabric automates data ingestion and interprets, monitors, enriches, reduces and routes data from any source, format or IT estate, including non-security related data for full visibility and coverage. Gurucul can actually look at data from multi-cloud environments and normalize that against a set of analytics and combine machine learning models together to be able to determine a true threat, delivering radical clarity into your IT stack.

Gurucul has the ability to do data mapping and data normalization through our data pipelines and the ability to monitor and understand the source of that data and where it’s coming from.

10 Questions to Ask SIEM Providers

Optimizing Cost and Efficiency with Gurucul

Gurucul’s platform streamlines security operations in multi-cloud environments, reducing costs associated with inefficient data management practices, data duplication and the cost associated with preparing raw data for analysis. By offering federated cloud search capabilities, data optimization and data masking controls, Gurucul empowers organizations to maintain compliance while minimizing operational overhead. It can become very challenging when you have localized SIEMs or have to log onto multiple data centers, rehydrate cold storage, manually search, and want to avoid data duplication. Other solutions might offer to send data to AWS or Azure, but this will incur data transfer fees. That is usually an unexpected cost and you may not know what data or how much you need. Some providers also charge on the number of searches or queries of the data. 

Gurucul provides universal federated search capabilities at no cost. It provides a centralized query interface and leverages obfuscated metadata for security purposes, while also abstracting and normalizing data from various sources. Gurucul understands localization rules regarding data and can filter out non-obfuscated data that doesn’t comply with masking requirements. It operates within internal and external regulations, aligning with national and localized data compliance standards.

Gurucul’s implementation takes only days to roll out and is user-friendly with a library of over 3,000 pre-configured detection and ML models. Its intuitive GUI facilitates automated case management and custom ML model development without the need for data science expertise. Real-time threat monitoring enables prompt resolution of potential issues.  The platform is open, flexible and 100% cloud-native. 

Conclusion

As enterprises increasingly adopt multi-cloud and hybrid cloud architectures, the demand for robust security analytics, such as the Gurucul REVEAL platform, escalates. Gurucul’s comprehensive suite offers vital tools for addressing the unique challenges faced by Security Operations Centers (SOCs) in these dynamic environments. With Gurucul, organizations gain unparalleled visibility into multi-cloud environments, advanced threat detection capabilities, and simplified compliance management. By empowering SOC teams to navigate the complexities of multi-cloud security confidently, Gurucul ensures the safeguarding of critical assets and data amidst evolving threat landscapes.