SOC 2.0 Guide for Better Cloud Security Visibility and Forensics

Working on the SOC team can be taxing; the majority of teams face information overload—overwhelmed by the number of alerts and false positives to track. A study conducted by the Cloud Security Alliance revealed that half of the companies surveyed had six or more tools generating separate security alerts, each of which required review.

This practical and easy-to-read article, gives an overview of the challenges that next-gen SOC 2.0 teams and security analysts face today and present tips and recommendations to:

• Increase visibility, and automate security monitoring and forensics
• Improve mean time to detect (MTTD) and mean time to respond (MTTR) to threats and incidents.
• Enrich alerts and log analysis with contextual information for faster and more accurate decision-making
• Automate response to uncomplicated incidents reducing false positives
• Create more advanced SOC Orchestration

Get the tools to address the major challenges of SOC 2.0 teams; increasing visibility and forensics, while improving incident response and creating more advanced SOC orchestration in this article. Read this article and get insights on how to better address today’s security challenges while increasing cloud security visibility and optimize forensics.

Table of contents

Abstract
Roles and Responsibilities Within the SOC Team
The Challenges of a Multi-Cloud Environment
Alert Fatigue and Other Major Obstacles
Machine Learning as an Action Accelerator
Optimizing Performance Through Automation
Weeding Out the Signal from the Noise
Cloud Security Intelligence
Automated Security Monitoring and Forensics
Check Point Cloud Solutions for Next-Gen SOC
Conclusion

Abstract

This article gives an overview of the challenges that the Security Operation Center (SOC) Teams currently face to effectively detect and address cybersecurity threats while securing data and company assets in a multi-cloud environment.

Visibility and incident investigation in the cloud is a growing challenge. Cloud forensics and incident investigation becomes costly and ineffective when there is too much security data to analyze; making it sometimes impossible to elevate true security alerts from the irrelevant ones.

As the volume and velocity of threats increase the Security Operation Center (SOC) team finds themselves still battling with multiple dashboards and false-positive alert fatigue. Even prioritizing, many enterprises will struggle to effectively triage, investigate, and respond to the endless threats faced on a regular basis.

This article describes not only the pain-points that SOC teams face analyzing a vast amount of data, logs and forensics in the cloud but also presents tips and recommendations to efficiently scale and automate incident response, threat hunters and analysts have better visibility and augmented insights into security threats and anomalies, helping the team to act fast and more efficiently to cyber threats.

Solutions like CloudGuard Log.ic, delivers advanced cloud intelligence and simplified visualization for faster and more efficient incident response. It provides more effective incident detection and auto-remediation, augmented through AI and ML, across multicloud environments e.g. AWS, MS Azure & GCP.

Check Point CloudGuard security intelligence and automation enable enterprises with:

• Simplified and contextualized visibility of complex multi-cloud architectures.
• Advanced analytics and forensics powered by ML and AI.
• Enriches traffic logs with contextualized information and high-quality actionable forensics.
• Seamless integration with SIEMs for more actionable insights.
• Relevant intrusion alerts, real-time monitoring, and automated detection and remediation.

This article describes the challenges of Next Generation SOC (Security Operation Center) 2.0 teams in the multi-cloud environment. Incident investigation in the cloud is a growing concern. When there are too many elements to investigate, this can lead to inefficiency and increased spending. Not only do SOC 2.0 teams often find themselves overwhelmed with false-positive alert fatigue; they must also contend with the associated human capital expenditure scale issue.

In this article, we discuss how to scale and automate incident response in order to allow the team to work in an agile manner and thus increase efficiency. This article also presents practical tips and tools that can assist next-gen SOC teams in improving cloud security visibility and threat investigation and forensics.

Roles and Responsibilities Within the SOC Team

A security operations center (SOC) is a centralized unit responsible for ongoing monitoring and analysis of an organization’s security status. SOC team members deal with intrusions, malicious code infection, data exfiltration, DoS, and other security incidents affecting computer systems and networks.

Each SOC team consists of staff with a number of different roles. While the names of these roles may differ from one organization to another, their responsibilities are similar. These include:

• Security Analyst: When it comes to security incidents, these professionals are usually on the front line, detecting, investigating, and—most importantly—responding to threats in a timely manner. In some cases, security analysts may be involved in implementing security measures dictated by management as well. Occasionally, they may also take part in the recovery phase, helping to get impacted systems safely back into production.
• Security Engineer: Ensuring tools are up to date, advising about new tools, and providing system support are the security engineer’s main duties. Often, these engineers also specialize in security information and event management (SIEM) platforms. In addition, they participate in the process of building security architecture, which frequently involves working with the system design authority (SDA), system security design authority (SSDA), and software production engineering teams (SPE). The security engineer must also define the requirements and procedures to ensure all parties involved have the support they need.
• Security Manager: Responsible for the overall supervision of operations related to cybersecurity violations, the primary focus of the security manager is to manage team members and coordinate with the security engineers. They also advise softwaredevelopers and software architect teams about which new security projects to take on. They are in charge of all SOC team members within the organization.
• Chief Information Security Officer (CISO): This individual is responsible for the development, implementation, and management of the organization’s information security vision, strategy, and program. The CISO is directly involved in developing, implementing, and maintaining security processes throughout the organization to reduce risk, respond to incidents, and limit exposure to liability in all areas. The CISO is considered senior management and generally reports to the CEO. Beyond the technical skills required, the CISO must also be capable of communicating complex problems to upper management, which may have limited technical knowledge.
• Additional Roles: Occasionally, organizations have additional SOC roles and personas. These might include, for example, positions such as director of incident response, who is charged with supervising and prioritizing actions during the incident handling phase.

No matter an organization’s size, establishing an effective, reliable SOC team is critical in order to ensure it is capable of handling incidents. Of course, people are the most overlooked security risk, and it is extremely important to be aware of the fact that human error is inevitable. The team needs to have written policies and procedures to serve as a protocol for all incident handling processes.

The Challenges of a Multi-Cloud Environment

The main challenge for SOC teams working in the multi-cloud environment is maintaining consistency among the many vendors. This task often turns out to be extremely difficult, as there are often disparities among vendors in how they present their processes and services. Often using different terms to describe their offerings, the overall picture can become so obscured to the point that, in extreme cases, a particular private cloud provider may, in fact, be a virtual network of another provider.

What then should a SOC team in a multi-cloud environment look like? The answer is not so straightforward. First, research is required in order to pinpoint the challenges that may arise in the multi-cloud environment so that any issues can be addressed prior to developing a security strategy.

The purpose of this research phase is first to understand where the cloud architecture is performing well and where it is lacking. Most likely, these potential failure points will appear where technologies converge and processes are not automated. The resulting research reports should also delve into the differences between the cloud environments and should include reports created by external entities with direct experience in implementing multiple clouds as well.

Dividing the SOC team into several autonomous units, each working on their own part of the cloud infrastructure, can also be beneficial. For instance, finding engineers who are experts in one particular platform and then training them on another can be an effective means for gathering the specialist knowledge and skills your SOC team needs when implementing multiple clouds. It is far easier to find a security analyst or security engineer, for example, with experience in AWS or GCP than to find someone someone with expertise in all three major cloud platforms (AWS, GCP, and Azure).

Creating a multi-cloud environment is no easy feat. This is further complicated when considering the ephemeral nature of the cloud, as well as the compliance and security implications when dealing with a multi-cloud environment. As multi-cloud environments are still relatively new, the rules for their implementation are also constantly being improved. It is therefore important to understand the specificity of a particular cloud environment from the very beginning, including what qualifies as a baseline for security and compliance, so you can better identify threats and anomalies. This can be facilitated by using one combined security solution instead of adapting multiple products from different security providers, thus avoiding the “patchwork” approach.

Alert Fatigue and Other Major Obstacles

Working on the SOC team can be taxing; the majority of teams face information overload—overwhelmed by the number of alerts to track. This results in what is known as “alert fatigue.” A study conducted by the Cloud Security Alliance revealed that half of the companies surveyed had six or more tools generating separate security alerts, each of which required review.

Many SOC team experts claim that these alerts do not contain data that requires investigation. In addition, because so many of these alerts are false positives, a large number of security analysts often dismiss them. Another issue with alert fatigue is the increased workload this creates, which can lead to job burnout among SOC team members.

A number of other factors can negatively impact the effectiveness of SOC teams. These include:

• Poor visibility of the cloud infrastructure and configurations
• Inability to prioritize threats
• Inability to capture action-oriented intelligence
• Inability to recruit and retain expert personnel
• Lack of resources
• Complexity and chaos in the SOC

In order to minimize and overcome these obstacles within the cloud environment, the next- generation security and operations center, or SOC 2.0, should, at a minimum, work to:

• Prevent alert fatigue: The number of tools that simultaneously generate alerts should be reduced as much as possible.
• Ensure visibility into the network and IT infrastructure: Greater insight into network and IT architecture is critical as this enables a better understanding of the problems and more efficient handling incidents.
• Retain expert personnel: SOC team and IT management should listen to team members’ feedback in order to prevent burnout and employee turnover.
• Prioritize threats: The SOC team should have clear priorities for actions and not waste time handling low-priority security incidents.

Machine Learning as an Action Accelerator

A discipline of the science of artificial intelligence (AI), machine learning’s main goal is the practical application of AI achievements to create an automatic system that can be improved with accumulated expertise (big data). SOC teams can take advantage of the many benefits of machine learning from the earliest stages.

The two primary methods within the machine learning domain are supervised and unsupervised learning. Supervised learning is the process in which the data set provided to the learning machine also carries the expected response. This enables the system to make intelligent decisions in the future, such as, in the case of SOC, identifying malicious activities.

Unsupervised learning occurs when the data set provided to the learning machine does not provide any answers, only a set of data. Here, the system uses relevant algorithms to infer what is typical and notifies when something changes or diverges from the norm, such as new types of incidents registered by SOC.

What are the benefits of using machine learning in the context of the next-gen cloud SOC 2.0? The main advantage is that it can be of great assistance to analysts on the front line of incident handling. Machine learning is crucial for classifying threats, preventing known malicious behaviors, and reducing false positives. It allows you to concentrate more on investigations and incident response.

Another benefit is the ability to automatically examine vast amounts of data in order to identify anomalies that may indicate the environment is being compromised. This enables the establishment of procedures for dealing with a given threat, which reduces the time investment of the security analyst, thus improving mean time to detect (MTTD) and mean time to respond (MTTR) quotients.

While this new technology gives SOC teams the edge in a number of areas, including behavioral and predictive analysis, it is not meant to replace security analysts. Rather, it is a tool that enables faster and more accurate decision-making.

Optimizing Performance Through Automation

The SOC team’s most basic task is to resolve problems related to well-known threats that could be affecting the organization (e.g., worms, viruses, or other malware) and to foresee security gaps. While the security operations center knows how to handle such threats, having to deal with so many minor threats can be time consuming and lead to inefficiencies when there are more complicated issues to deal with, such as zero-day attacks, which are becoming more widespread.

Automation and orchestration empower SOC teams by significantly improving efficiency in handling threats. All simple tasks should be automated in SOC 2.0 in order to allow the team to focus on more challenging undertakings related to targeted attacks.

The most challenging job for SOC teams is of course dealing with advanced targeted attacks. Such attacks are often spread out over a longer period and often include more than one vulnerability. Because each such attack is unique, automation may not be sufficient for deflecting such attacks, however, it can help indirectly by helping analysts solve other, simpler problems, thus freeing up their time for more complex issues.

Weeding Out the Signal from the Noise

Every SOC must battle an increasing number of false positives. Decreasing the number of false alarms and handling them effectively has therefore become a priority.

In order to achieve this, the first step is to create appropriate filtration rules. In order to reduce the chances of the filtration rules producing false positives, the largest possible group of security specialists should participate in this analysis. These previously adopted rules should then be evaluated in the automation environment prior to their approval. At this stage, it is critical to examine whether the rules generate false positives and to ensure real incidents aren’t missed. If necessary, further iterations of analyzing and evaluating filtration rules should be carried out.

These measures can significantly reduce the time spent by the SOC team handling false alarms. This will in turn ensure faster and more efficient incident response by weeding out the signal from the noise.

Cloud Security Intelligence

As companies migrate and expand their applications and services to multi-cloud environments at an unprecedented rate, despite the many benefits of multi-cloud, this also creates security problems. This is due to the fact that local deployments differ from those offered by cloud service providers such as Amazon Web Services or Microsoft Azure.

The accumulation and interpretation of data collected during daily cloud operations prior to an incident play a critical role. This has a direct impact on security, as any such information may be relevant for subsequent investigations. Organizations migrating to the cloud therefore must understand how log collecting and data analysis will change in the new environment.

Log analysis in the cloud is dynamic when compared to analysis in the local environment. This is due to the ephemeral nature of the cloud and cloud resources frequently changing, making tracking changes more challenging.

A contextual intelligence approach may be useful, as this can help speed up detection and the investigation phase. This will allow you to focus on prioritized threats and anomalies, with better insights into the global and regional threat landscape as well as of the specific business case.

Automated Security Monitoring and Forensics

Every mature organization should be able to perform continuous automatic monitoring of the environment for safety-related events. Establishing clear patterns for forensics and threat response and team-wide collaboration are essential. The SANS Institute’s next-gen SIEM research guide confirms the above statement. Meeting these conditions can:

• Ensure the ability to track and handle security incidents
• Provide a clear assessment of SOC team members’ efficiency
• Allow for automated responses in cases of uncomplicated incidents
• Create more advanced SOC orchestration

The next must-have characteristic for mature organizations is the implementation of advanced threat detection mechanisms using machine learning and artificial intelligence to help discover unusual behavior. Meeting these requirements enables the identification of previously unclear and unexplored threats. It also helps the SOC team improve its analytical and problem-solving skills.

Check Point Cloud Solutions for Next-Gen SOC

CloudGuard provides Cloud Intelligence and Threat Hunting in a powerful cloud visibility and remediation tool that delivers advanced cloud security intelligence and forensics across multi- cloud environments.

It enriches ingested logs, data, and alerts with contextual visibility and insights. The logs and data collected become more readable and actionable, powered by ML and AI, augmented by advanced security intelligence. It helps SOC and other security to better pinpoint underlying issues, improving forensics and significantly reducing time to detect and address security threats and anomalies. In addition, CloudGuard allows you to examine each data flow and audit trail in today’s elastic cloud environments. It also provides insights into cloud data and actions, thus facilitating the investigation process for the security operations center.

CloudGuard is equipped with automated threat detection and traffic analysis (NTA), identifying and investigating security breaches as well as unauthorized activities. It also offers contextual and graphical visualization of network traffic and user activity. Advanced object-mapping algorithms use cloud inventory and configuration information as well as real-time monitoring data from a number of different sources. This data can be pulled from sources such as VPC Flow Logs, CloudTrail, Amazon GuardDuty, or AWS Inspector.

Another beneficial feature is the ability to create your own response to any type of network alert or audit trail through CloudBots CloudGuard technology. In order to fully understand the cloud infrastructure, CloudGuard analizes traffic and enriches native security services and functions offered by AWS and Azure Security Center.CloudGuard applies the most appropriate methods for incident detection using built-in rules. In cloud environments, such as serverless applications, unauthorized or malicious activities are detected using AI and anomaly detection algorithms. Based on previously defined rules, CloudGuard generates policy violation and intrusion detection alerts in real time.

Another important benefit of the platform is the visualization of network traffic. CloudGuard’s graphical exploration tool, “Explorer,” enables you to investigate network activity and traffic in your cloud environment and beyond. An extensive set of predefined queries are available to choose from. Queries can also be custom created using CloudGuard’s query language.

Conclusion

Today’s SOC teams face numerous challenges, with visibility and incident investigation in the cloud, in particular, a growing challenge. While security data overload can make cloud forensics and incident investigation costly and inefficient, weeding out false positives from true security alerts can lead to alert fatigue. Moreover, SOC teams must keep up with multiple dashboards.

Despite their best efforts to prioritize tasks, many companies still struggle to triage alerts, investigate, and respond to the ongoing barrage of threats. Tools to aid in security intelligence and forensics across multi-cloud environments can play a critical role in helping SOC teams navigate these complexities and streamline network security operations to reduce MTTD.

Source: Check Point