Intro

As organizations increasingly migrate to the cloud, the landscape of cybersecurity evolves, presenting new and complex challenges for security teams. The dynamic nature of cloud environments, coupled with the scale and sophistication of potential threats, demands a proactive and context-driven approach to threat detection. Traditional security measures often fall short, requiring security teams to adapt and develop strategies that can effectively identify, prioritize, and neutralize threats in the cloud. In this blog, we’ll review threat detection challenges in the cloud, and how Stream Security can help overcome these challenges.

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Challenges of Threat Detection in the Cloud

To handle threats in the cloud, security teams need to understand the entire context of the cloud to:

• Prioritize Threats: In the cloud environment, the sheer volume of potential threats makes it crucial for security teams to prioritize based on severity and potential impact. This involves understanding which threats pose the most significant risk to the organization, allowing teams to focus resources on the most critical vulnerabilities first.
  

• Triage to Understand Blast Radius: Triage involves quickly assessing an incident to determine its scope and potential damage. In the cloud, this includes understanding how widespread the threat could be across interconnected services and data, helping to limit the potential blast radius and contain the threat before it spreads.
  

• Investigation to Understand Exploitability: After identifying a threat, SOC teams must investigate to understand how the threat could be exploited. This involves analyzing the vulnerability, the attacker’s methods, and the cloud environment's unique aspects to determine how easily the threat could be weaponized and what the potential consequences could be.
  

• Pinpoint the Root Cause: Effective threat detection requires identifying the root cause of an incident to understand how the attacker gained a foothold. This step is critical in the cloud, where attackers may exploit specific cloud-native features or misconfigurations, leading to widespread damage if not promptly addressed.
  

• Mitigate/Remediate: Once the threat is fully understood, mitigation and remediation steps are taken to neutralize the threat and prevent future occurrences. In the cloud, this involves both immediate actions to stop the attack and long-term strategies, such as improving configurations, patching vulnerabilities, or changing policies, to enhance overall security posture.

Given the complex and interconnected nature of cloud environments, understanding the full context behind a potential threat is crucial for effective detection and response. This is where advanced tools come into play, offering the ability to not only identify and prioritize threats but also to deeply analyze and mitigate them. Stream Security's Cloud Detection and Response (CDR) solution exemplifies this approach, providing the necessary context and real-time insights that security teams need to handle cloud-based threats efficiently and effectively.

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About Stream Security CDR

Stream Security’s cloud threat detection and response (CDR) solution leverage its revolutionary CloudTwin technology, provides a precise and constantly updated model of the cloud environment by continuously tracking behavior and configuration changes out of cloud logs. With dynamic algorithms at work, every alteration triggers a thorough analysis that delivers an accurate and comprehensive impact assessment on each change, and by that provides actionable results to detect, triage, pinpoint and address threats in minutes.

Stream Security combines three cloud context dimensions:

• Network – what the network pattern looks like, and are there any anomalies

• Identity – what the identity access map looks like, and what is the actual usage

• Exposure – what is the risk associated with each resource, according to its susceptibilities.

To provide threat detection contextual information, Stream Security utilizes its built-in anomaly-detection engine to:

• Get breach indications on workloads and user activity against the MITRE ATT&CK® framework

• Generate automated attack timelines with reach security context to resolve alerts in minutes

• Detect malicious activities using behavioral analytics & machine learning algorithms

Stream Security's detection mechanism provides comprehensive monitoring and analysis across cloud environments, focusing on both workloads and identities. By leveraging a blend of indicator-based and behavioral analytics, our system accurately identifies potential threats and anomalies, ensuring robust protection against diverse security risks.

Let’s review a threat detection investigation from end-to-end using Stream Security with the following example.

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Investigation Flow: Understanding and Responding to Anomalous Network Traffic

1. Alert Received: Anomalous Network Traffic

The security team is alerted to unusual network activity, indicating potential suspicious behavior within the cloud environment.

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2. Identifying the Impacted EC2 Instance

Upon receiving the alert, the security team identifies that the anomalous traffic originates from a specific EC2 instance, pinpointing it as the affected asset requiring further investigation.

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3. Analyzing Trigger Signals

The alert was triggered by three key indicators (signals):

- The EC2 instance exhibited unusual connectivity patterns to a data store, such as a database or file storage service.

- The instance initiated outbound connections that deviated from its normal communication behavior.

- There was an unusually high volume of outbound traffic compared to the instance's typical activity.

4. Initiating Further Investigation

To streamline the investigation process, Stream automatically correlates all events related to an attack based on specific principles, such as user activity and privilege escalation during lateral movement. It then provides investigators with a clear, comprehensive view of the incident via a graph – the Investigator:

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5. Reviewing the Timeline of Actions

Examination of the instance's activity timeline reveals that the attacker executed the AssociateIamInstanceProfile action, resulting in two high-severity configuration changes:

- Resource with over permissive DynamoDB GetItem permissions

- Resource has access to get data from Crown Jewel DynamoDB  

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6. Assessing Security Group Modifications

The attacker proceeded to perform the AuthorizeSecurityGroupIngress action on a Security Group, which effectively granted SSH access (port 22) to the compromised EC2 instance, increasing the risk of unauthorized access.

7. Failed Access Attempt on S3 Bucket

The attacker attempted the HeadObject action on an S3 bucket but encountered an 'AccessDenied' error, indicating restricted access to certain data.

8. Multiple GetItem Actions on DynamoDB

Despite the failed attempt on the S3 bucket, the attacker continued by performing multiple GetItem actions on a DynamoDB table, aiming to extract valuable information.

9. Correlation with Anomalous Network Activity

All these actions collectively resulted in the detected anomalous network activity from the EC2 instance, confirming the legitimacy of the initial alert.

10. Reviewing the Attacker’s Attack Path

Additionally, the platform shows the potential next move of the attacker based on the available attack path, based on the current adversary foothold.

11. Triage and Assessing the Blast Radius

With a clear sequence of the attacker’s actions and their attack path, the security team can triage the incident effectively. They assess the blast radius to determine the potential impact and scope of the breach, identifying all affected systems and data.

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12. Identifying the Attack Source

Instead of spending hours manually correlating contextless logs while the adversary advances their attack, Stream's investigation quickly identifies the attacker's origin as a Tor IP address, confirming the activity as malicious rather than a false positive. This insight solidifies the understanding of the threat and the necessary steps for mitigation.

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13. Utilizing Generative AI for Summarization and Reporting

To effectively communicate the status of an investigation to various stakeholders, Stream provides a business-level summary of the attack storyline, making it easy for everyone to understand and enabling quick, informed decision-making. Stream's generative AI feature further enhances this process by efficiently summarizing the entire investigation and generating actionable recommendations with just a click.

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Conclusion

The challenges of threat detection in the cloud are significant, given the dynamic nature of cloud environments and the complex, interconnected systems involved. Investigating threats effectively requires tools that provide not just alerts but comprehensive context.

A Cloud Detection and Response (CDR) tool with capabilities like real-time analysis of security and compliance implications, detailed tracking of configuration changes, and clear visualization of attack paths can make a significant difference. The solution helps security teams to get the entire cloud context needed to quickly identify the root cause of an issue, understand the full scope of an attack (including the blast radius), and see the impact of every change within the cloud environment.

By integrating these insights, security teams can prioritize threats more effectively, streamline investigations, and resolve issues with a clear understanding of the interconnected risks. This context-driven approach allows organizations to respond faster and more accurately to threats, ultimately enhancing their overall cloud security posture.

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