Tal Shladovsky
Aug 28, 2024
6
min
Cloud security is a top priority, but false positive alerts are a persistent headache. While cloud computing offers immense benefits, it also introduces new security challenges. One of the most frustrating issues is the overwhelming number of false positive alerts that security teams face. These false alarms can lead to alert fatigue, hindering their ability to identify and respond to actual threats.
In this blog, we'll dive deep into the root causes of cloud security false positives and explore specific examples to illustrate these challenges. By understanding why these false alerts occur, security teams can better prioritize their efforts and focus on real risks.
Cloud environments are inherently dynamic and complex. Unlike static on-premises infrastructures, cloud platforms exhibit elasticity, scalability, and frequent workload fluctuations. This dynamic nature complicates the establishment of baseline behaviors, often leading to the misclassification of anomalies as potential threats.
Scenario: An e-commerce company uses auto-scaling groups in AWS to handle traffic spikes during peak shopping seasons. When traffic increases, the auto-scaling group automatically launches new EC2 instances to accommodate the load.
Issue: Security tools might flag the sudden surge in newly created instances as a potential DDoS attack or unauthorized access attempt. This is because the tool lacks the context that the increase is a legitimate response to higher traffic, leading to a false positive alert.
Scenario: A development team frequently spins up temporary cloud environments for testing and debugging purposes. These environments include databases, application servers, and other services that are torn down after the tests are completed.
Issue: The frequent creation and deletion of these resources can confuse security monitoring tools, which may interpret the rapid changes as suspicious activity or potential breaches. As a result, false positives are generated, flagging these legitimate but temporary resources as threats.
Traditional security tools struggle to keep pace with the complexities of cloud environments. Lacking essential contextual understanding, these tools often generate false positives by misinterpreting cloud activities as potential risks. For instance, some configuration changes might be considered as risky, but unless treated with the broader cloud context, it is hard to assess their potential exposure.
Scenario: A company manages a large number of security groups in Amazon Web Services (AWS) to control inbound and outbound traffic for various resources like EC2 instances and load balancers. During a routine update, a security group is mistakenly configured to allow inbound traffic from all IP addresses (0.0.0.0/0) for testing purposes. However, this security group is not associated with any active resources, such as EC2 instances or load balancers.
Issue: A cloud security tool, designed to detect open and potentially vulnerable security group configurations, flags this as a high-severity alert due to the potential exposure risk. Although no resources are actually exposed, the tool lacks the contextual awareness to recognize that the security group isn’t in use. This results in a false positive alert, triggering unnecessary incident response actions and diverting attention from actual security threats.
Scenario: A global company has employees who work from multiple geographic locations. The company's security tool is configured to alert on any login attempts from unfamiliar IP addresses.
Issue: An employee logs in from a new remote location, triggering an alert for a possible account compromise. The security tool flags the login as suspicious without recognizing that the employee is traveling, leading to a false positive.
Scenario: A company uses Amazon S3 for cloud storage and has a security tool configured to alert when large amounts of data are transferred out of the S3 buckets, as a potential indicator of data exfiltration.
Issue: The company schedules regular data backups to a different region for disaster recovery. The security tool, unaware of this routine backup process, generates false positives each time the backup occurs, incorrectly flagging it as a possible data breach.
The ever-evolving threat landscape poses significant challenges for cloud security. As attackers innovate new methods to exploit cloud vulnerabilities, security tools must adapt rapidly. Unfortunately, this constant evolution can lead to increased false positives as tools struggle to differentiate between legitimate and malicious activity. For instance, new threat detection models may misclassify benign behaviors as suspicious, resulting in false alarms.
Scenario: A cloud-based web application is updated to mitigate a newly discovered zero-day vulnerability. The security team deploys updated rules to detect any exploitation attempts of the vulnerability.
Issue: Legitimate traffic that resembles the exploit’s pattern triggers the newly updated security rules, causing the tool to flag the traffic as malicious. Without enough context or data on how this zero-day exploit manifests in real-world attacks, the tool generates false positives, leading to unnecessary investigations.
Scenario: A company relies on a cloud security tool that is regularly updated to detect the latest types of cyber threats, including ransomware. To stay ahead of new ransomware variants, the tool's detection algorithms are frequently refined. Meanwhile, the company uses an AWS Lambda function to automatically generate encrypted backups of critical data as part of their disaster recovery plan. These backups are encrypted to ensure the data's security during storage and transit.
Issue: Following a recent update, the security tool begins to flag the automated encrypted backups as potential ransomware activity due to their resemblance to ransomware behavior, which also involves encryption. The tool, lacking the context that these encrypted files are part of a routine and legitimate backup process, generates false positives. This misidentification prompts unnecessary incident response efforts, leading the security team to investigate what appears to be ransomware, but is actually a critical component of the company’s disaster recovery strategy.
The level of flexibility the cloud makes it harder to understand the entire context of it, which leads to false positives. The less time you spend on false positives, the more time you will have to focus on what matters.
Stream Security leads in Cloud Detection and Response, modeling all cloud activities and configurations in real-time to uncover adversary intent. The platform correlates activities by principles, helping security teams connect the dots and understand correlations among cloud operations. It reveals each alert's exploitability and blast radius to predict the adversary's next move, enabling security teams to detect, investigate, and respond with confidence, outpacing the adversary.
