False positives represent a critical yet often overlooked challenge in the realm of cybersecurity. These occur when a security alert identifies a potential threat that ultimately proves to be harmless. Although these false alarms may seem like minor nuisances, they can deplete resources, overwhelm security teams, and significantly increase the likelihood of real threats being missed. Moreover, false positives can disrupt the achievement of other organizational goals and elevate the risk of actual threats slipping through unnoticed.
False Positives in Secret Scanning
False positives in secret scanning arise when a secret scanning solution flags legitimate content as suspicious. The primary causes of these false positives include:
- Overly sensitive tools
- Solutions lacking adequate contextual data
- Secret scanners with insufficient functionality
Sensitivity
Secret scanning solutions configured to be overly sensitive may flag any deviation from the norm. Whether these configurations are intentional or part of the tool’s native functionality, excessively strict thresholds mean that normal activities trigger alerts, distracting security analysts. To mitigate this, secret scanning solutions should allow for custom rule configurations, enabling users to adjust the sensitivity according to their organization’s specific needs.
Contextual Data
Scanning solutions that operate with incomplete contextual data may struggle to accurately interpret their findings. These tools typically rely on a binary threat detection matrix, labeling findings as either threats or non-threats without assigning severity ratings. As a result, security teams must investigate all findings, legitimate or otherwise.
Contextual analysis, however, considers a broader set of criteria surrounding an event. For instance, a secret scanning solution that uses contextual analysis will compare a potentially leaked or unmanaged secret against the organization’s secrets management system.
Costs of False Positives
False positives resulting from secret scanning solutions not only increase the risk of breaches by distracting security teams but also impact costs in various ways. These costs include:
- Missed threats: A notable example is the 2013 Target data breach. Investigators discovered that the retail company’s security solution had sent multiple automated alerts warning of a potential intrusion, but these alerts were ignored. This resulted in one of the largest data breaches in history, compromising personal and payment card data of approximately 40 million customers. The breach cost Target over $200 million, including costs for forensic investigations, security enhancements, legal settlements, and customer credit monitoring.
- Wasted time and resources: Each false positive necessitates an investigation by the security team. Security analysts spend valuable time examining alerts that pose no actual threat, diverting their attention from more critical issues.
- Increased labor costs: As the number of false positives rises, so does the workload. Organizations may need to hire additional security analysts to cope, inflating personnel costs. A recent survey indicated that 67% of respondents required more than five hours to identify a false positive. Considering the average salary for a security engineer in the United States is $165,000, organizations incur at least $413 in increased labor costs for each false positive. For an organization experiencing 1,000 false positives annually, this translates to over $400,000 in wasted labor costs.
- Security tool maintenance: Constantly tweaking security tools to reduce false positives can be time-consuming and expensive. Fine-tuning detection rules and thresholds requires significant effort, especially as organizations scale their infrastructure.
- Alert fatigue: Over time, security teams faced with constant false positives may start ignoring alerts altogether, increasing the likelihood of real threats being overlooked. This takes us back to the example of missed threats.
- Burnout and turnover: The repetitive nature of investigating false positives can lead to job dissatisfaction and burnout, pushing skilled analysts out of organizations and creating talent gaps.
- Decreased trust in security systems: If security tools consistently generate inaccurate alerts, organizations may lose faith in their cybersecurity systems, making them hesitant to rely on automated responses or trust system-generated insights.
Reducing False Positives with HCP Vault Radar
HashiCorp recognizes the impact of false positives and has developed HCP Vault Radar with a strong focus on reducing these false alarms when scanning repositories, collaboration tools, or data storage. This includes automatically assigning severity levels to findings, prioritizing the most critical items.
Version history: When HCP Vault Radar identifies a finding in the latest version of a file, it assigns a higher priority since it is likely that this finding has not been previously evaluated and is therefore more likely to be a secret.
High entropy: HCP Vault Radar evaluates the entropy (randomness) of content using entropy algorithms, which are effective at identifying random or complex strings that may indicate an exposed secret. The tool also evaluates string literals in code for entropy, helping to identify potentially suspicious strings in any format.
Activeness checks: Active credentials represent the most significant threat. When HCP Vault Radar discovers a credential, it checks with the associated application to see if the secret is still active. Active credentials are marked as critical risks within the prioritization portal. Currently, Vault Radar can test for:
- Google Cloud API keys
- Amazon Web Services (AWS) access keys
- Personal access tokens for GitHub
- JSON web tokens (JWT)
Vault correlation: To further support prioritization, HCP Vault Radar can correlate if a leaked secret is stored in a Vault secrets manager. Since most credentials in Vault are used in critical production environments, HCP Vault Radar assigns a higher severity score to exposed secrets also found in Vault’s key-value stores. This provides the contextual data that secret scanners have been lacking.
Ignore rules: HCP Vault Radar’s ignore rules allow you to ignore certain events based on a set of criteria unique to your organization. There are several types of ignore rules, including:
- Path: Path-based ignore rules enable you to ignore entire paths, such as directories used for documentation, or specific files within a resource.
- Resource: Resource ignore rules allow you to create ignore rules scoped to a specific resource, such as honeypot repositories or documentation repositories that may generate many unimportant alerts.
- Secret type: Secret type ignore rules allow you to ignore specific types of secrets. These rules can be configured by data source, meaning you could set a rule to ignore API tokens for a GitHub data source, but Confluence API tokens would still trigger an event. Types of secrets include key-value pairs, API keys, passwords, certificates, tokens, credentials, and database credentials.
- Secret: Secret ignore rules allow you to ignore specific secret values that may be used in a data source, such as an example password used in documentation or as an example within the application.
Remediating Unmanaged Secrets
In addition to secret scanning, HCP Vault Radar supports a comprehensive set of remediation workflows through ticketing and alerting solutions. These integrations utilize commonly used tools in DevOps, platform engineering, and security teams’ workflows, supporting incident response processes. HCP Vault Radar transmits all necessary information to remediate its findings, including:
- Author
- Location
- Activeness
- If the secret is in the current version of a document or history
- Whether the secret is publicly accessible
The secret itself is never exposed in the HCP console. Instead, the user is provided with a link to the location where the secret can be found, investigated, and remediated.
Next Steps
HCP Vault Radar is an exciting new addition to HashiCorp Vault’s secret lifecycle management capabilities, helping enterprises reduce the risk associated with credential exposure. The discovery of unmanaged secrets and subsequent remediation workflows further differentiate Vault’s secrets lifecycle management offering by enabling organizations to take a proactive approach to remediation before a data breach occurs.
Additional Resources
For more information on HCP Vault Radar and its capabilities, you can visit the official HashiCorp Vault product page.
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