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Detecting Sensitive Credential Leakage Through Apache Superset Application Logs Using Data Discovery and Automated Sensitive-Information Monitoring

Description

Organizations use Apache Superset as a web-based business intelligence and data visualization platform to connect to databases, create dashboards, and analyze organizational data. Application logs are important for troubleshooting, auditing, and security monitoring. However, improperly configured logging can unintentionally capture sensitive information such as authentication credentials, API keys, access tokens, database connection strings, or other confidential values.

If these credentials are written into application logs, anyone who gains access to the log files may potentially obtain sensitive authentication information. This creates a significant data security and privacy risk.

In this use case, a real Apache Superset environment is deployed on Ubuntu Linux inside an isolated laboratory using VirtualBox. Synthetic credentials and test secrets are used instead of real credentials.

A controlled logging scenario is created in which synthetic credential-like information is intentionally introduced into application-generated test logs. The objective is to determine whether sensitive information can be identified before it remains exposed in the logging environment.

Gitleaks is used for automated secret discovery and detection of credential-like information. Wazuh is used to monitor relevant log files and security events, while OpenSearch is used for centralized investigation and visualization.

A Python-based validation component is used to generate controlled test records and verify whether sensitive information is successfully identified and masked.

The objective is to demonstrate how organizations can detect credential leakage in application logs and implement automated monitoring to reduce the risk of sensitive information exposure.

The complete workflow is: Apache Superset → Synthetic Credentials → Controlled Log Generation → Log Monitoring → Secret Discovery → Credential Detection → Data Masking → Wazuh Monitoring → OpenSearch Investigation → Privacy Protection Validation

Existing Security Problem

Application: Apache Superset

Apache Superset is the real application used in this use case. It is deployed on Ubuntu and configured as a working business-intelligence environment. Superset can generate different types of application logs related to user authentication, web requests, database connections, application errors, configuration events, API activity, and background tasks.

Application logs can contain sensitive information when excessive logging or debugging output records authentication credentials, API keys, access tokens, database connection information, or other confidential values.

Existing Problem:

Sensitive information can accidentally appear in application logs when developers or administrators enable excessive logging or include sensitive request/configuration information in debugging output. If the resulting log files are accessible to unauthorized users, the exposed credentials may create potential account or data compromise.

The security problem is therefore:

Apache Superset → Application Activity → Sensitive Credential Included in Test Log → Credential Stored in Log File → Log File Accessible to Unauthorized User → Credential Exposure → Potential Account / Data Compromise

The proposed solution introduces sensitive-data discovery, credential-pattern detection, log monitoring, automated alerting, data masking, log-access restriction, centralized security investigation, remediation validation, and continuous monitoring.

Attack

Specific Attack: Sensitive Credential Leakage Through Application Logs

The controlled threat scenario demonstrates how credentials can accidentally become exposed through application logs. Synthetic credentials are used exclusively within the isolated laboratory, and controlled test activity intentionally introduces credential-like information into application-generated test logs.

The scenario focuses on password leakage, API-key leakage, access-token leakage, credential-like strings, sensitive log content, excessive application logging, and unauthorized log exposure.

Attack Behavior:
Apache Superset
→
Controlled Test Activity
→
Synthetic Credential Generated
→
Credential Appears in Test Log
→
Gitleaks Scans Log
→
Sensitive Credential Identified
→
Wazuh Monitors Log Activity
→
Security Event Generated
→
OpenSearch Investigation
→
Credential Exposure Confirmed
→
Sensitive Data Masked / Removed
→
Post-Remediation Validation

Security Concept

Sensitive Data Discovery and Privacy-Preserving Logging:

The primary security concept is Sensitive Data Discovery and Privacy-Preserving Logging.

The objective is to identify credentials before they remain exposed in application logs.

The secure processing flow is:

Application Logs
→
Log Collection
→
Sensitive Data Discovery
→
Credential Pattern Detection
→
Risk Classification
→
Alert Generation
→
Credential Masking / Removal
→
Secure Log Storage
→
Continuous Monitoring
→
Post-Remediation Validation

Defensive Mechanism

Sensitive Data Discovery

Application logs are scanned for credential-like information.

Purpose

Identify sensitive information that should not appear in logs.

Credential Pattern Detection

Known patterns associated with passwords, API keys, tokens, and secrets are identified.

Purpose

Detect potential credential leakage automatically.

Log Monitoring

Wazuh monitors relevant log files and their activity.

Purpose

Detect suspicious changes or newly generated log content.

Automated Alerting

Detected sensitive information generates a security alert.

Purpose

Notify security personnel quickly when credential leakage occurs.

Data Masking

Detected credentials are replaced with safe masked values.

Purpose

Prevent sensitive information from remaining visible in logs.

Log Access Restriction

Log files are protected using appropriate Linux ownership and permissions.

Purpose

Reduce unauthorized access to potentially sensitive log information.

Centralized Security Investigation

OpenSearch is used to investigate detected events.

Purpose

Determine the potential privacy and security impact.

Remediation Validation

The logs are rescanned after remediation.

Purpose

Verify that sensitive credentials are no longer exposed.

Continuous Monitoring

Monitoring remains active after remediation.

Purpose

Detect future credential leakage.

Security Tools

Target Application: Apache Superset

Apache Superset is the real application used in this project.

Purpose
  • Provide the working application environment.
  • Generate realistic application activity.
  • Produce application logs.
  • Provide the environment in which controlled credential leakage is demonstrated.

Primary Secret Detection Tool: Gitleaks

Gitleaks is used for automated sensitive-secret discovery.

Purpose
  • Detect passwords.
  • Identify API keys.
  • Detect tokens.
  • Identify secret-like patterns.
  • Scan log/test files for exposed credentials.

Security Monitoring Tool: Wazuh

Wazuh monitors application log activity.

Purpose
  • Monitor log files.
  • Detect changes.
  • Generate security alerts.
  • Provide centralized security telemetry.
  • Support file-integrity monitoring.

Investigation Platform: OpenSearch

OpenSearch is used for centralized investigation.

Purpose
  • Search security events.
  • Investigate credential-leakage alerts.
  • Review timestamps.
  • Identify affected log files.
  • Visualize security activity.

Validation Tool: Python

Python is used to create controlled test data and validate the detection workflow.

Purpose
  • Generate synthetic credentials.
  • Generate controlled test log entries.
  • Validate detection.
  • Verify masking.
  • Perform post-remediation checks.

Target Platform: Ubuntu Linux

Ubuntu hosts Apache Superset and the security-monitoring components.

Purpose
  • Host Apache Superset.
  • Store application logs.
  • Execute controlled tests.
  • Run Gitleaks.
  • Support Wazuh monitoring.

Security Testing Platform: Kali Linux

Kali Linux is used as the authorized security-testing system.

Purpose
  • Generate controlled test requests.
  • Validate whether exposed test credentials can be identified.
  • Perform authorized log-security testing.
  • Validate post-remediation protection.

Virtualization Platform: VirtualBox

VirtualBox provides the isolated laboratory environment.

Purpose
  • Host Ubuntu.
  • Host Kali Linux.
  • Isolate the test environment.
  • Prevent accidental exposure of test credentials.

Process

STEP 01

Prepare the Isolated Environment

  • Install VirtualBox.
  • Create an Ubuntu virtual machine.
  • Create a Kali Linux virtual machine.
  • Configure an isolated virtual network.
  • Assign laboratory IP addresses.
  • Verify communication between required systems.
  • Ensure the environment is separated from production systems.
Tools: VirtualBox + Ubuntu + Kali Linux
STEP 02

Deploy Apache Superset

  • Install Apache Superset on Ubuntu.
  • Start the application.
  • Verify that the web interface is accessible.
  • Create a test user.
  • Configure a controlled test dashboard.
  • Generate normal application activity.
  • Verify that Superset logs are being generated.
Tools: Apache Superset
STEP 03

Establish the Normal Log Baseline

  • Access the Superset web interface.
  • Perform normal login activity.
  • Open dashboards.
  • Perform normal application operations.
  • Review generated logs.
  • Identify normal log patterns.
  • Record the baseline before the controlled test.
Tools: Apache Superset + Ubuntu
STEP 04

Create Synthetic Credentials

  • Create credentials that have no real-world access.
  • Ensure all values are fictional.
  • Ensure they cannot authenticate to any real service.
  • Store them only inside the isolated laboratory.
  • Prepare them for the controlled leakage test.
Tools: Python
STEP 05

Generate Controlled Credential Leakage

  • Generate a controlled test application event.
  • Include synthetic credential-like values in the test log.
  • Ensure the values are written only to the laboratory log.
  • Record the timestamp.
  • Identify the affected log file.
  • Stop the test after sufficient evidence is generated.
Tools: Python + Apache Superset
STEP 06

Identify the Exposed Credential

  • Locate the affected log file.
  • Review the test log entry.
  • Identify the credential-like values.
  • Record the type of sensitive information detected.
  • Determine where the information was stored.
  • Record the potential exposure point.
Tools: Ubuntu + Python
STEP 07

Scan Logs Using Gitleaks

  • Run Gitleaks against the controlled log location.
  • Allow the scanner to inspect the test logs.
  • Identify detected credential-like information.
  • Review the detection results.
  • Record the affected file.
  • Record the detected secret category.
  • Preserve the test results.
Tools: Gitleaks
STEP 08

Configure Wazuh Monitoring

  • Configure Wazuh to monitor the relevant Apache Superset log directory.
  • Enable file-integrity monitoring.
  • Monitor newly created or modified log files.
  • Generate a controlled test event.
  • Verify that Wazuh records the log activity.
  • Confirm that the security telemetry is available for investigation.
Tools: Wazuh
STEP 09

Generate the Security Alert

  • Trigger the controlled credential-leakage event.
  • Allow Wazuh to collect the resulting log activity.
  • Identify the affected log file.
  • Generate the corresponding security event.
  • Verify the event timestamp.
  • Preserve the event for investigation.
Tools: Wazuh
STEP 10

Investigate the Event in OpenSearch

  • Review the Wazuh event in OpenSearch.
  • Search for the affected log file.
  • Review event timestamps.
  • Identify the source activity.
  • Identify the credential-leakage test event.
  • Correlate the event with the Gitleaks result.
  • Establish the incident timeline.
Tools: Wazuh + OpenSearch
STEP 11

Classify the Data Exposure

  • Identify whether the detected value is a password, token, API key, or other secret.
  • Determine the affected application log.
  • Determine whether the information is sensitive.
  • Assign an appropriate risk level.
  • Document the exposure.
Tools: Gitleaks + OpenSearch
STEP 12

Apply Data Masking

  • Replace the exposed test credential with a masked value.
  • Update the controlled logging configuration or test-processing workflow.
  • Ensure future log entries do not contain the original sensitive value.
Tools: Python + Ubuntu
STEP 13

Restrict Log Access

  • Review the permissions of the application-log directory.
  • Apply appropriate Linux ownership.
  • Apply restrictive permissions.
  • Verify that unauthorized test accounts cannot access the protected log files.
  • Document the resulting permissions.
Tools: Ubuntu Linux
STEP 14

Rescan the Logs

  • Run Gitleaks again after remediation.
  • Scan the affected log files.
  • Verify that the exposed credential is no longer present in the remediated logs.
  • Confirm that masked values are not classified as active credentials.
  • Record the post-remediation result.
Tools: Gitleaks
STEP 15

Validate Wazuh Monitoring

  • Generate another controlled test event.
  • Verify that Wazuh continues monitoring the log directory.
  • Confirm that log changes are detected.
  • Verify that the monitoring system remains operational after remediation.
Tools: Wazuh
STEP 16

Validate the Complete Protection Workflow

  • Generate a normal Superset event.
  • Generate a controlled credential-leakage event.
  • Verify Gitleaks detection.
  • Verify Wazuh monitoring.
  • Verify OpenSearch investigation.
  • Apply masking.
  • Rescan the logs.
  • Confirm that the sensitive value is no longer exposed.
  • Document the complete workflow.
Tools: Apache Superset + Gitleaks + Wazuh + OpenSearch + Python
STEP 17

Assess Privacy and Security Impact

  • Identify the type of credential that was exposed.
  • Identify the affected log.
  • Determine exposure duration in the laboratory.
  • Review who could access the log.
  • Evaluate the potential security impact.
  • Evaluate the potential privacy impact.
  • Record remediation actions.
  • Identify remaining logging-security gaps.
Tools: OpenSearch + Gitleaks + Wazuh
STEP 18

Perform Final Data Security & Privacy Assessment

  • Review the complete credential-leakage scenario.
  • Compare the original and remediated logs.
  • Review Gitleaks results.
  • Review Wazuh alerts.
  • Review OpenSearch investigation results.
  • Verify data masking.
  • Verify log-access restrictions.
  • Recommend secure logging practices.
  • Recommend continuous secret scanning.
  • Recommend periodic log-security assessments.
  • Finalize the Data Security & Privacy assessment.
Tools: Apache Superset + Gitleaks + Wazuh + OpenSearch + Python

Outcome

  1. A real Apache Superset environment is successfully deployed on Ubuntu, providing a practical application environment for testing sensitive-information protection.
  2. Synthetic credentials are generated and used exclusively within the isolated laboratory, allowing credential-leakage testing without exposing real authentication information.
  3. A controlled credential-leakage scenario is successfully reproduced in an application-log environment, demonstrating how sensitive information can unintentionally become part of application logs.
  4. Gitleaks successfully identifies credential-like information within the controlled log data, demonstrating automated sensitive-information discovery.
  5. Wazuh provides continuous monitoring of the relevant application-log environment, generating security telemetry when controlled log activity occurs.
  6. The detected credential-leakage event is centralized and investigated through OpenSearch, enabling analysts to review the affected log, timestamp, and associated security activity.
  7. The exposed synthetic credential is identified and classified according to its sensitivity, allowing the potential security and privacy impact to be assessed.
  8. Data masking and secure log-handling controls are applied, preventing the sensitive test credential from remaining exposed in the remediated log data.
  9. Post-remediation scanning confirms that the exposed credential has been removed or appropriately masked, validating the effectiveness of the implemented data-protection controls.
  10. The complete real-world Apache Superset deployment, synthetic credential generation, controlled credential leakage, sensitive-data discovery, Gitleaks scanning, Wazuh monitoring, OpenSearch investigation, data classification, masking, log-access restriction, post-remediation validation, and Data Security & Privacy assessment workflow is successfully demonstrated.
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