The shift to cloud-native technologies, containerization, and DevOps platforms has created an increased reliance on secrets, which include credentials, certificates, and keys. This evolution has positioned stolen credentials exploits as a leading cause of breaches, making up nearly 40% of all hacking breaches in 2024. In the same year, over 39 million secrets were detected on GitHub repositories, which underscores the massive scale in the challenge to protect them.

What is secret scanning?

Secret scanning is a security process that automatically scans code and other data sources for sensitive information. It plays a vital role in software security by detecting exposed credentials, API keys, and other sensitive data in code repositories, logs, and commit histories. This solution helps detect and prevent secrets from exposure in the application development process, providing developers and application security teams with faster, easier ways to help find, fix, and prevent secret leaks.

As an integral part of a comprehensive security strategy, secret scanning complements other security measures such as code scanning, vulnerability scanning, and compliance checks.

An effective secret scanning solution integrates seamlessly into developer workflows, enabling more secure software development with less disruption to velocity and providing tools that allow for quick and efficient remediation. For security teams, it helps eliminate the need to adopt and manage additional tools.

Modern development practices like DevOps and cloud-native apps have increased the risk of secrets being exposed. By identifying and mitigating potential security risks early in the development process, secret scanning enhances an organization’s overall security posture.

What are secrets in software development?

Secrets in software development refer to sensitive information that should be kept confidential. Examples include:

• API keys, which are unique identifiers used to authenticate and authorize applications accessing APIs.

• Authentication tokens, which provide temporary authorization for a specific user or application to access resources.

• Encryption keys, which encrypt and decrypt sensitive data to enable secure communication and user authentication.

Here are examples of environment variables and configuration files:

1. Environment Variables:

   import os # Accessing a secret API key from environment variables api_key = os.getenv('API_KEY')

2. Configuration Files:

   { "database": { "user": "admin", "password": "supersecretpassword" } }

Secrets are often hardcoded, which means they’re embedded directly into the source code. This practice makes the code simpler and more self-contained, but also more difficult to maintain.

Hardcoding risks of exposed secrets

Hardcoding secrets can expose organizations to risk, including unauthorized access, data breaches, and compliance violations. Malicious actors can exploit exposed secrets to gain access to sensitive systems and data through:

• Security vulnerabilities

• System failures

• Scaling complications

• Costly maintenance

• Environment incompatibility

Here’s an example of what hardcoding looks like:

public class Config { public static final String API_SECRET = "mySuperSecretKey"; }

Exposed secrets can lead to unauthorized access and theft of sensitive information, which can cause irreparable harm. For example, if login credentials are improperly published, like on a public repository, threat actors can steal that information, use it to infiltrate an organization’s network, and extract confidential data.

Malicious actors only need a few seconds to a few minutes to locate and exploit secrets. The longer secrets remain undetected by a company, the higher the likelihood that they will eventually be discovered by outside entities.

Compliance standards, such as SOC 2, NIST, and GDPR, require companies to responsibly manage and protect secrets. Failure to develop adequate security measures can result in fines and potential damage to a company’s reputation.

These risks underscore the critical importance of implementing robust security measures to detect and mitigate the exposure of confidential information.

How secret scanning works

Secret scanning tools parse through code and identify patterns that resemble known secret formats. They scan code repositories, commits, configuration tools, and other data sources for sensitive information, passwords, and access keys. Here are a few examples:

GitHub Secret Scanning automatically scans for different tokens from various providers, such as passwords and internal URLs:

import os # Accessing a secret API key from environment variables api_key = os.getenv('API_KEY') # Example of a secret scanning tool detecting a hardcoded secret api_key = "mySuperSecretKey" # This would be flagged by the secret scanner

Push protection prevents the disclosure of secrets stored in plain text files, such as source code and JSON files:

const AWS = require('aws-sdk'); const secretsManager = new AWS.SecretsManager(); async function getSecret() { const secretValue = await secretsManager.getSecretValue({ SecretId: "mySecretId" }) .promise(); return secretValue.SecretString; } // Example of a secret scanning tool detecting a hardcoded secret const secret = "HardcodedSecret"; // This would be flagged by the scanner

Automated vs. manual scanning

Secret scanning can be performed both manually and automatically. Automated scanning tools provide continuous monitoring and detection, while manual scanning involves periodic reviews by security professionals. Integrating secrets detection into CI/CD pipelines ensures that secrets are detected and addressed throughout the development lifecycle.

Here's an example of a manual scan:

public class Config { public static final String API_SECRET = "MySuperSecretKey"; // This would be flagged by the secret scanner }

Benefits of secret scanning

Organizations that implement secret scanning can experience:

• Early detection of vulnerabilities and real-time alerts, allowing organizations to address potential security risks before they can be exploited.

• An enhanced security posture, which helps companies stay ahead of potential threats and helps ensure that sensitive information is protected.

• Compliance standards being met for security regulations like SOC 2, NIST, and GDPR.

• Seamless incorporation into developer workflows, which can improve productivity, visibility, and alignment with software development processes.

• Cost-effective security, as proactive secret scanning tools reduce the threat of data breaches, which often lead to significant financial loses and reputational damage.

Real-world incidents and case studies

Exposed secrets have caused major security problems, such as the DeepSeek database leak. A security vulnerability exposed publicly accessible database from the AI startup which contained secret keys, backend details, metadata, and over a million lines of log streams. The lack of authentication would have potentially allowed attackers to gain access to sensitive data and to take full control over the database.

Many companies have successfully used secret scanning for risk identification, including:

• A professional services organization implemented secret scanning with GitHub Advanced Security to ensure nearly every line of code committed to their repository was automatically inspected.

• A customer-to-customer e-commerce company deployed secret scanning to improve secrets management and streamline issue resolution.

Future trends in secret scanning

AI-powered detection is shaping the future of secret scanning, with machine learning algorithms boosting accuracy and efficiency for better protection against exposed secrets. As technology advances, these algorithms will learn to identify patterns and anomalies in data that indicate potential breaches in the software supply chain. By constantly updating and refining their knowledge base, AI systems can become increasingly effective at preemptively detecting threats before they cause significant harm.

In addition to improved threat detection, AI can also facilitate automated responses to identified risks, reducing the time it takes to mitigate potential impacts. This can result in a more proactive approach to cybersecurity, where issues are addressed swiftly and efficiently. AI-powered secret scanning tools will be able to adapt to emerging threats.

Along with AI, security automation continues to evolve, with new technologies and methods being developed to improve the efficiency of secret scanning. These tools can continuously monitor code repositories, configurations, and logs to identify sensitive information, such as API keys, passwords, or personal data, which may have been inadvertently exposed.

By automating routine tasks, organizations can reduce the burden on security teams and help ensure a faster response to potential threats.

Secure your organization’s data

Secret scanning has become an indispensable tool for safeguarding sensitive information. By leveraging both automated and manual techniques, organizations can detect and mitigate security risks early in the development process.

GitHub offers a range of AI-powered tools and applications, including Secret Scanning, to safeguard your sensitive data.