What is Secret Scanning? Working & Best Practices

As organizations move towards securing their data, they need to tackle and resolve different issues. The most critical of them is the exposure of sensitive data like API keys, passwords, or tokens. To resolve this issue, organizations use secret scanning to find the secrets that are exposed. But what is secret scanning?  It is a process of finding sensitive data in places like a codebase, configuration files, or collaboration tools such as Jira, Confluence, etc.

The percentage risk of exposed secrets is exponentially increasing with the increasing complexity of software applications. Secret scanning becomes of utmost importance because leaked passwords or a leaked API key can cause hefty financial losses to organizations as they can lead to data breaches and unauthorized access.

This blog will guide you through the complete knowledge of secret scanning and its importance in the cybersecurity landscape. We shall explore the best practices to ensure a strong security posture through secret management.

Understanding Secrets

Let’s understand the role of secrets in the security of applications and systems in software development.

What are Secrets?

To perform activities such as setting up secure communication channels, identifying users, protecting personal devices, or obtaining access to personal records, one needs to have access to confidential credentials, which are called secrets.

There are different types of secrets, and a few examples are API keys, OAuth tokens, SSH keys, database connection strings, and encryption keys.

Types of Secrets Commonly Found in Code and Configuration Files

Secrets could be found in different places, such as in code, Jira tickets, or even configuration files. Here are some common types of secrets and where they might be found:

1. Hardcoded Credentials: To be true to the notion, if it’s easy, it might be wrong. Hardcoding secrets or credentials in code is a convenient way for developers since it saves them from writing extra configuration and code, but this can turn out to be disastrous for the organization. If sensitive information like passwords or API tokens get hardcoded directly in the application source code and the code is made public on the GitHub repository, the attackers can extract those credentials and reuse them.
2. Configuration Files Configuration files are a great way to keep all the data, such as the API key, database password, or whatever might be required by the application for it to run, but they carry the same amount of risk. Anybody can access the file and find all the secrets or sensitive information present in it if the file is not kept private or in a secure way.
3. Environment Variables Using environment variables is a better and more suitable way to store the secrets than the local applications. Still, they can also lead to exposure if your run-time logs display that information.
4. Secret Management Tools Many organizations use tools like HashiCorp Vault, AWS Secrets Manager, or Azure KeyVault to encrypt and store their secrets. These tools provide secure storage, encryption, and access controls for secrets. However, to utilize these tools to their maximum efficiency, we should still use correct security practices.

The Risks of Exposed Secrets

When secrets are improperly protected, they can lead to serious consequences for organizations. It is very important to understand the risks if your secrets are out in the open.

Consequences of Leaked Secrets

1. Unauthorized Access: Attackers are always looking for ways to exploit a system, and the easiest way for them to do this is to find the leaked secrets of the systems and their services. Every application, database, or cloud service is protected by some API Key or password, which, if exposed, can fall into the wrong hands and lead to them (threat actors) gaining unauthorized access.
2. Service Abuse: The world has seen dangerous DDOS attacks in the past, which basically means abusing a service to disrupt it and overwhelm it, which, in turn, makes the service unavailable or increases the operations cost of the provider. This can happen if the API Key for that service gets leaked, and the attackers gain access to it and abuse it to constantly request an API. This can disrupt the day-to-day operations of the organization and harm its reputation as well.
3. Regulatory Penalties: If somehow the secrets of an organization are compromised in a data breach, they can get into legal trouble and will have to pay hefty fines. Companies should follow proper regulations to ensure data security.
4. Loss of Customer Trust: Customer trust is what keeps the organization running. If data breaches happen in an organization, customers might lose their trust, which will, in turn, cause lower sales.

How are Secrets Exposed

The secrets can be exploited in many ways if they get into the wrong hands (threat actors):

1. Hardcoding: Developers write secrets into source code, which may become visible if the code is shared or made public.
2. Misconfigurations: Incorrect settings or misconfigs, such as public cloud storage buckets, can leak secrets unintentionally.
3. Unsafe Practices: Developers can also easily slip up and accidentally share secrets in chats and project tools and forget to remove what shouldn’t have been shared in the first place.
4. CI/CD Complexity: Automated pipelines usually need secrets, and those can leak if they are not properly handled in the build or deployment process.

How Do Secret Scanning Tools Work?

The secret scanning tools are made to scan and catch confidential information in code repositories or other data sources, including passwords, API keys, access tokens, etc. These tools work by parsing text or code and identifying patterns that resemble known secret formats.

The first step of the scan is to crawl through the target codebase or data source. Once done, it goes through each file line by line and looks for certain patterns. Many regular expressions are used to specify these patterns, which are sequences of characters that form a search pattern.

The algorithms that secret scanning uses are typically pattern matching and entropy analysis. Pattern matching compares the text (the value of a secret) against different type-specific regex patterns.

Randomly generated secrets (high-entropy strings) are detected with entropy analysis. The randomness is calculated by using the distribution of characters, and this is perceived to be more secure than other techniques as it provides unpredictability.

Limitations of Secret Scanning Tools

Challenges with secret scanning tools are several. One of these is false positives, in which the tool detects non-secret strings as secrets. This can result in false alerts and a waste of time & resources.

The next limitation occurs if the secret is hardcoded and encrypted. Developers could even split secrets into various pieces stored in separate variables or use encoding tricks that scanning tools would struggle to catch on to.

Secret Scanning in Different Environments

Secret scanning is an important security measure that should work in modern-day tooling. Each tool or platform has its own challenges and demands a different approach in order to detect secrets.

Version control systems

1. GitHub: Integrated into GitHub Advanced Security, secret scanning identifies known secret formats like credential keys and can automatically scan your repositories.
2. GitLab: GitLab offers Secret Detection as part of its CI/CD pipeline. You can set pipelines up to run via the web interface on commits, merge requests, or scheduled pipelines, which gives good flexibility in when and how these scans are performed.
3. BitBucket: BitBucket relies more or less on products like Nightfall or GitGuardian, which are third-party integrations that monitor repositories continuously. The biggest benefit is that these tools are customizable and tailored to your organization’s specific requirements and security policies.

The hardest part about using secrets in the version control systems is dealing with secret history. After a secret is committed and pushed, it will still remain in the history of that repository even if the developer later removes it.

For this problem, push protection and pre-commit hooks are used by many organizations to scan secrets before committing, which enforces a security check on secrets.

Collaboration tools

Collaboration platforms such as  Jira, Confluence & Slack have a separate set of challenges. Secrets could be passed in issue descriptions, comments, or wiki pages, etc., on Jira and Confluence. To keep the environment secure, some organizations will have APIs that allow scheduled scans of content to check for any new secrets.

Secrets could be shared via a chat/group message or file upload on Slack and similar messaging platforms. The biggest problem in these platforms is the dynamic content. The information constantly changes every day, with new posts added, edited, or deleted.

How to Respond to Found Secrets?

If an organization detects secrets in its code, the first action is to determine how serious this situation is. Find out the type of secret that has leaked (such as an API key or password) and its potential effect on your system. Answering this makes it easier to set up your IR (incident response) plan and also draws a line about how bad things have gotten.

Then, immediately take steps to revoke or disable the exposed secret, which blocks any other unauthorized bypass. Revoke the old secret & issue a new one. This process is called rotation. At the same time, check your logs and systems for any unauthorized access or irregular activity that may suggest someone abused the secret.

At last, a thorough incident report should be made describing how the secret was leaked and what steps were taken to fix it. This is the information you can learn from and improve your secret management.

Handling False Positives and False Negatives

False positives are when a tool flags the presence of a secret in code, but there isn’t one.  False negatives, on the other hand, occur when a tool fails to catch an actual secret. Those are issues that can make secret scanning tools less effective.

False positives waste developers’ time through non-issues, while on the other end, false negatives increase the chances of being hacked through unnoticed issues.

Organizations can take a few steps to keep these issues to a minimum. The accuracy needs to be enhanced in a couple of ways, including updating configurations and tools for scanning regularly. Machine learning helps reduce the false positive rate since it can learn from previous scans and improve how the detection is done. Contextual analysis, such as the location of the code in use, can further help to differentiate what secrets are and what are not.

Training Developers on Secrets Management

Always educate developers on how they can write secure code without exposing their secrets. Each of these sessions should focus on essentials like locating secrets, knowing the risks of secret exposure, and using best practices for password management.

The same is true for constructing a security awareness culture inside the development teams. To do this, leaders can foster an environment for honest conversations around security habits and experiences, as well as provide continued education.

What are the Best Practices for Secret Scanning Management?

Effective secret scanning management is crucial for maintaining the security of an organization’s sensitive information. Here are five key best practices that can help you enhance the effectiveness of secret scanning efforts:

1. Implement a Comprehensive Secret Scanning Policy

The secret scanning policy needs to define a secret and specify which environments need to be scanned and how often they are being run through the secret scanning tools. It should outline how secret scanning is to be managed within the organization, enforce policies around this practice, and define new roles. The policy should be revisited and updated to address new kinds of secrets or types of threats.

2. Integrate Secret Scanning into the Development Lifecycle

With early secret scanning, as well as scanning everywhere in the development process, companies will never expose any secrets. It can take the form of pre-commit hooks in version control systems to catch secrets before committing or secret scanning as part of CI/CD pipelines.

3. Prioritize and Manage Alerts Effectively

Too many alerts from secret scanning tools can result in alert fatigue if not handled correctly. Use a system to triage an alert based on its severity and potential impact. You might convert secrets into categories based on their level of severity and sensitivity. Use automation to triage alerts and direct them to different teams for resolution. Real-time alerts ensure an immediate response to high-risk exposures and eliminate the problem of security teams being overwhelmed with data.

4. Conduct Regular Audits and Penetration Testing

Along with automated scanning, manual audits, and penetration testing should be supported as well, which might highlight secrets an automated tool might miss. They can also ensure that secret scanning policies are followed uniformly throughout the organization by carrying out manual audits.

5. Provide Ongoing Education and Training

Scanning for secrets is only as effective as the awareness and cooperation of all employees, especially those in development or operations. Most staff working with secrets will often not understand the need for good secret management or know how serious it can be to leak your own keys. Secret scanning, alert training, and secure means of sharing sensitive information should be part of this training.

Future Trends in Secret Scanning

Secret scanning has been quickly evolving due to technological advancements and the increased complexity of security threats within the cybersecurity field. Five major trends that we might see in the future of secret scanning are:

1. Artificial Intelligence and Machine Learning Integration: If powered by the right set of AI & ML algorithms, secret scanning tools can surely reduce false positives, find unique secret patterns, and adapt to new secrets in a way more agile than rule-based techniques. They can also use historical data to predict if the secrets would have been exposed.
2. Shift-Left Security Practices: Secret scanning and other security practices are increasingly moving towards integrating earlier in the development process. Companies should follow the “shift-left” approach to detect leaks or mishandling of secrets in code before it ever gets deployed.
3. Automated Remediation: In addition to detection, existing tools can be enhanced in a way that allows them to automatically take action. This means things like automatically rotating compromised credentials or temporarily denying access to compromised secrets, thus reducing the time between detection and resolution.

Conclusion

Organizations are now putting a lot of importance on securing any and all sensitive information in the digital age. Exposed secrets can lead to unauthorized access, data breaches, and significant reputational damage. It is very important to understand how secrets are exposed and manage them effectively.

Secure practices like using secret management tools such as HashiCorp Vault are really important to keep the secrets secure. Similarly, it also fosters a culture of security awareness within development teams to easily identify and remediate secrets-related risks.

Focusing on treating secrets as a layer and continually educating teams to secure coding standards are how you make an organization more secure. With ever more sophisticated threats, adopting a proactive secrets management strategy will keep sensitive information secure and preserve the trust of customers and other stakeholders.

FAQs

1. What is Secret Scanning?

API Keys, passwords, and other credentials might get hardcoded in source code and configuration files, which will lead to their exposure. The process of detecting these sensitive credentials is called secret scanning. Organizations should follow best practices to ensure that secrets are not exposed unintentionally in public repositories to avoid data breaches and unauthorized access.

2. What are the Different Methods Used in Secret Scanning?

Secret scanning mostly consists of static and dynamic analysis. When the code is scanned at rest to look for secrets through pattern matching, it is called static analysis, whereas if the tools identify the secret during runtime, it is known as dynamic analysis. Nowadays, with the AI progression in the cybersecurity industry, we observe that there are few tools that take support from machine learning algorithms to increase the detection accuracy by referring to previous scans, which leads to fewer false positives.

3. Who is Responsible for Responding to Secret Scanning Alerts?

Secret scanning alerts are generated as soon as tools encounter any possibility of exposed secrets. The responsibility to respond to an alert should be of both the development and security teams. Developers will fix the issue, and the security team will advise them on how to fix the issue and revalidate once done.

4. How to use Git Secrets Scan?

To use Git Secrets scan, first install Git Secrets using package managers like Homebrew or by downloading it from the official GitHub repository. After installation, initialize Git Secrets by running git secrets –install in your repository to set up hooks that scan for secrets on commit. You can manually scan your codebase for any exposed secrets by using the command git secrets –scan. Finally, review any alerts and take appropriate action, such as revoking exposed secrets and replacing them.