Table of Contents

The complete guide to secrets scanning

You know that sinking feeling when you realize you’ve accidentally committed an API key or non-human identity credential to your code repository? Yeah, we’ve all been there. But here’s the thing: those little slips can have big consequences. Exposed secrets (or non-human identities) can lead to data breaches, financial losses, and damaged reputations. 

That’s where secret scanning comes in. By understanding what it is, how it works, and where it fits into your overall secrets management strategy, you can proactively safeguard your organization’s most valuable assets.

What is secret scanning?

Secret scanning is an automated process that proactively identifies sensitive information like API keys, access tokens, and other credentials that may be inadvertently exposed in code repositories or other data sources. It’s a critical application security capability that helps prevent non-human identities from being leaked and potentially abused.

Secrets scanning typically involves parsing through code and files and hunting for telltale signs of secrets. These could be certain string patterns that match the format of different secret types or even specific keywords and variable names that are dead giveaways. More advanced tools may use machine learning to identify secrets based on their context and usage.

To be truly effective, secrets scanning needs to cover all the nooks and crannies where secrets tend to hide—full repository history, pull requests, wikis, and adjacent systems like build logs, artifacts, or config files.

Why is secret scanning important?

Imagine a well-meaning developer accidentally commits a secret to a public repository. An attacker discovers this secret in a matter of minutes and gains unauthorized access to the organization’s systems, leading to a massive data breach. The financial and reputational damage could be catastrophic. This is where secret scanning comes in. It acts as a guard, continuously monitoring for any accidental exposure of secrets across your codebase, configuration files, and communication channels.

Organizations can swiftly remediate the issue by proactively identifying and alerting teams to potential secret leaks before they can be exploited. However, the benefits of secret scanning extend beyond just preventing data breaches. In many industries, such as healthcare and finance, strict regulatory requirements exist around protecting sensitive data. Failure to comply can result in hefty fines and damage your organization’s reputation. By implementing secret scanning, you can demonstrate your commitment to data security and help ensure compliance with these regulations.

Moreover, the cost of a data breach can be staggering — not just in terms of financial losses but also in terms of lost customer trust and damage to your brand. By investing in secret scanning, you’re taking a proactive step to mitigate these risks and protect your organization’s bottom line. In a world where data is currency, secret scanning is a wise investment in your organization’s future.

How do I know if it’s a secret?

How do you know if a piece of data is a secret? It’s not always straightforward, but there are several key indicators to look out for:

     

      • High entropy: Secrets often have a high level of randomness or entropy. If a string looks like gibberish and doesn’t resemble normal words or phrases, there’s a high chance you’ve got yourself a secret. Secret scanners can measure the entropy of a string to flag potential secrets; however, entropy alone can lead to false positives.

       

        • Regex: Many secrets, especially API keys and access tokens, follow distinct patterns. For example, AWS access keys always start with “AKIA,” and GitHub access tokens consist of 40 hexadecimal characters. Regular expressions (regex) can define and match these patterns in code or data. It’s worth mentioning that creating comprehensive regex rules for all possible secret formats can be challenging.

         

          • Known secret formats: Certain types of secrets, like SSH keys, SSH/TLS certificates, or PGP keys, have well-defined formats. If data matches one of these known formats precisely, it’s very likely to be a secret or non-human identity. Maintaining a dictionary of such formats allows for reliable detection.

           

            • Contextual clues: Developers often use revealing names for variables holding secrets, like “password”, “secret_key”, or “api_token”. Scanning code for these suggestive names can help uncover non-human identities. Machine learning can be used to identify naming patterns that are likely to be associated with non-human identities.

             

              • Machine learning: Machine learning and AI algorithms can be trained on large datasets of known secrets to recognize patterns and contextual clues that indicate a string might be a secret. These models continuously learn and improve based on feedback, reducing false positives. ML models can identify secrets without strict pattern matching or specific naming conventions.

               

                • Anomalous behavior: Secrets tend to be handled differently than normal data. Monitoring for unusual access patterns, like a sudden increase in database reads or API requests using a particular token, can help surface leaked secrets.

              The most effective non-human identities detection approaches combine multiple techniques, leveraging deterministic methods like pattern matching and probabilistic ones like machine learning. This layered strategy helps maximize the chances of finding real non-human identities while minimizing false positives.

              Where to scan for secrets?

              You can find secrets at numerous places in your development cycle and need a strategy before they get leaked and lead to costly breaches. Here are some of the key areas where you should focus your secret scanning efforts:

                 

                  • Code repositories: Developers are human, and mistakes happen. It’s too easy for an API key or database password to commit accidentally to your source code repository. Any standard secret scanner can go through your repositories, and even the entire commit history, to uncover any secrets that might be hiding in plain sight. It can leverage a combination of pattern matching, entropy analysis, and machine learning to identify potential non-human identities and deliver accurate results accurately.

                   

                    • Container images: As you build and deploy containerized applications, secrets can also find their way into your container images. Again, a secret scanner recursively unpacks the image layers and scans the files within for sensitive data. This ensures that no secrets are inadvertently baked into your deployments.

                     

                      • DevOps tools and pipelines: Your CI/CD pipelines and DevOps tools, such as Jenkins, Ansible, or Terraform, often require secrets to function, to wit, you need secure secrets management solutions that integrate seamlessly with your DevOps stack. This shall enable you to centrally store, manage, and rotate secrets, ensuring that your pipelines can access the needed secrets without the risk of exposure.

                       

                        • Observability pipelines: As you collect and process logs, metrics, and traces, sensitive data can sometimes find its way into your observability pipelines. Secret scanners can detect and redact sensitive information in real-time as the data flows through the pipeline, ensuring that secrets aren’t in your logs or metrics.

                      Covering your tracks here can significantly reduce the risk of secret exposure. Combine dedicated secret scanning tools with secure secret management solutions to build a robust defense against secret leakage. Remember, the key is to scan early and often, catching secrets before they can cause harm.

                      How to scan for secrets?

                      Effective secret scanning involves being strategic and understanding the context in which these secrets exist. Simply running a secret scanner and calling it a day is not enough. You need a comprehensive approach involving at-rest scanning and real-time monitoring.

                      At-rest scanning is like conducting a thorough audit of your digital assets. This includes:

                         

                          • Combing through your entire code repositories, including all branches and commit history, to uncover any secrets that may have been forgotten.

                           

                            • Scouring all the data stored within your SaaS applications, from configuration settings to dormant databases.

                          While at-rest scanning provides a solid foundation, real-time scanning is what keeps watch over your systems:

                             

                              • Monitoring new code pushes and CI/CD processes for any non-human identities that might slip through the cracks.

                               

                                • Keeping an eye on new data entered or changed within your SaaS applications, catching secrets as they appear.

                              Integrating secrets scanning throughout the development process provides a layered defense. Real-time scanning can block secrets from being committed in the first place. Periodic scanning of the entire codebase can uncover pre-existing secrets that need to be revoked and replaced. By quickly detecting and addressing leaked secrets, we can be a lot more proactive in reducing the risk of unauthorized access and data breaches.

                              But the real magic happens when you layer context on these scans. By understanding the type of non-human identity, location, and potential impact, you can prioritize your remediation efforts. For example, a stale SSH key buried in an old repository might pose a different risk than a current AWS access key pushed to a public GitHub repo.

                              The key is not just to find secrets but to understand their significance and act accordingly. In other words, with a context-driven, prioritized approach to secret scanning, you can focus your efforts where they matter most.

                              Parting thoughts

                              There are a ton of things you can do to prevent your secrets from leaking out to criminal hands:

                                 

                                  • Centralized secrets management: Use a centralized secrets management solution like a secrets vault to store and manage non-human identity credentials securely. Platforms like AWS Secrets Manager or HashiCorp Vault are common choices, and getting them in regular usage will help contain secrets sprawl going forward.

                                   

                                    • Enforce least privilege access: Apply the principle of least privilege when assigning permissions to non-human identities. Also, it’s worth reviewing and adjusting these permissions regularly to ensure they remain aligned with the current requirements.

                                     

                                      • Context-aware secrets rotation: Implement a secrets rotation strategy that factors in the criticality and context of each non-human identity. Prioritize the rotation of high-risk secrets, such as those with elevated privileges or access to sensitive data. Leverage automation to streamline the rotation process and ensure that secrets are regularly updated based on predefined schedules or triggering events.

                                       

                                        • Implement strong authentication mechanisms: While multi-factor authentication does not directly apply to non-human identities, it’s worth enforcing strong authentication mechanisms for the systems and processes interacting with NHIs. To that end, implement secure authentication protocols, such as mutual TLS (mTLS) or OAuth 2.0, to ensure that only authorized systems and services can access and utilize the non-human identity credentials.

                                      As much as adhering to these best practices will lead to a strengthened security posture for your non-human identities, implementing these measures effectively requires comprehensive visibility and contextual understanding of how your NHIs interact with each other.

                                      This is where Entro comes in, delivering context-aware, prioritized secret scanning to your doorsteps. While most secret scanners only focus on code repositories, ML-powered Entro goes above and beyond. It not only performs git secret scanning but also goes through your Jira tickets, wikis, Slack channels, logs, and config files — ensuring no stone is left unturned in the hunt for exposed non-human identities.

                                      Entro provides the vital context you need to understand and prioritize the risks truly. It answers critical questions like how many non-human identities you have, where they’re located, who owns them, what permissions they have, and which services they’re tied to. 

                                      So, if you’re tired of flying blind, come on and experience the Entro difference. Book a demo today and see how context-aware secret scanning can transform your security posture.