A Developer’s Guide to Secrets Scanning

Even the most experienced developers make mistakes. Under tight deadlines, fast-moving sprints, and complex environments, it’s not uncommon for API keys, passwords, or cloud tokens to accidentally end up in source code. 

In 2024 alone, over 23 million hardcoded secrets were added to public GitHub repositories, a 25% increase from the previous year. As development accelerates, so does the risk of sensitive credentials slipping into version control. Even one secret can give attackers a direct line to critical systems, leading to data breaches, compliance violations, and reputational damage.

Automated tools give attackers a head start, often spotting exposed secrets before your team knows they’re there. At this point, scanning for secrets isn’t up for debate. The real challenge is building it into your daily workflow without slowing anyone down.

What Is Secret Scanning?

Secret scanning is the automated process of searching applications and software environments for sensitive data that should not be exposed or stored insecurely. These scans look at application code, configuration files, infrastructure-as-code (IaC) templates, container images, and sometimes documentation or logs where secrets may be accidentally stored. Common examples of secrets include:

• API keys

• Database credentials (usernames/passwords)

• Cloud provider access tokens

• SSH private keys

• TLS/SSL certificates

• Encryption keys

• OAuth tokens

• Webhooks or service credentials

• Third-party service tokens (Stripe, Twilio, Slack, etc.)

Modern secret scanners detect sensitive data using pattern matching, entropy analysis, and custom rules. Pattern matching looks for known formats like AWS keys or database URLs, while entropy analysis flags strings that look random enough to be passwords or tokens. 

Scanners can run locally to catch secrets before a commit, scan repositories for recent and historical exposures, or integrate into CI/CD pipelines to monitor every build and deployment.



Why Developers Need Secrets Scanning

Secret sprawl is a security issue, but it’s also a visibility problem. Most teams don’t realize how many credentials are scattered across their code, pipelines, and history. Tracking all exposure points is difficult, even for experienced teams.

1. Accidental Secrets in Source Control

Even in teams with strong coding standards, it’s easy for a credential to slip into a commit. A developer may temporarily hardcode a credential while troubleshooting, only to forget to remove it. This oversight contributed to the 2024 Disney breach, where attackers exploited an insecure Slack API key to access code and proprietary information. 

Secrets scanning provides an essential safety net, automatically detecting and flagging these exposures before they escalate into security incidents.



2. Shadow Pipelines and Poor Visibility

In large organizations or fast-moving projects, it’s common for unofficial or poorly documented CI/CD pipelines to emerge. Some are built quickly to meet deadlines or test new features, often without the usual security checks. These ad hoc pipelines can become blind spots where secrets are more likely to leak, especially without application dependency mapping to trace how components and credentials interact across services. Automated scanning across all repositories and pipelines helps regain visibility and control.

3. Persisting Secrets in Git History

Deleting a secret from the latest commit does not remove it from the repository’s full history. Attackers know this and routinely scan public and private repositories for secrets embedded in older commits. Scanning of the entire Git history is necessary to find and flag legacy leaks. 

4. Noise and Alert Fatigue

Too many scanners flood teams with low-priority alerts. When every potential issue looks urgent, critical findings can get lost in the noise, and teams can become desensitized. Alert fatigue is a recognized issue in the industry and one that most organizations struggle with. Context-aware prioritization—often supported by collaborative AI agents—provides insights into how and where a secret is used, so teams can focus on the exposures that pose the most significant risk.



5. Compliance and Governance Risks

Security frameworks such as SOC 2 and ISO27001 increasingly expect proactive secrets management. Automated scanning demonstrates due diligence and helps organizations meet audit requirements. This reduces the risk of compliance violations and the associated penalties.

6. Dependence on Manual Code Reviews

While code reviews remain a critical security practice, they cannot identify all secret exposures. Human reviewers can overlook sensitive strings, especially in large or complex codebases. Secrets scanning automates this process, reducing the burden on reviewers and increasing detection rates.

7 Best Practices for Secrets Scanning

Static, manual checks can’t keep up with modern development cycles or how secrets move across systems. What’s needed are processes and best practices that stay in step with developers while maintaining enough rigor to catch real risks.

1. Integrate Pre-Commit Secrets Scanning Locally

You should catch secrets before they ever leave a developer’s environment. Integrating scanning into local pre-commit hooks can prevent credentials from entering source control. Hooks should not rely on developer discipline, but should be enforced via pre-commit CI or branch protection rules. Tools like Gitleaks can run automatically with every commit, checking for exposed secrets without adding extra steps to the development workflow:

Repos:
  - repo: https://github.com/zricethezav/gitleaks
    rev: v8.18.0
    hooks:
      - id: gitleaks
        stages: [commit]

To maintain accuracy over time, rulesets should be version-controlled and updated regularly to reflect new secret types, such as updated cloud access keys or internal custom API patterns.

2. Embed Secrets Scanning in CI/CD Pipelines

CI/CD scanning compensates for gaps in local scanning and enforces security at the repository level. Scanning should be a required check in merge/pull requests, triggered during the build stage, and ideally re-validated after merging.

Jit’s Product Security platform orchestrates TruffleHog and Gitleaks, two of the most popular open-source scanners. When Jit does find a hardcoded secret, the issue can be resolved directly in the developer’s environment and there’s no need to create another pull request.  You can scan code continuously without slowing down development and build processes. You can use Gitleaks by default, but teams that prefer TruffleHog can easily switch by adding this code to the jit-plan.yaml:

override:
  workflows:
    secret-detection:
      jobs:
        secret-detection:
          enabled: false
        secret-detection-trufflehog:
          enabled: true



3. Continuously Scan Full Repository Histories

A common mistake is assuming that deleting a secret from the active codebase is enough, but without full-history scanning, old exposures can remain exploitable indefinitely. Attackers specifically look for this, scanning public and private repos for older code with forgotten credentials. 

Running full-history scans regularly helps detect and remediate these hidden risks. Scanning tools must also account for forks and mirror repositories, as secrets can proliferate beyond the original project. Gitleaks supports historic scanning with the command gitleaks detect --source=. --log-opts="--all".

4. Apply Context-Aware Prioritization

Not every secret has the same impact. A test API key might pose little risk, while a production database password could expose critical systems. Context-aware prioritization enables teams to focus their limited resources on leaks with the greatest potential for impact. This prioritization streamlines triage, reducing delays and shortening the mean time to remediation (MTTR). 

Jit’s Context Engine evaluates not just the secret type, but also runtime usage, environment, and potential blast radius. It automatically maps each finding to related infrastructure and assigns weighted priority factors. For example, if a secret is connected to a Lambda function behind an internet-facing API, it’s assigned priority factors like Production Environment, Internet-Facing, or Sensitive Credentials. With this function, teams can better triage secrets based on the likelihood of exploitation and the business context. 

5. Centralize Secret Management

Hardcoding secrets in code is often a symptom of poor secret management maturity. Make it a goal to reduce the need for hardcoding secrets in the first place. Centralizing secret storage solutions like AWS Secrets Manager, HashiCorp Vault, and Azure Key Vault allows teams to securely store and manage secrets, apply access controls, and enforce rotation policies. 

When you use S3 to securely store logs or encrypted artifacts, you should also leverage S3 Security Features—like bucket policies, IAM conditions, and access logging—to maintain firm access boundaries.

These tools should be integrated directly into development environments and deployment workflows so that secrets can be dynamically injected at runtime or retrieved. However, try to avoid over-reliance on environmental variables for secret injection. They can be logged, dumped, or leaked if not carefully controlled, especially in containerized environments.

6. Regularly Rotate Secrets

Even with good hygiene, secrets can be compromised through insider threats, social engineering, or supply chain vulnerabilities. Rotation policies limit the value of any single credential by reducing its lifespan. 

As a general guideline, non-production secrets should rotate every 30 to 90 days, while production credentials should rotate at least every 30 days. Secrets should rotate automatically via built-in cloud provider features like AWS Secrets Manager with Lambda-based rotation logic or orchestration tools integrated with CI/CD workflows. This automation eliminates the risk of forgotten or inconsistent rotations, especially when manually managing secrets at scale.



To avoid downtime during rotation, use techniques like dynamic secret reloading (so services can pick up new credentials without restarting) or rolling deployments, which gradually restart instances with updated secrets to maintain availability.

7. Train Developers in Secure Coding Practices

No scanning solution can fully replace awareness. Train developers to recognize patterns that lead to credential leaks. These patterns should include unsafe storage methods, poor handling of third-party credentials, logging secrets during debugging, and using insecure fallback credentials in test pipelines. 

Tools like Secure Code Warrior offer language-specific training paths that sharpen secure coding skills. Training should also incorporate code review workshops with real incidents as case studies. 

Secrets Scanning That Works at the Speed of Development

Between distributed teams, microservices, rapid release cycles, and sprawling CI/CD pipelines, credentials can leak in ways no single developer or reviewer can fully control. Secret scanning is the only reliable method to catch hardcoded secrets early enough to prevent serious breaches. Scanning should begin at the earliest stages of development and extend through commit, merge, build, deployment, and beyond. 

Jit runs continuously, scanning every commit and pull request, no matter the language. When a secret is found, developers see the alert in their IDE and can fix it without opening another pull request. All findings feed into the Backlog, where they’re automatically prioritized based on how and where the secret is used. This design keeps security aligned with how developers already work, making it easier to catch and fix issues without slowing down releases or creating extra overhead.

If you want to see how this works in practice, visit Jit.io and start for free today.