One leaked API key can bypass millions in security investments. And it’s surprising how easily those keys end up exposed.

Jump directly to the list of the tools 🛠️ 👇

Modern development moves fast:

• CI/CD pipelines push hundreds of commits daily
• Developers work across multiple repos
• Infrastructure-as-code means secrets live in more places than ever

GitGuardian's 2025 State of Secrets Sprawl report found 23.8 million secrets were leaked on public GitHub repositories in 2024 alone. That's a 25% jump from the year before.

That’s not the worst part, though. 70% of secrets leaked in 2022 are still active today. These are live credentials sitting in git history, just waiting to be found.

Many secret scanning tools catch these leaks before they become breaches. SAST (Static Application Security Testing) tools only look for security vulnerabilities, but secrets detection software hunts for compromised credentials: AWS access keys, database passwords, SSH private keys, OAuth tokens, and anything else that could grant unauthorized access to your systems.

How Secret Scanning Works

Most real-time scanning tools use three techniques:

1. Pattern matching looks for known secret formats. An AWS access key, for example, always starts with "AKIA" followed by 16 characters. These signatures make automated detection possible across billions of commits.
2. Entropy detection catches high-randomness strings that look like generated secrets even when they don't match known patterns. This helps find custom API keys and internal tokens that wouldn't show up in pattern libraries.
3. Context-aware validation reduces false positives by checking what's around a potential secret. Is it in a test file? Is there a comment saying "example key"? Smart scanners use these signals to separate real leaks from noise.

The hard part is balancing detection accuracy with alert fatigue. A tool that catches everything but buries your team in false positives isn't much better than one that misses half the real secrets.

What Makes a Good Secret Scanning Tool in 2026

Not all secrets scanners are built the same. Here's what matters when you're evaluating options:

• Continuous scanning vs. scheduled scanning makes a huge difference in mean time to detection. Triggered scanning that runs on every commit catches leaks in seconds. Automated scans that check entire repositories might take hours or days to flag the same issue.
• Detector breadth determines what you actually catch. Generic password detection matters as much as cloud provider keys. You need coverage for AWS, GCP, Azure, internal tokens, database credentials, and everything developers might hardcode.
• Integration depth goes beyond just connecting to GitHub. Can you scan GitLab, Bitbucket, and Azure DevOps? What about Jira tickets, Slack channels, container images, and CI/CD artifacts? Secrets sprawl across the entire software development lifecycle, and your scanning needs to keep up.
• Prioritization and triage separate enterprise tools from basic scanners. Risk scoring, policy rules, and severity classification help teams focus on what matters instead of drowning in test keys and false alarms.
• Remediation workflows are where most tools fall short. Detection is useless if you can’t do something about it. The best platforms include playbooks for secret rotation, integration with ticketing systems, and guided steps that help developers fix issues fast.
• Machine learning for false positive reduction is non-negotiable. Even great pattern-matching produces noise. AI secrets detection tools that learn from your environment cut alert volume while maintaining accuracy.
• Reporting and compliance features matter for regulated industries. Audit trails, dashboards showing exposure trends, and evidence that sensitive data are continuously monitored help meet SOC 2, ISO 27001, and similar frameworks.

Still, scalability tends to be the bigger issue for enterprise security teams. Some tools works great for 10 developers but collapse under the weight of 500+ engineers across dozens of code repositories and multiple cloud environments.

Best Secret Scanning and Detection Tools for 2026

Now, let’s start looking at the tools. Each section covers what the platform does, who it's built for, and where it shines (or falls short).

GitGuardian

GitGuardian is an enterprise-grade secrets security platform that scans across the entire SDLC: source code, CI/CD pipelines, containers, IaC files, and even public GitHub history, as well as collaboration tools such as Jira or Slack. It's built for organizations that need comprehensive detection combined with governance and remediation features to fix what gets found.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Best-in-class detection accuracy - 550+ detectors with industry-leading coverage	Premium features like public monitoring require paid tiers
Enterprise governance features - Audit trails, RBAC, developer feedback loops	Free version limited to very small teams
Strong remediation focus - Not just detection, actual incident resolution
Public monitoring - Catches secrets leaked in personal repos (6-8+ years of history)
Multi-source scanning - Beyond git: Docker, Slack, Jira, Confluence, logs
ML-powered false positive reduction - 50% FP reduction through machine learning
317+ validity checks - Automatically verifies if secrets are active
Saves 23+ hours per team weekly - Through automated remediation workflows
Incident lifecycle management - Timeline tracking, regression detection, collaboration
Breach detection - Honeytoken support for breach monitoring
NHI governance - Secret inventory and risk assessment for non-human identities

Pricing

Free tier available. Team and Business plans for growing organizations. Enterprise pricing customized for large deployments.

Enterprise fit

Excellent for organizations with 500+ developers, multi-cloud environments, or regulated industries where audit trails and compliance matter. The only secrets security platform purpose-built for complete detection, remediation, and governance at enterprise scale.

Website

https://www.gitguardian.com

Gitleaks (Open Source Tool)

Gitleaks is a popular open source tool for scanning git repositories. While it provides basic scanning capabilities, it lacks the enterprise governance and remediation features needed for production secrets security programs at scale.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Completely free and open source	No validity checks - Can't verify if secrets are active
Easy to get running quickly	High false positives - Entropy detection creates noise
Strong community-contributed rule sets	No incident management UI - Detection only, no workflows
Good for baseline scanning without vendor lock-in	No remediation automation - Zero help with fixing issues
	No real-time alerting - Misses developer notifications
	Limited scanning scope - Can't scan Docker, Slack, Jira, logs
	Single server limitation - No distributed scanning
	No enterprise governance - Missing SSO, RBAC, audit trails
	Software maintenance slowing - Development pace has decreased

Pricing

Free and open source. Some vendors wrap Gitleaks in commercial offerings with added features.

Enterprise fit

Works as a starting point for evaluating risk or as a supplementary scanner. Organizations serious about secrets security need platforms like GitGuardian that provide complete incident lifecycle management, remediation automation (saving 23+ hours per team weekly), and enterprise governance at scale.

Website

https://github.com/gitleaks/gitleaks

Truffle Security Enterprise

Truffle Security is known for git repository scanning with entropy-based detection. While it offers basic scanning capabilities, it lacks the enterprise remediation features and accuracy needed for production secrets security programs.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Open source core with community support	Scans entire repos vs deltas - Longer mean time to detection (MTTD) and higher resource consumption
Entropy detection catches some custom secrets	800+ detectors overwhelm teams - Creates significant alert fatigue and noise
Forager provides limited public monitoring (1 year coverage)	No ML-powered false positive reduction - vs GitGuardian's 50% FP reduction through machine learning
	No generic secrets detection - Missing passwords and usernames detection
	No breach detection capabilities - No Honeytoken support
	Limited public monitoring - Only 1 year coverage, no identity stitching (vs GitGuardian's 6-8+ years)
	No developer-centric remediation playbooks - Missing guided workflows
	Basic scanning tool - Detection only, not a complete remediation platform

Pricing

Open source core with paid enterprise features and support.

Enterprise fit

Works as a basic scanning tool for historical audits. TruffleHog detects but leaves you to figure out remediation and buries you in false positives along the way. Organizations serious about secrets security need platforms like GitGuardian that provide ML-powered accuracy, complete incident lifecycle management, and automated remediation workflows.

Website

https://trufflesecurity.com/trufflehog

GitHub Advanced Security

GitHub Advanced Security (GHAS) is GitHub's native security offering that provides code scanning, secret scanning, and dependency review capabilities. As part of the GitHub ecosystem, it offers integrated security features for organizations standardized on GitHub Enterprise.

Core features:

• Secret scanning for GitHub repositories with 160+ detectors
• Push protection to prevent secrets from being committed
• Historical scanning of Git repository history
• Native integration with GitHub's security dashboard
• Custom pattern support via regular expressions

Pros & Cons

✅ Pros	❌ Cons
Native GitHub Enterprise integration	GitHub-only - No GitLab, Bitbucket, or Azure DevOps support
Built-in push protection	Weak generic detection - 7 detectors (beta), high false positives
SAML SSO and enterprise auth	No governance - Missing incident assignment and playbooks
SIEM integration (Splunk, Sentinel)	No pre-commit/pre-push hooks - Server-side only
Unified security dashboard	No CI/CD integration - Can't scan Jenkins, CircleCI, etc.
Audit logs for compliance	Limited validity checks - Only 4 token types vs 317+
Available for on-premises	No severity scoring - Manual prioritization required
	No sensitive file detection - Misses .env, .pem files
	Repositories only - Can't scan Docker, logs, Slack, Jira
	No incident management - Missing timeline and workflows
	Repository-level grouping - Duplicate alerts across repos

Pricing

Priced per active committer on a monthly or annual basis. Requires GitHub Enterprise Cloud or GitHub Enterprise Server. Contact GitHub sales for enterprise pricing.

Enterprise fit

Works well for organizations fully committed to the GitHub ecosystem who want basic secrets detection as part of their existing GitHub investment. However, enterprises with multi-platform development environments, complex remediation workflows, or requirements for comprehensive secrets governance across their entire attack surface will find significant gaps in coverage and capabilities. Most security-mature organizations supplement GHAS with specialized secrets security platforms to address these limitations.

Website

https://docs.github.com/en/code-security/secret-scanning

Core features:

Aikido Security

Aikido is a unified application security platform that combines secrets detection with SAST, SCA, and other AppSec capabilities in one tool.

Core features:

Pros & Cons

✅ Pros	❌ Cons
All-in-one approach reduces tool sprawl	Secrets detection depth - May not match specialized tools
Fast setup and decent coverage	Trade-off between breadth and depth
Good for teams wanting consolidation over specialization

Pricing

Tiered SaaS plans based on team size and feature needs.

Enterprise fit

Strong option for mid-market teams that prioritize unified tooling and want good-enough secrets coverage alongside other AppSec scans.

Website

https://www.aikido.dev

Yelp - Detect Secrets

Detect Secrets (originally built at Yelp) is a lightweight scanner focused on preventing commits with secrets before they hit your repository. While useful for basic pre-commit enforcement, it lacks enterprise capabilities.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Simple and developer-friendly	Only 20 secret types - vs 550+ in GitGuardian
Great for pre-commit enforcement	No VCS integrations - No GitHub, GitLab, Bitbucket, Azure DevOps
Baseline approach focuses on new issues	No incident management interface - Zero UI or workflow capabilities
	No incident lifecycle management
	No remediation playbooks - Detection tool only
	No real-time alerting - Missing email notifications
	No collaboration features - No developer workflows
	No enterprise features - No SSO, RBAC, audit logs, or API
	Limited scanning - Can't scan Docker, Slack, Jira, logs
	Detection only - No help with fixing issues

Pricing

Free and open source.

Enterprise fit

Useful for team-level enforcement and developer education. Not sufficient alone for enterprise-wide secrets governance. Best used as a prevention layer in combination with platforms like GitGuardian for comprehensive detection and remediation.

Website

https://github.com/Yelp/detect-secrets

 Spectral (Checkpoint)

Spectral, acquired by Check Point, combines AI-assisted detection with developer-focused workflows. While it offers IaC security features, it requires significant custom development for VCS integrations and lacks native enterprise capabilities.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Strong IaC security focus alongside secrets detection	Custom integration required - Must build own GitHub app and AWS Lambda functions
CI/CD integration support	No validity checks - vs 550+ types in GitGuardian
Natural fit with Check Point products	No presence checks - Can't verify secret removal from git history
	No Docker scanning - Missing container image support
	No native Slack/Jira scanning - Must build custom connectors
	No Bitbucket or Azure DevOps - Limited VCS support
	No post-receive hooks - Missing continuous monitoring
	No native PR check runs
	Incomplete unified view - Relies on user declarations
	No restricted developer view - Missing scoped access

Pricing

SaaS model with enterprise bundle options.

Enterprise fit

Suitable for organizations with IaC security needs who can invest engineering resources in building custom integrations. Organizations needing native VCS integrations, comprehensive incident management, and automated remediation typically choose platforms like GitGuardian that provide these capabilities out-of-the-box.

Website

https://spectralops.io

HashiCorp Vault Radar

Vault Radar is HashiCorp's dedicated secrets scanning and discovery product, separate from HashiCorp Vault itself (which GitGuardian integrates with for remediation). Radar finds unmanaged or leaked secrets across developer tools and repositories.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Strong fit for HashiCorp-standardized organizations	Better as extension of HashiCorp ecosystem
Supports hybrid deployment models	Public monitoring and SDLC-wide scanning - May lag best-of-breed specialists
Natural integration with Vault for remediation

Pricing

Commercial SaaS as part of HashiCorp Cloud Platform (HCP). Enterprise and tiered pricing models.

Enterprise fit

Best for large, complex organizations with multi-cloud and multi-repository environments that already rely on HashiCorp Vault and want integrated secrets discovery plus lifecycle governance.

Website

https://www.hashicorp.com/products/vault/hcp-vault-radar

GitLab Secret Detection

GitLab Secret Detection uses an analyzer containing the Gitleaks tool to scan repositories. While convenient for GitLab users, it's limited to GitLab-only environments and lacks the depth needed for enterprise secrets security.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Seamless UX for GitLab users	GitLab-only - No GitHub, Bitbucket, Azure DevOps coverage
Simple enablement with GitLab Ultimate	Only 90 secret types - vs 550+ in GitGuardian
No additional vendor complexity	False-positive prone entropy detection - No pre/post-filters
	Very limited generic detection - Only API keys with "API-" prefix
	Cannot deactivate detectors - No CLI or UI control
	No sensitive file detection - vs 22 file names + 14 extensions in GitGuardian
	GitLab pipelines only - vs 8 providers in GitGuardian
	No PR check runs - Missing merge request scanning
	No incident management - Missing severity scoring, assignment, status tracking
	No automated remediation - Missing playbooks and developer feedback loops

Pricing

Included with GitLab Ultimate subscription.

Enterprise fit

Strong choice if GitLab is your central VCS and you don't need scanning outside that ecosystem. Works best when paired with additional tools for non-GitLab environments.

Website

https://docs.gitlab.com/ee/user/application_security/secret_detection/

Cycode

Cycode positions itself as a complete SDLC and ASPM (Application Security Posture Management) platform. While it offers broad security coverage, its secrets detection capabilities are secondary to its platform approach and lack key features that enterprises need for comprehensive secrets security.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Unified platform for consolidated tooling	Far fewer detectors - vs GitGuardian's 550+ detectors
ASPM-ready with native integrations	No validity checking - Cannot verify if secrets are active
Decent multi-source coverage (Slack, Jira, S3)	No breach detection - Missing Honeytoken support
Full VCS integration support	No public GitHub monitoring - GitGuardian scans 6-8+ years
	No generic secrets detection - Missing passwords and usernames
	No NHI governance - Missing secret inventory and risk assessment
	No secrets manager integration - Cannot automate remediation
	No customizable remediation guidelines - Missing UI customization
	Full developer onboarding required - vs GitGuardian's external incident links
	Cannot verify secret removal - Missing git history verification
	No developer feedback collection - Orchestration workflows incomplete

Pricing

Commercial SaaS with enterprise tiering.

Enterprise fit

Suitable for organizations prioritizing unified SDLC platforms over best-of-breed secrets security. However, enterprises serious about secrets governance typically choose specialized platforms like GitGuardian.

Website

https://cycode.com

Pricing: Commercial SaaS with enterprise tiering.

Enterprise fit: Suitable for organizations prioritizing unified SDLC platforms over best-of-breed secrets security. However, enterprises serious about secrets governance typically choose specialized platforms like GitGuardian.

Website: https://cycode.com

Legit Security

Core features:

Pros & Cons

✅ Pros	❌ Cons
Comprehensive SDLC-wide view	Secrets detection depth varies - vs specialized tools
Strong fit for governance at scale	Pipeline security focus - Not secrets-first approach

Pricing

Enterprise SaaS pricing.

Enterprise fit

Excellent for AppSec teams that need to govern CI/CD pipelines at scale and want secrets detection integrated into that broader mission.

Website

https://www.legitsecurity.com

Aqua Security Trivy

Trivy is a widely-used open source scanner that covers containers, infrastructure as code, and secrets detection. It's become a standard tool in cloud-native environments.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Excellent for containerized and Kubernetes workloads	Secrets detection not as deep - vs dedicated secrets scanners
Broad coverage across multiple security domains	Better as part of layered approach - Not standalone secrets solution
Strong community support

Pricing

Free and open source core. Aqua offers enterprise versions with additional features and support.

Enterprise fit

Great for cloud-native and Kubernetes-heavy teams. Should be paired with dedicated secrets scanning for comprehensive coverage across the SDLC.

Website

https://trivy.dev

Prisma Cloud (Palo Alto Networks)

Prisma Cloud is Palo Alto Networks' comprehensive CNAPP platform. It includes secrets detection as part of its code-to-cloud security posture management.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Enterprise-grade CNAPP with secrets detection bundled	Secrets detection is one capability among many
Strong compliance and audit capabilities	Less specialized - For git history analysis and developer workflow remediation

Pricing

Commercial SaaS with enterprise CNAPP pricing models.

Enterprise fit

Very large organizations buying comprehensive CNAPP/CSPM solutions who want decent secrets detection bundled with broader cloud security.

Website

https://www.paloaltonetworks.com/prisma/cloud

SentinelOne

SentinelOne is primarily an endpoint detection and XDR platform with growing capabilities around identity protection and credential security.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Strong SOC and security operations integration	Not a purpose-built secret scanner
Good for credential protection via XDR stack	Lacks coverage and depth - vs dedicated secrets detection tools

Pricing

Enterprise security bundles, typically sold as part of broader XDR/EDR packages.

Enterprise fit

Works for organizations where secrets risk is one component of a broader security operations strategy that's managed through a unified platform.

Website

https://www.sentinelone.com

Cider Security (Prisma Cloud)

Cider Security is an ASPM platform that aggregates security signals from multiple scanners into unified risk views that include secrets detection.

Core features:

Pros & Cons

✅ Pros	❌ Cons
Centralized view reduces alert fatigue	Secrets detection not the primary focus
Includes secrets alongside CI/CD, SAST, SCA, IaC signals	Only valuable as orchestration layer - Requires existing scanners

Pricing

Commercial SaaS with enterprise ASPM/Prisma tiering.

Enterprise fit

Large enterprises standardizing on Prisma Cloud who want secrets detection inside an end-to-end ASPM orchestration approach.

Website

https://www.paloaltonetworks.com/prisma/cloud/cloud-code-security

How to Choose the Right Secret Scanning Tool for Your Organization

The right tool depends on your organization's size, technical stack, and security maturity. Here's a practical framework:

For Startups and Small Teams

Focus on ease of use and getting CI guardrails in place quickly. Open source secret scanning tools like Gitleaks or Detect Secrets give you baseline protection without budget pressure. Pair them with native tools from your VCS provider.

The goal is to prevent accidental exposure without creating friction that slows shipping. Developer education matters a lot more than elaborate governance workflows.

For Mid-Market Organizations

Integration depth and manageable false positives become critical. You need coverage beyond just git repositories (like Jira, Slack, container images). Tools like GitGuardian, Spectral, or Aikido provide breadth without overwhelming small security teams.

Look for automated remediation guidance and reasonable alert volumes. At this stage, you're balancing protection with team capacity. A tool that finds everything but requires manual triage of 1,000 alerts per week just isn't sustainable.

For Enterprise

Governance, policy enforcement, multi-cloud support, and multi-repository scale become non-negotiable. You need centralized visibility, role-based access, audit trails, and the ability to handle hundreds of repositories across different VCS platforms.

Enterprise secret scanning tools need to support prevention (pre-commit and CI/CD scanning) and detection (historical and public real-time monitoring).

These organizations typically need platforms like GitGuardian, HashiCorp Vault Radar, or comprehensive ASPM solutions that include secrets as part of broader application security governance.

Evaluation Checklist

When comparing tools, prioritize these factors:

• Detection quality: Test accuracy on your actual codebases. What's the true positive rate? How well does it catch generic secrets like hardcoded passwords?
• False positive ratio: Alert fatigue kills security programs. How much noise does the tool generate? Does it learn from your environment?
• Integration breadth: Can you scan code on all your VCS platforms? What about CI/CD, containers, IaC, and collaboration tools?
• Remediation depth: Does it just alert, or does it guide developers through rotation? Can it create tickets automatically? Does it integrate with your secrets manager?
• Reporting and compliance: Can you prove continuous monitoring for auditors? Are there dashboards showing exposure trends over time?
• Public repository monitoring: Does it catch secrets leaked in personal repos by your employees? This is often a huge blind spot.
• Non-human identity coverage: Does the tool help you inventory and manage service accounts, API keys, and machine identities?

Implementation Strategies and Best Practices

Yes, having the right tool matters, but implementation determines whether it actually protects you. Here's how to roll out secret scanning for coverage you can trust:

Layered Rollout Strategy

Here’s a step-by-step approach to rolling this out:

1. Start with developer pre-commit hooks using tools like Git-Secrets or Detect Secrets. This catches secrets before they hit your repository and creates immediate feedback for developers.
2. Add repository-level mandatory scans that run on every push. This is your safety net when local hooks get bypassed.
3. Implement CI/CD pipeline scans that check for dynamic secrets in build artifacts, configuration files, and deployment packages. Secrets don't always live in source code—they hide in Docker images and IaC templates too.
4. Extend to artifact scanning for containers and infrastructure code. Tools like Trivy or Bridgecrew handle this well.
5. Enable public repository monitoring to catch secrets leaked in personal repos. Developers often copy code from work to personal projects without realizing they've included credentials.

Process Best Practices

Get this right, and you’ll turn potential disaster into controlled containment:

• Define your policy clearly. What counts as a secret? Are test credentials excluded? Do expired API keys trigger alerts? Make these decisions upfront to avoid confusion later.
• Set ownership. Who triages alerts: AppSec, IAM, DevOps? Who's responsible for rotation? Unclear ownership leads to ignored findings.
• Automate critical workflows. High-severity leaks should create tickets automatically and notify the right teams. Speed matters when credentials are exposed.
• Train developers on secure alternatives. Scanning finds problems, but education prevents them. Teach developers how to safeguard sensitive data with secrets managers, environment variables, and proper key management.
• Track metrics that matter. Mean time to remediation, recurrence rates, and exposure trends over time. These metrics show whether your program is actually reducing significant security risks.

Common Pitfalls to Avoid

Mistakes happen, but knowing the most common ones is the first step in preventing them:

• Alert fatigue from false positives kills adoption. If developers ignore your alerts because 80% are noise, the tool isn't helping. Prioritize accuracy and tune aggressively.
• Bypassable controls create false confidence. Pre-commit hooks are great, but can be disabled. Don't rely on them alone—layer multiple automated security checks.
• No rotation playbook means detected secrets stay valid. Finding a leaked key is only valuable if you can rotate it quickly. Document the process and automate where possible.

The Future of Secret Scanning

Times are changing, and these are the trends reshaping secrets detection heading into 2026:

1. AI-assisted detection and validation is moving beyond basic pattern matching. Machine learning models can understand context, reduce phantom detections, and even suggest remediation approaches based on similar past incidents. We're seeing this already in tools like GitGuardian and Spectral, but expect it to become standard across the category.
2. Expansion into containers and infrastructure as code reflects where secrets actually live. Developers increasingly define infrastructure through code, and every Terraform template or Kubernetes manifest can contain hardcoded credentials. Secret scanning is expanding to meet the reality of modern development.
3. Non-human identity governance is becoming core to secrets security. Organizations are realizing that service accounts, API keys, and machine identities far outnumber human credentials. Managing and monitoring these NHI secrets is non-negotiable, and you need a dedicated non-human identity secret scanning and governance tool.
4. Continuous monitoring beyond VCS because git repositories are just one leak vector. Secrets show up in Jira tickets, Slack messages, wikis, shared documents, and monitoring tools. Comprehensive protection means scanning everywhere credentials might land (not just where code lives).

The goal isn't perfect detection—that’s impossible. It's more about reducing the window of exposure to the point where attackers can't reliably exploit leaked credentials. That means faster detection, automated remediation, and reliable governance that guarantees secrets don't stay valid long enough to cause damage.

More Security, Less Exposed Secrets

Credential leaks remain one of the most straightforward paths to a serious data breach. Secret scanning tools exist to catch those leaks before they're exploited, but not every tool will be the right fit for your use case.

Specialists like GitGuardian and TruffleHog offer deep detection and enterprise governance, while platform-native tools like GitHub Advanced Security and GitLab Secret Detection work well if you're tightly coupled to those ecosystems. Then, there are cloud-provider options like AWS Secrets Manager and Azure Key Vault that handle specific environments but need complementary scanning for broader SDLC coverage.

The right approach usually involves layering: 

• A best-of-breed scanner for comprehensive detection and governance
• Integrated with your VCS platform's native capabilities
• Connected to your secrets manager for remediation

GitGuardian is built for this problem: catching leaked credentials across your entire development environment and helping teams fix them before attackers can exploit them.

Start for free today or schedule a demo with our team.

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