Most teams discover a credential leak during incident triage, not from their scheduled code scan. Three patterns sink otherwise solid programs: tokens living in Git history long after a "rotation," client bundles shipping publishable and sometimes secret keys, and CI logs that print credentials the moment a test fails. You think you know where your secrets live until a debug flag exposes them.
The scale of the problem is documented. GitGuardian's State of Secrets Sprawl found 23.8 million secrets leaked on public GitHub repositories in 2024, a 25 percent jump year over year, and roughly 70 percent of secrets leaked in 2022 were still active two years later. Detection was never the hard part. Rotation and revocation are.
AI-assisted development has made it worse in a specific, predictable way. Coding assistants default to importing client SDKs directly into the frontend, which ships keys to every visitor, and they bake credentials into commits that reviewers wave through because the output looks competent. Gartner's 2026 cybersecurity trends flag exactly this: developers deploying through no-code platforms and vibe coding at a pace that outstrips security governance.
Credential abuse still appears in 39 percent of breaches in Verizon's 2026 DBIR, which is why the useful question is not how many matches a scanner produces but how many live credentials it gets revoked. This guide compares Keygraph, Cyscale, GhostCred, and GetLeaked on validation, context, and where each one actually looks.
Secrets Scanning Tools at a Glance
| Tool | Best for | Pricing model | Standout |
|---|---|---|---|
| Keygraph | Enterprise AppSec teams needing validated, low-noise findings | Custom quote | Runs entirely inside your own cloud account, air-gap capable |
| Cyscale | Teams wanting leaks prioritized by live cloud context | Custom quote, AWS Marketplace | Ties a leaked key to the workload, identity, and owner it affects |
| GhostCred | Fast verified reports across code and cloud | Custom quote | Emphasis on verified findings and plain-English risk reports |
| GetLeaked | Pre-launch checks on public app surfaces | Not published | Targets exposed keys in client bundles and responses |
How We Evaluated These Tools
Match counts are the wrong metric, so the assessment focused on what determines whether a leak actually gets closed:
- Validation before alerting: whether a candidate match is confirmed as a live secret, since unvalidated regex hits generate rotations nobody completes.
- Scope beyond Git: client bundles, CI logs, container images, and cloud configs, because a code-only scanner misses where modern leaks actually surface.
- Commit history depth: whether the tool reads history or only the current state, given that deleting a file does not remove the secret.
- Prioritization context: identity scope, runtime exposure, and blast radius, which decide what gets rotated first.
- Ownership routing: whether a finding reaches the person who can rotate it without a manual hunt.
- Deployment fit: self-hosted, in-VPC, or air-gapped options for teams whose policy rules out SaaS.
Our Top Picks for Secrets Scanning
1. Keygraph
Keygraph is a continuous agentic pentesting platform whose secrets scanning module finds leaked credentials, tokens, and API keys across code and commit history, then validates, deduplicates, and prioritizes them by blast radius. Its defining trait is deployment: the whole platform runs inside your own cloud account.
Best for: Security teams that want code-aware detection plus validation to cut false positives, particularly regulated organizations that need air-gapped or in-VPC operation.
Key features:
- Two-phase detection that flags candidates by pattern, then re-reads them with model inference and surrounding code context to confirm real secrets
- Classification by type, scope, and blast radius across full repository history
- Deduplication and false-positive carry-forward so the same finding does not resurface every scan
- Deploys entirely inside your AWS, GCP, or Azure account, with no managed control plane and no externally operated data plane; source, scan results, and AI inference stay inside your perimeter
- Fully air-gapped operation with no outbound calls required, plus SAML 2.0 or OIDC sign-in and SCIM provisioning
- Integrations across GitHub, GitLab, Azure DevOps, Jira, Slack, Docker Hub, GHCR, Amazon ECR, and Google Artifact Registry
Why we like it: The validation pass is what separates a useful tool from a noise generator, and the blast-radius view tells you which tokens still reach production so rotation work closes real risk. The deployment model is the sharper differentiator though.
Sending your source code and scan results to a vendor's control plane is exactly the exposure you are trying to fix, and running inference locally removes that objection before procurement raises it. Secrets scanning also sits alongside SAST, SCA, and agentic pentesting on the same platform, so findings arrive with attack context rather than in isolation.
Limitations:
- Limited independent reviews and public benchmarks as of July 2026, with a small third-party footprint next to established AppSec vendors
- Bring-your-own-model endpoints add token costs and, in many enterprises, an internal AI approval cycle before rollout
- Secrets scanning is one module in a broader pentesting platform, so confirm what your quote covers if that is all you need
- Self-hosted deployment means you own the infrastructure it runs on
Pricing: Not publicly available. Self-hosted and air-gapped options are described in vendor materials. Contact Keygraph for a quote.
2. Cyscale
Cyscale is an agentless CNAPP that detects leaked secrets alongside outdated software, dependency risk, and container image findings, then connects each one to the cloud and runtime assets it actually affects. It covers AWS, Azure, Google Cloud, and Alibaba Cloud.
Best for: Teams that need secrets detection tied to live cloud context, identities, and owners so they can prioritize what still matters in production.
Key features:
- Secret detection across repositories, artifacts, and delivery workflows with deduplication
- Container image scanning that finds vulnerable packages, malware, secrets, and misconfigurations inside build artifacts before those workloads run
- Contextual prioritization by blast radius, exposure, and fix urgency rather than flat severity
- Identity and entitlement context through CIEM, so a leaked key is assessed by what it can actually reach
- Agentless onboarding through provider APIs, with a Kubernetes agent only where cluster runtime inventory requires it
- Compliance evidence mapped to ISO 27001, SOC 2, PCI DSS, CIS, and NIST, plus AI security posture management for AI assets
- Available through AWS Marketplace, including private offers
Why we like it: The value is connecting a leaked token to the running workload and the person who owns it, then closing the loop with rotation and verification.
A key with no live permissions and a key with production write access are the same string to a regex scanner and completely different problems to a security team; Cyscale is built to tell them apart. The agentless model also means you can get coverage without a deployment project.
Limitations:
- Small third-party review footprint, roughly 6 reviews at a 4.8 average on G2, far below larger CNAPP peers
- CNAPP breadth adds onboarding effort if secrets scanning is genuinely all you want
- Positioned primarily for SMEs, so validate fit if you are running a large multi-account estate
- No publicly documented on-premise or air-gapped option
Pricing: Not publicly available. Transactable via AWS Marketplace private offers, or contact Cyscale directly.
3. GhostCred
GhostCred is positioned as AI-powered credential and secret leak scanning across repositories, configs, and cloud environments, producing verified risk reports quickly.
Best for: Engineering-led teams that want fast verification and a short path from finding to an evidence-based rotation plan.
Key features:
- Scans repositories, configs, and cloud for API keys, service accounts, and OAuth tokens
- Emphasis on verified findings rather than raw pattern matches
- Plain-English risk reports aimed at getting owners to act
- API access for pipeline integration, per product documentation
Why we like it: The emphasis on verified, high-signal reporting addresses the real bottleneck, which is convincing a service owner to rotate a credential now instead of next sprint. A report that proves a key is live is a different conversation from a list of suspicious strings.
Limitations:
- No independent reviews, analyst coverage, or third-party references were verifiable as of July 2026, so request customer references before committing
- No public benchmark or head-to-head detection data exists, making a proof-of-value test essential rather than optional
- Product scope and roadmap are documented only in vendor materials
- Pricing is opaque, so budget planning requires a sales conversation
Pricing: Not publicly available. Contact GhostCred for a quote.
4. GetLeaked
GetLeaked scans public app surfaces for hard-coded secrets and exposed API keys, returning evidence-backed findings with remediation guidance. It focuses on what an attacker can already see in responses and client bundles.
Best for: Early-stage web and AI app teams that need a fast pre-launch check for exposed keys in public assets.
Key features:
- Public-surface scanning across client bundles, source maps where exposed, and responses
- Checks for hard-coded keys, secrets in logs, and direct AI provider calls made from the browser
- Row Level Security checks for backend-as-a-service configurations where an anon key sits in front of unprotected tables
- Evidence-backed reports with fix steps written for non-security teams
Why we like it: It targets the mistakes that most reliably leak spend or data in public deployments, which makes it a sensible launch gate. This is also precisely the failure mode AI coding tools produce at scale: a client SDK imported into the frontend ships its key to every visitor, and an LLM provider key exposed that way means your token billing is now open to the internet.
Limitations:
- No independent reviews or third-party coverage were verifiable as of July 2026; treat vendor claims as unconfirmed
- Scope is deliberately narrow to public surfaces, so Git history and internal systems need a complementary tool
- Pricing is not published on third-party sources, and free-scan terms should be confirmed rather than assumed
- Open-source and free alternatives exist in this specific niche, so benchmark before paying
Pricing: Not publicly available on third-party sources. Contact GetLeaked for current pricing.
Detection and Validation Comparison
| Tool | Validation approach | Commit history | Scope beyond Git |
|---|---|---|---|
| Keygraph | Pattern match plus model confirmation in code context | Full history | Container registries and artifacts |
| Cyscale | Deduplication with cloud-context confirmation | Yes | Cloud configs, artifacts, container images |
| GhostCred | Verified risk report workflow | Yes | Configs and cloud environments |
| GetLeaked | Evidence capture with proof notes | Current state, not deep history | Client bundles, source maps, responses |
Deployment and Verification
| Tool | Deployment | Air-gap capable | Public footprint |
|---|---|---|---|
| Keygraph | Self-hosted inside your own cloud account | Yes, no outbound calls required | Limited, small third-party presence |
| Cyscale | Agentless SaaS, Kubernetes agent where needed | Not publicly documented | Small but positive, ~6 G2 reviews |
| GhostCred | Cloud, API integration | Not stated | Not independently verifiable |
| GetLeaked | Cloud, URL or repo input | Not applicable | Not independently verifiable |
Strategic Decision Framework
| Critical question | Why it matters | What to evaluate | Red flags |
|---|---|---|---|
| Does it validate secrets before alerting? | Unvalidated matches produce rotations nobody finishes | Live validation step, evidence quality, rate-limited verifiers | Pure regex counts with no proof of exploitability |
| Can it see beyond Git into bundles and logs? | AI-assisted development leaks through the frontend, not the repo | Bundle and response analysis, CI log hooks, artifact scanning | Code-only scope in an AI-assisted pipeline |
| How are findings prioritized? | You need to fix what still reaches production first | Identity scope, service importance, external exposure, blast radius | Flat severity with no runtime context |
| Does the finding reach an owner? | Detection without routing just moves the backlog | Ownership mapping, ticketing integration, rotation tracking | Findings that land in a dashboard nobody owns |
| Will deployment fit your constraints? | Sending source code to a vendor is its own exposure | Self-hosted, in-VPC, air-gapped, where inference runs | SaaS-only when policy requires in-VPC |
| Can you verify the vendor independently? | This category has several thin public footprints | Customer references, published docs, benchmark data | Nothing verifiable beyond the vendor's own site |
Problems & Solutions
-
Problem: Secrets persist in Git history and stay active long after a rotation that everyone believed was complete.
Solution: History scanning plus validation is the only combination that closes this. Deleting a file does not remove the secret from the commit graph, and GitGuardian's finding that roughly 70 percent of secrets leaked in 2022 remained active two years later shows how rarely rotation actually finishes. Keygraph scans code and full commit history and classifies blast radius so teams can prioritize tokens that still reach production. Cyscale ties a leaked key to the cloud identity and workloads it still controls, then routes rotation with owner context. -
Problem: AI coding assistants ship keys into the frontend, and reviewers wave the code through.
Solution: This is the fastest-growing leak path of 2026 and it does not show up in a repository scan of server-side code. Coding assistants default to importing client SDKs directly into the browser bundle, which publishes the key to every visitor, and an exposed LLM provider key means your token billing is open to the internet. GetLeaked inspects public pages, bundles, and source maps for exactly this, and GhostCred surfaces exposed keys across repos and configs. Pair either with a review rule that treats generated code as more suspicious than human-written code, not less. -
Problem: Backend-as-a-service misconfigurations expose data even when no secret technically leaked.
Solution: An anon or publishable key is harmless only if the access controls behind it work. When Row Level Security is off or misconfigured, a key the vendor labels safe for the client becomes a data exfiltration path, and this remains one of the most common pitfalls in AI-assisted code. GetLeaked explicitly checks for RLS-off risks and public access patterns before launch. Cyscale's context view shows which exposed data paths and identities matter most once you are in production. -
Problem: Scanner output arrives as a wall of matches and the backlog never clears.
Solution: Prioritization and ownership routing do more for outcomes here than better detection. Keygraph's validation phase and deduplication cut what reaches the queue in the first place; Cyscale's exposure-aware ranking and ownership routing put what remains in front of someone who can act. Measure the program on credentials revoked, not findings generated, or you will optimize for the wrong number. -
Problem: Your policy will not allow source code or scan results to leave your environment.
Solution: This rules out most of the category, so check it early rather than after a proof of concept. Keygraph is the option here that runs entirely inside your own AWS, GCP, or Azure account with no managed control plane and full air-gapped operation, including local model inference. If you need agentless cloud coverage instead and SaaS is acceptable, Cyscale onboards through provider APIs without deploying workload agents.
The Bottom Line
The best secrets programs measure success by revoked, non-exploitable credentials in production, not by the number of matches in a report. Every tool here will find secrets. The differences are whether it proves they are live, tells you which ones still reach production, and gets the finding to someone who can rotate it.
If you need validation depth with source code that never leaves your perimeter, Keygraph is the pick, and the in-account deployment model answers a security objection most competitors cannot.
If your leaks matter mainly in terms of what they unlock in a live cloud estate, Cyscale connects the key to the workload and the owner. GhostCred and GetLeaked target speed and public-surface coverage respectively, and both are worth a proof of value given how little independent evidence exists on either.
Whichever way you go, choose the tool that validates before alerting and ties each leak to owners and live exposure. That is how detection turns into risk actually removed.
