Better secrets detection starts by filtering non-secrets

At a glance

What this is: This is an analysis of why generic secrets scanners flag public-by-design credentials and how context-aware suppression reduces noise.

Why it matters: It matters because IAM and AppSec teams need to separate real credential exposure from publishable tokens, or they waste effort, slow triage, and miss the secrets that actually expand attack paths.

By the numbers:

• The new publishable-credential signatures cut false positives by 2.37%.
• We scanned 100GB from Common Crawl twice with Betterleaks.

👉 Read Aikido's analysis of better generic secrets detection and false positive reduction

Context

Generic secrets detection fails when a scanner treats every high-entropy string as if it were a credential with the same operational meaning. Some tokens are intentionally public, some are sensitive only in specific configurations, and some are true secrets. For IAM and AppSec teams, the governance problem is not just finding more secrets, but separating exposure that changes access risk from noise that does not.

This matters for secrets management because false positives are not a cosmetic issue. They consume analyst time, distort remediation priorities, and can hide the real identity and access controls that need attention, including service account governance, API key handling, and lifecycle control for credentials embedded in code and pipelines.

Key questions

Q: How should security teams reduce false positives in generic secrets scanning?

A: Start by removing credential types that are public by design, then add contextual checks for keys whose risk depends on platform permissions or surrounding code. The goal is not to suppress more findings overall, but to preserve analyst time for credentials that can actually be used to access sensitive systems.

Q: Why do publishable credentials create problems for secrets governance?

A: Because they often look identical to real secrets, yet they are not always sensitive. Without context, a scanner cannot tell whether the value is meant to be exposed or whether it grants access that could be abused. That ambiguity inflates noise and weakens prioritisation.

Q: What do teams get wrong about secrets scanning at scale?

A: They treat scanning as the control instead of one input to lifecycle governance. At scale, the harder problems are ownership, rotation, revocation, and central inventory. A tool that finds secrets but cannot support remediation prioritisation still leaves governance gaps unresolved.

Q: How do you know if a secrets scanning programme is working?

A: It is working when the queue contains mostly actionable exposures, not repeated false positives from known public credentials or sandbox values. The best signal is a lower review burden with the same or better detection of real secrets that would change access risk.

Technical breakdown

Why generic secret scanners produce noise

Generic secret scanners usually begin with regular expressions that match high-entropy strings, then apply a liveness check to confirm whether a candidate is live. That works well for credential types with stable formats, such as known cloud or SaaS keys. It breaks down when the scanner cannot distinguish between a real secret and a value that is public by design, context dependent, or intended only for sandbox use. In those cases, format alone is not enough to establish risk, so scanners need suppression logic or contextual rules before they escalate findings.

Practical implication: tune detection rules so analysts review real exposures, not every string that merely resembles a credential.

Why publishable credentials need context, not just matching

Publishable credentials are a useful example of why identity and access context matters in secrets scanning. A publishable key may be designed for front-end use, may share the same shape as a secret key, or may only become sensitive when a hidden permission is enabled. In each case, the scanner must know whether the key can authenticate to something privileged, not just whether it looks like a token. That requires allowlists, platform-specific signatures, or a liveness check tied to the access the credential actually grants.

Practical implication: classify credential types by privilege and exposure path, not by syntax alone.

Why filtering should happen before expensive triage

A layered secrets workflow should remove the clearest false positives first, then reserve costly analysis for ambiguous candidates. That is a resource allocation problem as much as a detection problem. If a scanner sends too many low-value findings to human reviewers, the programme slows down and genuine exposures sit longer. The article’s approach reflects a practical pattern: cheap suppression rules first, then deeper checks only where the access meaning is unclear or potentially dangerous.

Practical implication: place suppression and context filters ahead of manual review and machine-assisted triage.

NHI Mgmt Group analysis

Generic secrets detection is really a governance problem disguised as a scanning problem. The article shows that teams lose value when every credential-shaped string is treated as equally risky. For IAM and AppSec programmes, the real question is whether the value grants access, and to what. That is why secrets governance must distinguish identity-bearing secrets from public-by-design values before analysts spend time on them.

Context is now part of secret classification. A credential’s shape no longer tells the whole story when a token may be public in one product, sensitive in another, or dangerous only after a permission change. That means detection logic has to understand platform behaviour, privilege state, and deployment context. Practitioners should treat this as a control-design issue, not just a tuning exercise.

False positives weaken lifecycle control by flooding the queue. When scanners over-report, rotation, revocation, and incident follow-up lose focus because teams cannot see which findings actually map to active access. This is where secrets management intersects with identity governance: the control objective is not only finding credentials, but preserving analyst capacity for the credentials that can be used to move, persist, or exfiltrate. The better metric is actionable signal, not raw finding count.

Publishable-secret collisions create a naming problem the industry still underestimates. If the same string format can represent both benign and sensitive access, then scanners need a stronger taxonomy for non-human identities and token classes. That aligns with the OWASP Non-Human Identity Top 10 and NIST SP 800-53 Rev 5 Security and Privacy Controls, especially where authenticator handling and access control depend on accurate classification. Practitioners should expect future secrets programmes to rely more on semantic policy than on regex alone.

What this signals

Context-aware secret filtering is becoming a governance control, not just a scanner feature. As detection pipelines mature, teams will need to show that suppression rules are documented, reviewed, and tied to explicit credential classes. That makes secrets triage part of control assurance, especially where non-human identities and API keys sit inside application delivery paths.

Secret classification will increasingly depend on identity semantics. The distinction between a public token, a sandbox credential, and a live secret is not just syntactic. It is an access decision. Teams that cannot express that decision in policy will continue to over-collect findings and under-protect the credentials that matter most.

Precision becomes the operating model when secret volume rises. The practical shift is from broad scanning to narrow, validated findings that can be acted on quickly. For identity and security programmes, that means aligning secrets management with lifecycle controls, review capacity, and the actual privilege attached to each credential class.

For practitioners

• Implement platform-specific suppression rules Build denylist rules for public-by-design credential types before routing findings to analysts. Start with the most common publishable keys in your codebase and confirm each suppression still leaves genuinely sensitive variants visible.
• Separate syntax matching from privilege validation Use a second-stage check that asks whether a candidate credential can reach anything sensitive, especially when publishable and secret keys share the same format. This reduces false positives without hiding exposure that could still be abused.
• Prioritise context-rich triage for ambiguous tokens Route keys with overlapping shapes, sandbox credentials, and permission-dependent tokens into manual triage or deeper validation instead of blanket exclusion. That preserves review capacity for findings that actually change access risk.
• Track actionable signal, not raw finding volume Measure secrets programme performance by the number of confirmed exposures resolved, not by the number of strings surfaced. Pair that metric with mean time to classify ambiguous findings so filtering improvements do not hide operational delays.

Key takeaways

• Generic secrets scanning creates noise when it ignores whether a credential is public by design or actually able to grant access.
• Aikido reports a 2.37% reduction in false positives and 27,886 fewer manual reviews after adding publishable-key suppression rules.
• Secrets programmes should treat contextual classification and triage efficiency as core governance controls, not optional tuning.

Key terms

• Generic Secret: A generic secret is a credential-like string that matches a broad pattern but does not have a unique format proving what it is. In practice, scanners need additional context to decide whether it is a live secret, a public token, or a value that only looks sensitive.
• Publishable Credential: A publishable credential is a token or key that a provider intends to be exposed in user-facing code or public contexts. It may still require careful review because some products reuse shapes across public and sensitive credentials, and some keys only become risky when permissions change.
• Liveness Detection: Liveness detection is the mechanism that checks whether a biometric sample comes from a real, present person rather than a spoof such as a photo, screen, or mask. In identity programmes, it is a core defence against presentation attacks and should be tested under realistic operating conditions.
• False-positive reduction: False-positive reduction is the practice of making detection systems ignore legitimate identity activity that only looks risky in isolation. It depends on context, not just thresholds, and becomes most effective when lifecycle, workflow, and authentication signals are available to the same decision engine.

What's in the full article

Aikido's full blog covers the operational detail this post intentionally leaves for the source:

• The exact rule patterns used to suppress common publishable credentials from generic secret findings
• The five classification cases that determine whether a token needs context, liveness testing, or manual triage
• The Common Crawl scan methodology and how the 2.37% false-positive reduction was measured
• The default Betterleaks behaviour for publishable keys and how it affects analyst workflow

👉 Aikido's full post covers the rule logic, scanning workflow, and classification edge cases in detail.

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It helps practitioners connect credential lifecycle control to wider identity and access programmes.