A custom secret detection rule is a user-defined pattern used to identify organisation-specific credentials, tokens, or sensitive phrases that generic scanners may miss. These rules can be expressed as exact text or regex and then applied across repositories, logs, chat, cloud workflows, and AI agent surfaces to improve coverage.
Expanded Definition
Custom secret detection is the practice of writing organisation-specific rules to find credentials, tokens, certificates, API keys, and sensitive markers that generic scanners do not recognise. It usually extends baseline scanning by adding exact matches, regex patterns, or context-aware detections tuned to internal naming conventions, code paths, chat channels, and AI agent workflows.
Usage in the NHI domain is still evolving because different platforms expose different rule syntax, match scopes, and confidence controls. Some teams use it narrowly for source code and pull requests, while others apply it across logs, tickets, CI/CD, and agent tool outputs. The practical goal is not just broader detection, but better detection of secrets that are unique to one organisation’s systems and therefore invisible to commodity rules. That is why this capability is closely related to the OWASP Non-Human Identity Top 10 and to broader identity governance in the NIST Cybersecurity Framework 2.0.
The most common misapplication is treating custom secret detection as a one-time regex exercise, which occurs when teams add patterns without validating false positives, ownership, and rotation workflows.
Examples and Use Cases
Implementing custom secret detection rigorously often introduces tuning overhead and review burden, requiring organisations to weigh wider coverage against alert quality and maintenance effort.
- Detecting internal API token formats in source code, such as prefixes used only by one platform or product line, where generic scanners miss the pattern.
- Flagging sensitive phrases in tickets or chat messages, such as environment names combined with deployment secrets, to reduce accidental disclosure in collaboration tools.
- Scanning CI/CD variables and build logs for organisation-specific credential markers, especially when pipelines echo values during debugging or failure handling. See the Guide to the Secret Sprawl Challenge for why these exposures persist.
- Monitoring AI agent prompts, tool outputs, and retrieval traces for proprietary secret formats that can be surfaced during autonomous execution, a risk also discussed in the Top 10 NHI Issues.
- Using custom regex rules to catch environment-specific certificate labels or vault aliases that indicate a secret has been embedded outside approved storage.
For teams aligning to standards, detection logic should complement OWASP Non-Human Identity Top 10 guidance on secret hygiene and the identity lifecycle controls described in the NHI Lifecycle Management Guide.
Why It Matters in NHI Security
Custom secret detection matters because NHI compromises often begin with a credential that standard scanners failed to recognise. NHIMG reports that 79% of organisations have experienced secrets leaks, and 77% of those incidents resulted in tangible damage, which shows how quickly missed detection becomes a breach path. The issue is amplified in environments where secrets spread into code, logs, tickets, and agentic toolchains, making discovery and remediation harder after exposure.
Well-tuned custom rules can expose secrets before they are reused across service accounts, automation jobs, or AI agent actions. They also support governance by helping security teams prove that detection coverage matches the organisation’s own naming conventions and operational reality, not just a vendor’s default dictionary. That matters because secrets often survive longer than expected, even after discovery, and delayed response keeps blast radius open.
Practitioners should pair detection with ownership, revocation, and rotation workflows, because finding a secret without acting on it only documents exposure. Organisational exposure becomes especially visible after a leak, a failed pipeline, or an agent event, and at that point custom secret detection becomes operationally unavoidable to contain the compromise.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 | Secret discovery and leakage prevention are core NHI secret management concerns. |
| NIST CSF 2.0 | PR.DS-1 | Data-at-rest protection includes detecting sensitive secrets where they are stored or exposed. |
Tune custom secret rules to find org-specific credentials, then route matches into revoke-and-rotate workflows.
