A secrets discovery approach that uses cloud APIs, logs, and external telemetry instead of host-installed software. It scales more easily across distributed environments, but its coverage depends on the quality and completeness of the surrounding visibility layer.
Expanded Definition
Agentless secrets scanning is a discovery method for credentials, tokens, API keys, and certificates that relies on cloud APIs, audit logs, repository metadata, and other external telemetry rather than software installed on endpoints or containers. That makes it attractive for fast coverage across multicloud estates, partner environments, and ephemeral workloads where host-level collection is impractical. In NHI security, the term is narrower than general “secrets discovery” because it describes the collection plane, not the remediation workflow or the inventory system that follows.
Definitions vary across vendors on how much telemetry is enough to count as agentless, but the operational idea is consistent: the scanner has no local sensor and must infer exposure from surrounding visibility. That distinction matters because OWASP Non-Human Identity Top 10 treats secret exposure as a core lifecycle risk, while NIST AI Risk Management Framework emphasises traceability and measurement of control effectiveness. The most common misapplication is assuming agentless coverage is complete when the organisation only scans a subset of APIs, leaving chat systems, ticketing platforms, and private repositories unexamined.
Examples and Use Cases
Implementing agentless secrets scanning rigorously often introduces a visibility tradeoff: broader reach across cloud services and SaaS platforms can come at the cost of weaker signal quality, requiring organisations to balance speed of discovery against false negatives from incomplete telemetry.
- Scanning cloud object storage and source-control metadata for exposed API keys without deploying endpoint agents into every developer workstation.
- Monitoring CI/CD audit logs to detect secrets that appear in pipeline variables, build artifacts, or deployment manifests, a pattern highlighted in the CI/CD pipeline exploitation case study.
- Ingesting SaaS events from collaboration tools to find tokens pasted into tickets or messages, which aligns with findings in Guide to the Secret Sprawl Challenge.
- Reviewing Git hosting and identity provider logs to flag newly introduced secrets in pull requests, similar to the exposure patterns discussed in the OWASP Top 10 for Agentic Applications 2026.
- Using external telemetry to prioritise investigation of newly created service accounts whose credentials may already be duplicated across multiple locations.
For broader incident patterns, NHIMG research such as the State of Secrets Sprawl 2026 and 2025 State of NHIs and Secrets in Cybersecurity shows why secrets often surface outside traditional code paths and why discovery must extend beyond repositories.
Why It Matters in NHI Security
agentless scanning matters because NHI risk rarely stays inside one system. A leaked token can move from a repository into a ticket, a chat thread, or a build log, and once it is duplicated, defenders lose the assumption that revocation alone will stop use. NHIMG research in The State of Secrets Sprawl 2026 reports that 28% of secrets incidents now originate outside code repositories, and 62% of all secrets are duplicated and stored in multiple locations, which means discovery must be broad enough to catch the first and second copy. That is why agentless methods are often paired with response workflows, not treated as a standalone control.
It also fits the reality of agentic systems, where secrets can be introduced by automation faster than endpoint tooling can be deployed. Once exposure happens in a CI/CD runner, SaaS workspace, or cloud-native control plane, the organisation needs external telemetry to reconstruct blast radius and confirm whether a credential is still active. Organisations typically encounter agentless scanning as a necessity only after a secret has already been exposed in a build log or collaboration tool, at which point the visibility gap becomes operationally unavoidable to address.
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 AI RMF and 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 | Covers secret exposure and discovery gaps across NHI environments. |
| NIST AI RMF | Focuses on measuring AI system risks and control effectiveness. | |
| NIST CSF 2.0 | DE.CM-8 | Supports continuous monitoring through external data sources and telemetry. |
Scan broadly for exposed secrets and tie findings to revocation, rotation, and owner assignment.
