Knowledge Settings are pre-scan instructions and supporting documentation that tell a security testing platform how an application is expected to behave. They help align automated testing with real business logic, authentication paths, and edge cases, so results are more relevant and less dependent on generic assumptions.
Expanded Definition
Knowledge Settings are the pre-scan instructions, context, and supporting documentation that tell a security testing platform how an application is supposed to behave. In practice, they narrow the gap between generic scanning and application-aware testing by describing business logic, authentication paths, data handling expectations, and edge cases that matter to a specific system.
Definitions vary across vendors, but the core idea is consistent: the scanner is not just pointed at a target, it is briefed on what “normal” looks like so findings are more actionable. This is especially important where a platform must distinguish intended controls from risky behavior, such as a deliberate bypass for service-to-service traffic or a workflow that only appears vulnerable because it uses nonstandard session handling. That makes Knowledge Settings closely related to test scope, but not the same as scope itself. For governance context, see the NIST Cybersecurity Framework 2.0 and NHIMG’s Ultimate Guide to NHIs for why context-rich identity and access detail matters in automated security workflows.
The most common misapplication is treating Knowledge Settings as a one-time setup step, which occurs when application logic, authentication flows, or environment assumptions change but the scan context is never updated.
Examples and Use Cases
Implementing Knowledge Settings rigorously often introduces maintenance overhead, requiring organisations to weigh higher scan accuracy against the cost of keeping context current as systems change.
- A platform is told which endpoints require multi-step login, so it can test authenticated paths instead of flagging every protected route as unreachable.
- Documentation explains that a specific API returns different responses for internal agents and external clients, helping the scanner avoid false positives about broken access control.
- Business rules are supplied for checkout, approvals, or token issuance flows, so the tool can validate edge cases that generic crawling would miss.
- Service account behavior and machine-to-machine trust boundaries are described in advance, improving testing of NHI-adjacent workflows that rely on non-interactive identity.
- Reference notes from an internal security review are attached so the scanner can prioritize known sensitive paths and suppress findings already accepted as compensating controls.
For identity-heavy environments, the Ultimate Guide to NHIs is a useful reference point for understanding why machine identities need explicit context, while the NIST Cybersecurity Framework 2.0 reinforces that asset and control awareness should shape testing priorities.
Why It Matters in NHI Security
Knowledge Settings matter because automated testing is only as reliable as the assumptions it receives. In NHI security, that is especially consequential: service accounts, API keys, CI/CD tokens, and agent credentials often follow workflows that look unusual to generic tools. Without the right context, scanners can miss real exposure, over-report safe behavior, or fail to exercise paths that depend on machine identity rather than human login patterns.
This becomes more important as organisations scale. NHIMG notes that only 5.7% of organisations have full visibility into their service accounts, which means testing platforms frequently operate without a complete picture of the identities and permissions they are evaluating. Knowledge Settings help close that gap by making application behavior explicit before the scan begins, but they do not replace governance, inventory, or rotation practices. They are a control input, not a control outcome.
Practitioners also use them to reduce operational noise during continuous testing, particularly where secrets, tokens, or agent permissions change faster than manual review cycles can keep up. Organisations typically encounter the cost of missing or stale Knowledge Settings only after an incident review reveals that the scanner never had the right context to detect the weakness in the first place, at which point the term 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, CSA MAESTRO and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Context-aware testing helps reveal NHI misconfigurations and auth weaknesses. |
| NIST CSF 2.0 | GV.RM-01 | Risk management depends on accurate system context and testing assumptions. |
| NIST Zero Trust (SP 800-207) | PR.AC | Zero Trust testing must model identity-specific access paths and trust boundaries. |
| CSA MAESTRO | Agentic systems require explicit operational context for safe evaluation. | |
| OWASP Agentic AI Top 10 | Agentic AI testing relies on task context, permissions, and expected behavior. |
Provide application context so automated testing can validate NHI paths and avoid generic assumptions.
