Probe and check coverage aligned to MS-1
1 (Approaches for AI risk measurement are documented).
Defined metrics + thresholds for accuracy, bias, robustness.
Last reviewed July 2026
In NIST AI 600-1, Approaches for AI risk measurement are documented addresses measure. Defined metrics + thresholds for accuracy, bias, robustness. Penaxtra records this control at high severity and establishes its state by exercising it against the running system, so the result reflects observed behavior rather than a documented assertion.
Penaxtra turns this NIST AI 600-1 obligation into recurring, testable evidence: scheduled scans and posture checks produce findings tied to MS-1.1, and the append-only audit log records what was tested and when. The NIST AI 600-1 MS-1.1 identifier is attached when the finding is created, so it appears in the exported evidence pack already mapped to the control. Where the same weakness maps to another framework, the finding carries those control identifiers as well.
1 (Approaches for AI risk measurement are documented).
1 identifier.
Findings for MS-1.1 carry the NIST AI 600-1 MS-1.1 identifier along with the corresponding control identifiers in the other frameworks Penaxtra maps, so one result is reflected across each mapped framework.
Defined metrics + thresholds for accuracy, bias, robustness. It is a NIST AI 600-1 control; Penaxtra assesses it at high severity.
Penaxtra turns this NIST AI 600-1 obligation into recurring, testable evidence: scheduled scans and posture checks produce findings tied to MS-1.1, and the append-only audit log records what was tested and when.
Each finding is tagged with the NIST AI 600-1 MS-1.1 identifier and exported in the PDF and JSON evidence pack, so it appears on the auditor control list with the identifier already attached.