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@PraeSynBH PraeSynBH released this 25 Oct 04:33
· 1 commit to master since this release
aimf-v1.0.12
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AI Management Framework (AIMF) v1.0.12

First public release of the AIMF by PraeSyn, LLC — a control-based instrument for assessing and
improving how an organisation governs, manages, maps and measures its AI systems.

  • Page: https://www.praesyn.com/aimf
  • Licence: CC BY-NC-ND 4.0 (see documents/AIMF-Full-Documentation/LICENSE.md)
  • Build: from the source tree tagged aimf-v1.0.12, which is the commit carrying its own
    publication gate record (validation/aimf-publication-gate-record-2026-10-23.json, gate CLEAR under
    the publication amendment of 2026-10-23).

Assets

Asset Bytes sha256
AIMF-Full-Documentation-v1.0.12.zip 1,196,412 e99c07d1402f812c4618119bbab9cf16c6a583d46b2693af8c92aba9507c7e6d
AIMF-Full-Documentation.zip 1,196,412 e99c07d1402f812c4618119bbab9cf16c6a583d46b2693af8c92aba9507c7e6d

Both assets are the same bytes; the unversioned name is a stable "latest" pointer. The two archives
differ only by name — verify with shasum -a 256.

What's in the framework

  • 72 controls across four dimensions (GOVERN / MANAGE / MAP / MEASURE), with a 0–4 / ML1–ML5
    maturity scale and per-control behavioural anchors.
  • A question bank — one question, one evidence expectation and five anchors per control.
  • Six selection profiles (Foundational, Balanced, Healthcare, Financial Services, Government,
    High Assurance) and five maturity-level guides.
  • Standards mappings: NIST AI RMF 1.0, ISO/IEC 42001:2023, ISO/IEC 27001:2022 (Partial), and
    Executive Order 14179.
  • The assessment guide and scoring protocol, the instrument-validation record, and the shipped
    tooling (scripts/aimf_mappings.py) that regenerates and checks the mapping tables.

Honest limitations

  • The ISO/IEC 27001:2022 mapping is Partial: 54 of 72 controls carry a mapping tag, 18 are
    recorded as no-analogue. It is not clause-complete and is not a statement of conformity.
  • Reproducibility holds within one model family, not across vendors: on the held-out case
    AIMF-VAL-2026-B, two independent blind sessions of one model agreed on 65 of 72 controls (90.3%);
    two different vendors agreed on 43 of 72 (59.7%). Quote 65 of 72 with that qualifier.
  • AIMF is not a certification and produces no attestation, certificate or audit opinion.

Attribution

This work uses the AI Management Framework (AIMF) by PraeSyn, LLC,
licensed under CC BY-NC-ND 4.0.
https://www.praesyn.com/aimf
Assets 4
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