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Scope a Review
AI Control Crosswalk

SecEng Prove · Labs

ATLAS · OWASP · NIST AI RMF · ISO 42001 · AI Trust Governance

AI Control Crosswalk

Navigate AI risk across frameworks.

4Frameworks
377Mappings
377Public-safe
66%Avg confidence

One surface for OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS, and ISO 42001 — with directional mappings, evidence prompts, and scorecard bridges across all four.

Framework Crosswalk
Public-safe

AI Trust

Governance

70

MITRE ATLAS

107

OWASP LLM

269

NIST AI RMF

35

ISO 42001

Threats + Techniques
Risks
Functions + Controls
Management Themes
Dimensions
Directional only
Public-safe rationale
Evidence-informed alignment
Navigation first

Used during engagements

A public proof surface for SecEng Prove.

AI Security Sales Enablement
AI Security Operating Model
AI Product Security Assessment
Governance Evidence Pack
Customer Security Review Support

Public snapshot

Crosswalk relationships stay directional and public-safe.

Framework graph

Cross-framework signal stack

Directional mappings connect ATLAS, OWASP, NIST AI RMF, ISO 42001/AIMS, and AI Trust Governance for navigation and evidence planning.

377

mappings

Public-safe rows

Every visible mapping stays on the public side of the boundary.

377 / 377

100% signal

Framework families

ATLAS, OWASP, NIST AI RMF, ISO 42001/AIMS, and AI Trust Governance are represented.

5 / 5

100% signal

Average confidence

Directional crosswalk confidence based on the public mapping corpus.

66%

66% signal

Relation types

The public graph includes mapping, alignment, and scorecard-signal relationships.

6 / 4

100% signal

Claim posture

Directional relationships help users navigate and prioritize. They do not imply certification equivalence or compliance proof.

5 frameworks377 public-safe rows6 relation typesnavigation first

How to use it

The crosswalk is a public proof surface for Workbench-backed evidence planning, not a spreadsheet export.

Use this to translate between framework language, identify evidence gaps, and prioritize what a client should prove during a Prove-phase engagement.

Claim language stays cautious so the public surface does not overstate equivalence or maturity.

Methodology

The crosswalk uses public metadata and directional mapping logic.

Taxonomy

Framework map

Public framework metadata becomes a browsable map across ATLAS, OWASP, NIST AI RMF, ISO 42001/AIMS, and AI Trust Governance.

directionalpublic-safeevidence navigation

Taxonomy

Deterministic first pass

The graph is seeded from public framework metadata, directional crosswalks, and cautious claim language.

repeatablemetadata-drivencautious language

Taxonomy

Public signal only

The page supports navigation, cross-reference, and prioritization. It is not a claim of equivalence or compliance.

no equivalence claimno certification claimbuyer review

Crosswalk

Framework relationships and scorecard bridges.

Filter the crosswalk rows, inspect the rationale, and open the source links for the public-safe data trail.

377

Inspector

Selected mapping

88%
mitre_atlas
owasp_llm_top10
inferred

LLM Prompt Injection maps to Prompt Injection. Confidence is directional and public-safe, not a claim of official equivalence.

Source node

AML.T0051

LLM Prompt Injection

Target node

LLM01

Prompt Injection

Rationale

Both address prompt injection against LLM applications, including untrusted instructions influencing model behavior.

Use the underlying JSON export to seed Supabase crosswalk rows or to generate a private benchmark view.

Private engagement

Turn framework mapping into governance evidence.

Use these framework maps to scope evidence prompts, maturity gaps, and remediation work across AI governance, security, and product risk. A governance evidence sprint produces artifacts your buyers and board can act on.