OUR DIAGNOSTIC METHODOLOGY

Our Diagnostic Methodology

AuditPulse eliminates subjective compliance guesswork. We use a structured diagnostic that maps your answers against the specific articles and controls of four regulatory frameworks simultaneously, producing a scored gap assessment with regulatory citations.

The Evaluation Sequence

Everything from the initial questionnaire execution to final validation calculation operates on a strict sequence designed to surface legal vulnerability rapidly without false positives.

The 7-Question Diagnostic

01

You answer seven questions about your AI stack covering data practices, model governance, human oversight, vendor reliance, and documentation. Each answer is matched against the specific requirements of four regulatory frameworks to identify where gaps exist.

Regulatory Vector Mapping

02

The answers are mapped against the requirements of four regulatory frameworks: EU AI Act (2026), NIST AI RMF 1.1, ISO 42001:2023, and SOC 2 Type II trust criteria. Where a framework requires specific documentation or controls and your answers indicate a gap, the engine flags it as a finding.

AI Synthesis

03

Claude analyses your answers against the specific requirements of each framework and identifies where your documentation or controls fall short of what regulators require. Each finding is mapped to the exact regulatory article and includes the maximum penalty exposure and a remediation step.

Calculating Output Scores

04

Your score reflects how completely your current governance documentation and controls satisfy each framework's requirements. A higher score means fewer gaps. Critical gaps in high-risk areas reduce your score more significantly than minor documentation shortfalls.

REPORT STRUCTURE

Structuring The Defensible Asset

Outputs are generated as strict A4 PDF objects and delivered instantly to your inbox. The structural layout adheres directly to accepted Board-level compliance briefing protocols to ensure total transparency across your corporate reporting lines.

  • Executive Summary Block
  • Core Aggregate Score Metric
  • 1-to-1 Explicit Citation Mappings
  • Line-Item Remediation Roadmaps
  • Methodology Statement Footer
AuditPulse
CONFIDENTIAL | AI COMPLIANCE REPORT
AI COMPLIANCE DIAGNOSTIC REPORT
Meridian Software Group
James Okafor, CISO
68
OUT OF 100
ELEVATED
EU AI ACT
52
REVIEW
NIST AI RMF
67
REVIEW
ISO 42001
59
REVIEW
SOC 2
74
PASS
CRITICAL
NO HUMAN OVERSIGHT ON HIGH-RISK DECISIONS
EU AI Act Art. 14 - automated decisions affecting financial services customers require documented human review checkpoints.
CRITICAL
NO IMMUTABLE DATA LINEAGE RECORDS
ISO 42001:2023 §8.4 - inference model lacks traceable data provenance required for auditor review and SOC 2 CC6.1.
FLAGGED
AI INCIDENT RESPONSE PLAN INCOMPLETE
NIST AI RMF RS-1.1 - no documented response procedure for hallucination events, bias drift or third-party model API failure.
AuditPulse Diagnostic Report
Ref: AP-2026-4821
Trust Architecture
Audit Chain of Custody
Every finding is timestamped, legally cited, and signed by a named reviewer. Court-admissible by design.
Model Risk Assessment Initiated
2026-03-12 09:41 UTCEU AI Act Art. 9(2)
Bias Threshold Exceeded - Predictive Decision Engine
2026-03-12 09:44 UTCNIST AI RMF 1.1 GOVERN 1.2
Critical: Data Lineage Gap Identified
2026-03-12 09:51 UTCSOC 2 Type II - CC6.1
Report Signed - J. Harlow, Bar #CA-4821
2026-03-12 10:02 UTCISO 42001:2023 §6.4
Methodology Provenance
Every audit maps to four independent regulatory frameworks. Click any to inspect exact control mappings.
EU AI Act (2027)
94%
Maps to Articles 9, 10, 13, 14, 15. Risk classification, data governance, transparency obligations, human oversight, and accuracy monitoring are all instrumented and logged per annex requirements.
NIST AI RMF 1.1
88%
Full coverage of GOVERN, MAP, MEASURE, MANAGE functions. Each subcategory is tested with automated probes and human review, producing evidence artifacts per NIST SP 600-1 guidance.
ISO 42001:2023
Certified
AuditPulse is itself ISO 42001 certified. Our audit methodology was designed in collaboration with the standards body and maps directly to Clauses 4–10 with documented objective evidence.
SOC 2 Type II
91%
Trust Services Criteria mapped to AI-specific controls. Security, availability, processing integrity, confidentiality, and privacy are each tested against model behavior and data pipeline configurations.

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