ETQ024
Adopt quality-criteria-based evidence standards—covering relevance, lineage, and authenticity—so AI-generated and digital sources meet IIA evidentiary requirements.
CyberTRIZ analysis · Audit contradiction ETQ024 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Audit Evidence Standardization vs New Evidence Sources
Business ContextStandard evidence requirements improve consistency and defensibility, but modern audits increasingly use system logs, process-mining outputs, sensor information, digital communications, AI-generated analysis, and other sources that may not fit traditional evidence categories.
Audit TRIZ ResolutionStandardize evidence-quality criteria rather than restricting acceptable evidence to familiar formats. New sources are evaluated for relevance, reliability, authenticity, completeness, lineage, and suitability for the intended conclusion before being incorporated into audit work.
Applicable TRIZ Principles
Principle 35 – Parameter Changes shifts standardization from evidence format toward evidence-quality characteristics.
Principle 28 – Mechanics Substitution permits digital evidence to replace traditional evidence where reliability is equivalent or stronger.
Principle 15 – Dynamics allows evidence methodologies to evolve as new reliable sources become available.
Expected Outcome
Consistent evidence quality
Greater use of digital evidence
Improved audit innovation
Reduced dependence on outdated evidence formats
Decision Indicators
Reliable digital evidence is rejected because methodology recognizes only traditional formats.
New evidence sources are used without defined reliability criteria.
Auditors convert digital information into manual documents solely to satisfy workpaper conventions.
Evidence standards lag behind changes in organizational technology.
Different teams evaluate identical new evidence sources inconsistently.