AI integrity means reconstructability.
IQAI is built around a control question: can an organization reconstruct how sources became AI-assisted outputs, what changed, who reviewed them, and what was ultimately relied on?
Security and privacy are not enough if the output cannot be reconstructed.
AI-assisted workflows can extract, summarize, classify, score, route, and recommend. When those transformations feed business reliance, organizations need a record of the path from source material to reviewed output.
What entered?
Inputs, source documents, data feeds, evidence bundles, prompts, or reference material need intake context.
What changed?
AI-assisted extraction, summarization, scoring, fusion, routing, or synthesis should be visible.
What was trusted?
The reviewed output, approval state, residual issues, and reliance record should be tied together.
From source material to reliance record.
The control model turns AI-assisted work into a reviewable chain. It does not ask the model to certify truth. It creates the records a human, auditor, lawyer, or reviewer needs.
Data, documents, source links, evidence bundles, or reference material.
What entered, when, under which policy, and with which intake rules.
Extraction, summarization, scoring, routing, fusion, or synthesis events.
Claims, reports, summaries, scores, recommendations, or code changes.
Authorized humans approve, modify, reject, or escalate outputs.
What was relied on, tied to versions and review outcomes.
Retention-aligned records for authorized review, supervision, or audit.
Standards language becomes product architecture.
The control model is expressed in standards-native terms: output integrity, provenance, processor risks, management measures, review checkpoints, and reliance records.
| Standards-facing area | Integrity hook | IQAI interpretation |
|---|---|---|
| Output processing | Output classification, version binding, provenance link, review status, residual-risk disclosure. | Outputs should not move into reliance without labels, versioning, evidence links, and review status. |
| Provenance | Extend beyond source origin to material transformations and AI-assisted operations. | Knowing where data came from is not enough; reviewers need to know what changed. |
| Processor risks | Wrong fusion, unattributed synthesis, overreliance, unlabeled outputs, weak handoff traceability. | AI-assisted processing creates integrity risks at the transformation and handoff boundary. |
| Management measures | Processing receipts, logs, review checkpoints, retention, permitted use, release approval, overrides. | Governance needs artifacts: who reviewed, what was approved, what was retained, and what remains open. |
From integrity concept to IQAI control.
IQAI translates abstract governance language into operational product controls that can be used by legal, risk, compliance, audit, security, and engineering teams.
| Integrity concept | IQAI implementation | Buyer value |
|---|---|---|
| Source-to-output traceability | Claim/evidence linkage and source support review. | Auditability and defensible review. |
| Transformation events | Records of extraction, summarization, scoring, routing, synthesis, or agent action. | Reconstructability across AI-assisted work. |
| Output integrity control | Supported, Weak, Unsupported, Needs External Verification, or Requires Human Review. | Review discipline before circulation or reliance. |
| Human review checkpoint | Reviewer queue, approval, rejection, modification, escalation, and sign-off state. | Accountability remains with humans. |
| Processing receipt | Summary of inputs, transformations, outputs, review result, and remaining open issues. | Evidence record for audit, legal, or governance review. |
| Reliance record | Final reviewed artifact tied to version, reviewer, support posture, and reliance decision. | Defensible decision trail. |
| Bounded external check | Policy-gated lookup using minimal query fields and allow-listed sources. | Verification without broad data exposure. |
One integrity architecture across three control surfaces.
Diagnostics, Risk, and Code are not isolated tools. They are different applications of the same integrity pattern: inspect before reliance.
AI behavior before reliance
Stability, overconfidence, unsupported claims, drift, hesitation, reproducibility, and decision-readiness.
Documents before liability
Claim registers, evidence links, support posture, human review, publication receipts, and reliance records.
AI-assisted work before production
Agent behavior, file changes, protected paths, task drift, unauthorized scope expansion, and review state.
The records that make AI-assisted work reviewable.
The integrity model translates into concrete records that legal, audit, compliance, security, and technology teams already understand.
Source and ingestion context
Agreements, data-flow descriptions, source classifications, ingestion logs, and run identifiers.
Transformation traceability
Records of merges, extraction, summarization, tool/model metadata, and configuration snapshots.
Output integrity labeling
Output labels, release status, version identifiers, export packages, and change logs.
Human review checkpoints
Reviewer roles, approvals, modifications, rejections, timestamps, and rationale where required.
Reliance and accountability
Reliance records tied to released versions, exception records, overrides, and separation of duties.
Bounded external checks
Allow-listed checks, minimal-query records, and interpretation rules for external outcomes.
What the contribution package contains.
The underlying work is structured as a standards-contribution memo, not a product brochure. It contains committee-facing material, clause mapping, proposed language, discussion tools, and evidence categories.
Where the overlay lands
Maps the integrity overlay to WD 7709 areas such as data processing, role model, data sharing, processor risks, output processing, and provenance.
Gap, addition, rationale
Connects current WD hooks to integrity gaps, suggested additions, and why regulated users, auditors, and reviewers would care.
Sources to reliance
Defines the source-to-output chain: source data, ingestion record, transformation events, processing outputs, human review, reliance record, and audit trail.
Standards-native vocabulary
Includes proposed terms such as AI-assisted processing, transformation event, processing receipt, reliance record, output integrity control, and bounded external check.
Product-neutral wording
Provides candidate paragraphs for scope, technical framework, result output processing, provenance, management measures, and AI-assisted processing notes.
Crosswalk and committee support
Includes a crosswalk, priority shortlist, chair / rapporteur script, liaison pointers, non-goals, questions for discussion, and evidence examples.
Standards-facing contribution package.
The underlying package provides clause-level hooks, definitions, proposed wording, crosswalks, and discussion language for ISO/IEC WD 7709 review.
Confidential SME contribution package
The package frames AI integrity as reconstructability across sources, transformations, outputs, human review, reliance records, and audit trails. It supports the architecture behind IQAI without claiming ISO publication, certification, or product conformity.
IQAI makes AI-assisted work reconstructable before reliance.
Sources, transformations, outputs, human review, reliance records, and audit trails: this is the control architecture behind responsible enterprise AI adoption.