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DECISION INTEL · REGULATORY ALIGNMENT
AI Verify Principle Mapping
decision-intel.com/regulatory/ai-verify

Regulatory alignment

Decision Intel & the AI Verify Foundation.

Every Decision Provenance Record maps onto the 11 internationally-recognised AI governance principlescodified by AI Verify, Singapore IMDA’s governance framework, cross-aligned with the EU AI Act and the OECD AI Principles. The reference implementation a Fortune 500 procurement team can paste into a vendor risk assessment.

Self-assessment · no third-party certification
AI Verify is a self-assessment governance framework codified by the AI Verify Foundation (Singapore IMDA, aligned with EU and OECD). The Foundation does not certify products. Decision Intel’s alignment with the 11 internationally-recognised AI governance principles is self-attested; no third-party audit has yet been performed against this mapping.
Read the security postureAI Verify Foundation

The 11 principles, and the DPR field that satisfies each.

Each row names the AI Verify principle, defines it in one sentence, describes the mechanism inside Decision Intel that satisfies it, and points at the specific Decision Provenance Record field that makes the mechanism verifiable.

  1. Principle 01

    Transparency

    The AI system discloses information about itself to relevant stakeholders.

    Every audit ships with the SHA-256 fingerprint of the exact prompt version used, plus full model lineage: which model tier ran on which pipeline stage, with decoding parameters recorded per stage. Nothing about the model or the prompt is hidden.

    Prompt fingerprintModel lineage
  2. Principle 02

    Explainability

    The AI system’s outputs can be understood in human terms.

    Every flagged bias carries a stable taxonomy ID (DI-B-001 → DI-B-022) and a primary APA academic reference with DOI where available. A GC reading the DPR can trace every flag back to its peer-reviewed source.

    Academic citations22-bias taxonomy with DOIs
  3. Principle 03

    Repeatability / Reproducibility

    The AI system’s behavior can be reproduced given the same inputs.

    Input-document hash + prompt fingerprint + model lineage together make every analysis reproducible from the same inputs. The risk-scorer node is deterministic (not LLM-generated), so the final score is stable for identical inputs.

    Input-document hashPrompt fingerprintModel lineage
  4. Principle 04

    Safety

    The AI system behaves safely during deployment.

    GDPR anonymiser runs as the first node of the pipeline — no analysis LLM ever sees raw PII. Content is encrypted at rest with AES-256-GCM and a keyVersion rotation protocol. Ensemble sampling across three Kahneman-side nodes (bias detective + noise judge + reference-class pull) bounds individual-model failures by measuring inter-judge variance directly.

    Pipeline lineageJudge variance (noise score)
  5. Principle 05

    Security

    The AI system resists unauthorised access and tampering.

    TLS 1.2+ in transit, AES-256-GCM at rest with keyVersion rotation, SOC 2 Type II infrastructure (Vercel + Supabase) with Decision Intel’s own product-level Type I targeted for Q4 2026, CSRF protection via middleware, hashed + tamper-evident fingerprints on the DPR itself. Every encrypted row carries a keyVersion stamp so keys rotate without bricking historical data.

    Input-document hashPrompt fingerprintReviewer signatures
  6. Principle 06

    Robustness

    The AI system remains reliable under perturbation or partial failure.

    Three-judge noise jury arbitrated by a meta-judge — individual model failures do not cascade. Model routing classifies errors as transient vs permanent and fails over to a second provider when thresholds are exceeded. Exponential-backoff retries + atomic rate limiting on every call.

    Model lineageJudge variance
  7. Principle 07

    Fairness

    The AI system mitigates unintended discrimination across groups.

    The 30+ cognitive-bias taxonomy covers multiple fairness-relevant biases (authority bias, in-group favouritism, halo effect, availability bias). Cross-framework regulatory mapping includes GDPR Article 22 (non-discrimination on automated decisions) and the EU AI Act’s high-risk fairness provisions. Recalibration learns per-org failure patterns so fairness is auditable per customer.

    Academic citationsRegulatory mapping (GDPR Art 22, EU AI Act)
  8. Principle 08

    Data Governance

    The AI system handles data lawfully and in line with governance policy.

    No-training contract with every AI processor engaged. Per-org data isolation. Signed Data Processing Addendum on every paid tier. The GDPR anonymiser redacts PII before any third-party LLM receives the content. Encryption keys rotate with a documented protocol.

    Pipeline lineage (node 1 = GDPR anonymiser)
  9. Principle 09

    Accountability

    Responsibility for the AI system’s outputs is clear and documented.

    Every DPR includes a reviewer counter-signature block for the CSO or General Counsel to sign on receipt. Immutable audit log captures every action — who exported, who viewed, who edited — with filters, date range, and CSV export for downstream compliance tooling. Chain-of-custody timestamp on the record.

    Reviewer signaturesAudit log (separate)
  10. Principle 10

    Human Agency & Oversight

    The AI system supports, rather than replaces, human judgment.

    The Recognition-Rigor Framework (R²F) is designed around this principle. Kahneman’s rigor (debiasing) and Klein’s recognition (expert-intuition amplification) are both applied — but the CSO’s judgment stays in the centre, reinforced from both sides, never replaced. The DPR is the evidence of their oversight, not a substitute for it.

    Every field — the DPR is the oversight artifact
  11. Principle 11

    Inclusive Growth, Societal & Environmental Well-being

    The AI system contributes to outcomes that are socially and environmentally positive.

    Cross-framework regulatory mapping across 19 frameworks — international anchors (Basel III, EU AI Act, SEC Reg D, FCA Consumer Duty, SOX, GDPR Art 22, LPOA) plus African-market regimes (NDPR, CBN, ISA Nigeria 2007, WAEMU, CMA Kenya, BoG, FRC Nigeria, CBE, PoPIA, SARB, BoT) — aligns Decision Intel with societal governance objectives. Decision-quality audits reduce the strategic-decision failures that cascade into stakeholder harm. Cost-tier model routing reduces inference energy per audit where decision quality allows.

    Regulatory mappingModel lineage (cost-tier routing)

What alignment does, and does not, mean

Accurate, defensible, and stated openly.

AI Verify is a self-assessmentgovernance framework. The AI Verify Foundation does not certify products. No “AI Verify certified” label exists. Claims of full compliance or certification would be inaccurate.

What Decision Intel claims: every field of the Decision Provenance Record maps onto one or more of the 11 principles codified by AI Verify. The mechanism that satisfies each principle is named above. A procurement team, General Counsel, or internal auditor can verify the mapping row by row against the product.

The AI Verify Foundation’s own FAQ states that the framework “does not guarantee that any AI system tested will be free from risks or biases or is completely safe.” Decision Intel makes the same disclaimer: a bias-audit tool is a control, not a guarantee.

The same principles travel across African regulatory regimes

The 11 internationally-recognised AI governance principles the Decision Provenance Record already maps to (transparency, explainability, repeatability, safety, security, robustness, fairness, data governance, accountability, human agency & oversight, inclusive growth) align beat-for-beat with the emerging African framework stack. Every DPR field already satisfies these obligations; the full provision-level cross-walk is covered in the /security posture.

  • NDPR Art. 12Nigeria
    Automated-decision rights for Nigerian data subjects
  • CBN AI GuidelinesNigeria
    Model governance + explainability for regulated financial institutions (draft 2024)
  • FRC NigeriaNigeria
    Code of Corporate Governance — board-level decisioning + dissent capture
  • WAEMU8 West African Member States
    Cross-border data localisation + BCEAO financial-sector governance
  • CMA KenyaKenya
    Listed-company decisioning + prospectus disclosure (Conduct Regs 2024)
  • CBKKenya
    Banking (Amendment) Act 2024 §33B — digital-lending + AI/ML model risk
  • BoG Cyber & ICT RiskGhana
    Cyber, data and AI/ML model-governance for regulated financial institutions
  • CBE AI GuidelinesEgypt
    AI/ML governance + explainability for Egyptian banks (CBE 2023 framework)
  • PoPIA §71South Africa
    Automated-decision rights + data-subject access (in force July 2021)
  • SARB Model RiskSouth Africa
    Model risk + AI governance for SA-regulated banks (Directive D2/2022 + JS 2/2024)
  • BoT FinTechTanzania
    AI/ML decisioning under the BoT Regulatory Sandbox Guidelines 2023

Ready to put the DPR into your procurement pack?

The design-partner cohort gets the Decision Provenance Record bundled on every audit at $1,999/mo (20% off the $2,499 Strategy list) so the mapping above stops being a reference doc and starts being the artifact your General Counsel forwards to the audit committee.

See the design-partner program Security posture