> **Source of truth:** [`../practices/EG-Data-OnePager.md`](../practices/EG-Data-OnePager.md) | **Canonical framing:** [`../HAIAMM-v3.0-Framing.md`](../HAIAMM-v3.0-Framing.md) §8 / §12

---

# Education & Guidance (EG) - Data Domain
## HAIAMM Assessment Questionnaire v3.0

**Practice:** Education & Guidance (EG)
**Domain:** Data
**Purpose:** Assess organizational maturity in building AI-data-assurance literacy for data-handler workforce (shadow data in AI as the primary L1 cultural outcome) and deep practitioner skills for the reviewer population performing lineage verification, classification review, DPIA composition, and data-flow security review of AI/HAI data archetypes.
**Scoring Model:** Evidence + Outcome Metrics (see Scoring Methodology below)

---

## Instructions

- Answer each question honestly based on **current, implemented practices** (not plans or aspirations)
- Each question has two components: **Evidence** (what you did) and **Outcome Metrics** (how well it worked)
- **Scoring uses 4 tiers:** Fully Mature (1.0), Implemented (0.67), Partial (0.33), Not Implemented (0.0)
- **Answer progressively** - Complete all Level 1 questions before Level 2
- **Level progression** - Achieve ALL questions at lower level before advancing
- **Baseline first** - Record current metric values before setting targets

---

## Scoring Methodology

| Score | Label | Criteria |
|-------|-------|----------|
| 1.0 | Fully Mature | Evidence complete AND ≥3 outcome metrics meet targets |
| 0.67 | Implemented | Evidence complete AND 2 outcome metrics meet targets |
| 0.33 | Partial | Evidence partially complete OR <2 outcome metrics meet targets |
| 0.0 | Not Implemented | No evidence of the activity in place |

---

## Maturity Level 1

**Objective:** Deliver foundational data-assurance literacy to ≥95% of the data-handler workforce and role-based practitioner training to 100% of the reviewer population, with an active shadow-data-in-AI awareness campaign.

---

### Question 1: Ship data-handler workforce AI-data-assurance literacy training

**Q1.1:** Do you have a current-year AI-data-assurance literacy course (≤20 minutes) completed by ≥95% of engineers, data scientists, ML platform engineers, and analysts handling AI/HAI data, covering the seven data archetypes, the five data-specific HAI TTPs, the AI Data Use Policy rules, and the sanction-gate intake process, with content updated within 30 days of any policy or archetype change?

**Evidence Required:**
- [ ] LMS module published covering the seven AI/HAI data archetypes (training corpus, inference input stream, retrieval store, prompt/completion log corpus, embedding store, fine-tuning dataset, evaluation/test set) with org-specific examples
- [ ] Module includes the five data-specific HAI TTPs in plain language: training-data poisoning (TM), training-data leakage (TM), retrieval-poisoning (AGH), embedding inversion (TM), and prompt injection via retrieved documents (AGH), one concrete data-handling example per TTP matched to the relevant archetype
- [ ] AI Data Use Policy five-rule summary, sanction-gate intake process, and before-you-use decision aid included
- [ ] LMS completion report showing current-year completion rate across in-scope data-handler headcount
- [ ] Named training content owner with documented quarterly review cadence
- [ ] Evidence that content was updated within 30 days of last policy, archetype, or compliance map change
- [ ] New-hire coverage SLA documented (AI-data-assurance literacy within 30 days of start)

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| % data-handler workforce with current-year AI-data-assurance literacy completion | ___% | ___% | ≥95% | ☐ | |
| % sanction-gate reviewers with completed practitioner training | ___% | ___% | 100% | ☐ | |
| Reviewer calibration drift, avg classification-tier delta across reviewers on shared samples | ___ | ___ | ≤1 classification-tier step | ☐ | |
| Reviewer calibration drift, avg DPIA trigger disagreements per sample | ___ | ___ | ≤1 per sample | ☐ | |

**Metric Collection Guidance:**
- **Data-handler literacy completion:** LMS current-year completion report filtered to in-scope data-handler roles (engineers, data scientists, ML platform engineers, analysts). Cross-reference with HR headcount for denominator. Formula: `completed_count / in-scope_headcount × 100`
- **Reviewer training completion:** LMS report filtered to sanction-gate-approval-permissions group (data stewards, DPOs/delegates, AppSec/AI safety reviewers). 100% is a pass/fail gate.
- **Calibration drift, classification tier:** Quarterly exercise where reviewers independently score the same sample data-asset intake. Record each reviewer's classification label; calculate mean absolute tier deviation. Source: calibration debrief facilitator record.
- **Calibration drift, DPIA trigger:** Same exercise; count DPIA trigger determinations that differ from the facilitated consensus answer per reviewer per sample; average across reviewers.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No AI-data-assurance literacy training in place)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

### Question 2: Deliver role-based practitioner training for the reviewer population

**Q1.2:** Has the practitioner population (data stewards, DPOs/delegates, AppSec/AI safety reviewers) completed a role-based training module (~2.5 hours) covering lineage verification, classification scanning and label propagation, consent-basis verification (GDPR Arts. 6/9), DPIA composition (Art. 35), opt-out and deletion enforcement, training-data canary insertion, embedding-store retention and inversion defense, and retrieval-source classification propagation, with completion gated on sanction-gate-approval permissions and calibration drift inside target for two consecutive quarters?

**Evidence Required:**
- [ ] Practitioner training module published covering lineage verification (catalog query patterns: Atlan, Collibra, DataHub, Unity Catalog, MLflow lineage APIs), classification scanning and label-propagation rules, GDPR Arts. 6 and 9 consent-basis verification
- [ ] DPIA composition module included: the six DPIA sections, when mandatory (large-scale, systematic evaluation, special-category data), Art. 35 trigger determination, and DPIA adequacy assessment judgment
- [ ] Opt-out/deletion enforcement module: Art. 17 propagation through derived embeddings and retrieval indexes; re-training/re-indexing triggers
- [ ] Training-data canary insertion technique covered (design, insertion, tracking in data inventory)
- [ ] Embedding-store retention and inversion defense covered (inversion attack risk, access-control patterns, retention-limit enforcement)
- [ ] Retrieval-source classification propagation module: label propagation to retrieval index, retrieval-poisoning (AGH) entry point through unclassified sources
- [ ] Completion gating enforced: sanction-gate-approval permissions provisioned only after LMS training completion confirmed
- [ ] Calibration exercise records from two consecutive quarters showing drift inside target (≤1 classification-tier step, ≤1 DPIA trigger disagreement per sample)

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| % sanction-gate reviewers with completed practitioner training | ___% | ___% | 100% | ☐ | |
| Reviewer calibration drift, avg classification-tier delta on shared samples | ___ | ___ | ≤1 classification-tier step | ☐ | |
| Reviewer calibration drift, avg DPIA trigger disagreements per sample | ___ | ___ | ≤1 per sample | ☐ | |
| Reviewer throughput, gate intakes closed per reviewer per week (post-training trend) | ___ | ___ | trending up | ☐ | |

**Metric Collection Guidance:**
- **Practitioner training completion:** LMS completion report filtered to sanction-gate-reviewer role group. Source: LMS + access-control system confirming permissions are gated on training.
- **Calibration drift, classification tier:** Independent scoring of shared sample data-asset intakes; mean absolute classification-tier deviation across reviewer cohort. Source: quarterly calibration debrief facilitator records.
- **Calibration drift, DPIA trigger:** Count of DPIA trigger determination discrepancies per sample versus facilitated consensus; averaged across reviewers. Source: calibration debrief records.
- **Reviewer throughput:** Gate intake queue analytics, intakes closed per reviewer per week before and after training rollout. Source: intake queue system.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No practitioner training in place)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

### Question 3: Run the shadow-data-in-AI awareness campaign

**Q1.3:** Is a shadow-data-in-AI awareness campaign running with at least monthly content, a visible amnesty path linked from the Data AUP and intake form, measurable attribution of intake submissions and amnesty disclosures to campaign channels, and DPO/data-steward micro-content for every Critical or High AI data archetype active in the inventory?

**Evidence Required:**
- [ ] Campaign launch documented: executive sponsor message naming shadow data in AI, amnesty window announced, sanctioned-archetype catalog published with fast-track framing
- [ ] Monthly content cadence evidenced: content calendar or publication log showing at least one shadow-data-in-AI piece per month (approved data source, fast-track win, anonymized TTP catch, external incident reframe)
- [ ] Amnesty path linked from the AI Data Use Policy, the intake form, and the engineering/data-science team channel pins
- [ ] Campaign channel URLs tagged for attribution; intake-queue referrer-source field populated
- [ ] DPO and data-steward micro-content (Art. 10 data-governance evidence, DPIA trigger assessment, deletion-capability requirements) deployed for every Critical or High AI data archetype active in the inventory
- [ ] Feedback channel for new data sources with documented ≤5 BD triage SLA

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| Shadow-data-in-AI disclosures per quarter (amnesty path, intake queue tagged "amnesty") | ___ | ___ | rises Q1–Q2 then trends down | ☐ | |
| Intake submissions attributable to campaign channels | ___% | ___% | ≥30% of net-new intakes | ☐ | |
| % data-handler workforce with current-year AI-data-assurance literacy completion | ___% | ___% | ≥95% | ☐ | |
| Campaign content cadence met | ___ | ___ | ≥1 piece/month | ☐ | |

**Metric Collection Guidance:**
- **Shadow data disclosures:** Count of intake submissions tagged "amnesty" per quarter. Expect increase in Q1–Q2 post-launch (awareness working) and decrease thereafter (sanctioned catalog adoption reducing ungated use). Source: intake queue analytics.
- **Campaign attribution:** Percentage of net-new intake submissions arriving via tagged campaign channel links or form-referrer fields. Source: intake queue referrer field; UTM tracking or equivalent.
- **Data-handler literacy completion:** Same methodology as Q1.1. Shared metric.
- **Content cadence:** Count of published shadow-data-in-AI content pieces per calendar month. Source: content calendar or publication log.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No campaign running)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

## Maturity Level 2

**Objective:** Deepen practitioner skill through scenario-based training from real intake cases, deliver product-line-specific data-handler tracks calibrated to SM-Data L2 risk tiers, and run seasonal shadow-data-in-AI campaigns tied to model-release and data-refresh cycles.

---

### Question 4: Scenario-based reviewer training from real intakes

**Q2.1:** Is there a scenario library of ≥30 anonymized real intake cases powering practitioner training across the org's in-scope data archetypes, with paired calibration exercises showing Critical-tier drift ≤1 classification-tier step and ≤0 DPIA trigger disagreements per sample for two consecutive quarters, including a practitioner capstone requiring three live end-to-end intakes under a senior-reviewer shadow?

**Evidence Required:**
- [ ] Scenario library of ≥30 scenarios documented, each with as-submitted data-asset description, original reviewer decisions (classification label, DPIA trigger determination, SR gaps), any disagreement, and resolved outcome
- [ ] Scenarios organized per archetype (training-corpus, retrieval-store, fine-tuning-dataset, embedding-store, prompt/completion-log-corpus) and per TTP cluster (training-data poisoning, training-data leakage, retrieval-poisoning, embedding inversion, prompt injection via retrieved documents)
- [ ] Paired calibration exercises in place: two reviewers score the same scenario independently; debrief facilitated on classification-label delta and DPIA trigger delta
- [ ] Critical-tier calibration drift records for two consecutive quarters showing ≤1 classification-tier step and ≤0 DPIA trigger disagreements per sample
- [ ] Practitioner capstone in place: practitioners run three live intakes with senior-reviewer shadow and produce passing classification record, lineage review, and DPIA scoping note
- [ ] Scenario library reviewed quarterly with retirement criteria for stale scenarios documented

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| Reviewer calibration drift on Critical-tier scenarios, avg classification-tier delta | ___ | ___ | ≤1 classification-tier step | ☐ | |
| Reviewer calibration drift on Critical-tier scenarios, avg DPIA trigger disagreements | ___ | ___ | ≤0 per sample | ☐ | |
| % Critical/High-tier data assets with ≥1 team member trained on applicable product-line track | ___% | ___% | 100% | ☐ | |
| % training content refreshed in last 90 days | ___% | ___% | ≥80% | ☐ | |

**Metric Collection Guidance:**
- **Critical-tier calibration drift:** Quarterly calibration exercise focused on Critical-tier scenarios (PII-bearing training corpora, special-category fine-tuning datasets). Record classification labels and DPIA trigger determinations independently; calculate mean absolute deviation. Source: calibration debrief facilitator records.
- **Product-line track coverage:** LMS completion records for product-line tracks cross-referenced against SM-Data L2 inventory Critical/High-tier assets. Formula: `assets_with_trained_practitioner / total_Critical_High_assets × 100`
- **Content freshness:** LMS content-management change log; count modules updated within the last 90 days.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No scenario library in place)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

### Question 5: Product-line-specific data-handler tracks

**Q2.2:** Have product-line-specific data-handler tracks (clinical AI, fintech AI, developer-tool AI, consumer AI, or equivalent for the org's product mix) been delivered to ≥1 trained practitioner per Critical/High-tier data asset, each track covering the data archetypes and governance obligations specific to that product line, with team-level coverage tracked in the SM-Data inventory?

**Evidence Required:**
- [ ] Clinical AI track developed: PHI training corpora (HIPAA minimum-necessary, BAA verification), GDPR Art. 9 health-data basis verification, DPIA composition for clinical training data, embedding-store inversion risk for medical records, retrieval-poisoning risk in clinical RAG pipelines
- [ ] Fintech AI track developed: PCI cardholder data in training corpora, PCI-DSS 3.4 controls, FINRA/SEC model-input retention, GDPR Art. 6 lawful basis for financial data, prompt/completion log retention for regulatory record-keeping
- [ ] Developer-tool AI track developed: source-code training corpora (customer IP, trade secrets), fine-tuning dataset lineage and license compatibility, embedding stores of code (inversion and IP-exposure risk), prompt/completion log corpora from coding assistants (retention and secondary-use restriction)
- [ ] Consumer AI track developed: inference input streams with end-user personal data, consent-basis verification for logged interactions, opt-out enforcement, GDPR Art. 22 automated-decisioning safeguards, cross-border transfer risk
- [ ] Mandatory enrollment policy: any team owning a Critical or High-tier data asset in the applicable product line must have ≥1 trained practitioner
- [ ] LMS completion records cross-referenced with SM-Data inventory showing ≥1 trained practitioner per Critical/High-tier data asset

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| % Critical/High-tier data assets with ≥1 team member trained on applicable product-line track | ___% | ___% | 100% | ☐ | |
| % workforce literacy completion maintained | ___% | ___% | ≥95% | ☐ | |
| Shadow-data-in-AI campaign behavior-target achievement rate | ___% | ___% | ≥70% of campaigns | ☐ | |
| % training content refreshed in last 90 days | ___% | ___% | ≥80% | ☐ | |

**Metric Collection Guidance:**
- **Product-line track coverage:** LMS module completion records filtered to product-line track modules joined against SM-Data L2 asset inventory by owning team. Formula: `assets_with_trained_practitioner / total_Critical_High_assets × 100`
- **Workforce literacy maintained:** Ongoing LMS current-year completion rate; same methodology as Q1.1.
- **Campaign behavior-target achievement:** For each campaign with a pre-set behavior target (e.g., reduce ungated fine-tuning dataset uploads by 50%), record whether post-campaign measurement met target.
- **Content freshness:** LMS content management change log; modules with last-updated date within 90 days of assessment.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No product-line data-handler tracks in place)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

### Question 6: Seasonal, behavior-driven shadow-data-in-AI campaigns

**Q2.3:** Are shadow-data-in-AI campaigns running on a seasonal, behavior-driven cadence tied to model-release windows, data-refresh cycles, post-incident moments, and hiring surges, with pre-set measurable behavior targets, post-campaign measurement, ≥70% of campaigns hitting their target, and ≥80% of training content updated in the last 90 days?

**Evidence Required:**
- [ ] At least 2 behavior-driven campaigns run in the last 12 months, each with a documented pre-measured behavior target (e.g., "reduce ungated fine-tuning dataset uploads by 50% in Q3")
- [ ] Post-campaign measurement records for each campaign showing whether the behavior target was met
- [ ] Campaign scheduling aligned to observed shadow-data risk windows (model-release windows, data-refresh cycles, post-external-incident moments, hiring surges)
- [ ] Amnesty windows running alongside campaigns with attribution tracking
- [ ] Campaign redesign process: campaigns missing behavior targets by >20% are redesigned
- [ ] Content change log showing ≥80% of training modules updated in last 90 days

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| Shadow-data-in-AI campaign behavior-target achievement rate | ___% | ___% | ≥70% of campaigns hit target | ☐ | |
| % training content refreshed in last 90 days | ___% | ___% | ≥80% | ☐ | |
| Reviewer calibration drift on Critical-tier scenarios | ___ | ___ | ≤1 classification-tier step, ≤0 DPIA trigger disagreements | ☐ | |
| % workforce literacy completion maintained | ___% | ___% | ≥95% | ☐ | |

**Metric Collection Guidance:**
- **Campaign behavior-target achievement:** Post-campaign measurement comparing pre-campaign baseline to post-campaign state for each behavior target. Source: intake queue analytics, data-pipeline monitoring, or equivalent behavioral signal.
- **Content freshness:** LMS content management change log; count modules with last-updated date in the 90-day window preceding assessment date.
- **Calibration drift:** Same methodology as Q2.1.
- **Workforce literacy:** Same methodology as Q1.1.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No behavior-driven campaigns in place)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

## Maturity Level 3

**Objective:** Operate continuous calibration at scale, externalize the AI-data-assurance curriculum and reviewer rubric as industry-shared artifacts, and contribute to emerging AI-data-handler certification pathways.

---

### Question 7: Externalize the curriculum, scenario library, and reviewer rubric

**Q3.1:** Has the practitioner curriculum, anonymized scenario library, and reviewer rubric been published externally (CSA AI Safety Initiative, IAPP AI data-governance track, OpenSSF AI, DAMA, or sector ISAC) with documented adoption, citations, forks, or direct acknowledgment, and do external community contributions loop back into internal content within 30 days?

**Evidence Required:**
- [ ] Workforce AI-data-assurance literacy module published externally (learning objectives, assessment questions, reference-card template covering the seven archetypes and five data-specific TTPs)
- [ ] Practitioner curriculum published externally (module outlines, per-archetype reviewer job aids, DPIA composition guide for AI training data, lineage-verification checklist, embedding-inversion risk assessment guide)
- [ ] Anonymized scenario library published (scenario format, per-archetype examples including Clinical AI/Fintech AI/Developer-Tool AI/Consumer AI tracks, calibration debrief format)
- [ ] Reviewer rubric published (classification-label criteria, DPIA-trigger determination scoring, lineage-verification scoring, SR-gap-list completeness scoring)
- [ ] External adoption evidence: citations, forks/downloads, direct adoption acknowledgment from ≥1 other organization
- [ ] Process documented for external contributions to flow back into internal content within 30 days

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| External adoption, citations, forks, downloads of curriculum/scenario library/rubric artifacts | 0 | ___ | tracked, trending up | ☐ | |
| % Critical-tier data reviewers holding an external AI-assurance or AI-data-governance credential | 0% | ___% | ≥50% by year 2 of L3 (where credential exists) | ☐ | |
| Monthly live calibration cadence met | ___ | ___ | monthly, on calendar | ☐ | |
| ATLAS TTP contributions or confirmations per year (training-data poisoning, retrieval-poisoning, embedding inversion) | 0 | ___ | ≥1 where novel observations exist | ☐ | |

**Metric Collection Guidance:**
- **External adoption:** Repository fork/download counts; citation tracker (Google Scholar, IAPP, CSA publication references); direct outreach acknowledgment records. Tracked quarterly.
- **External credentials:** HR credential registry cross-referenced with Critical-tier data reviewer list. Credentials in scope: CSA AI Safety, ISACA AI, IAPP AI data-governance certification, CIPP/E with AI extension, sector-specific ISAC credentials. Formula: `credentialed_Critical_tier_reviewers / total_Critical_tier_reviewers × 100`
- **Live calibration cadence:** Calendar entries confirming monthly calibration rounds completed; facilitator sign-off records.
- **ATLAS contributions:** ATLAS GitHub contribution history or MITRE working group correspondence for data-domain techniques (training-data poisoning, retrieval-poisoning, embedding inversion). Source: contribution log.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No external publication in place)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

### Question 8: Continuous live calibration

**Q3.2:** Is a monthly live calibration cadence operating, each round using a current anonymized data-asset intake from the live queue, independent reviewer scoring of classification label/DPIA trigger/primary TTP/top-3-SR-gaps, drift reported to the program sponsor, with calibration results feeding the scenario library within 30 days and ≥50% of Critical-tier data reviewers holding an external credential where one exists?

**Evidence Required:**
- [ ] Monthly calibration calendar entries confirmed for last 12 months (or since L3 initiation)
- [ ] Per-round calibration records: anonymized intake used, reviewer cohort, independent scoring results (classification label, DPIA trigger, primary TTP), drift calculation, facilitator debrief notes
- [ ] Drift reported to program sponsor each month with trend over last quarter
- [ ] Process documented and evidenced for adding new scenarios to the library within 30 days of calibration rounds revealing drift
- [ ] Individual reviewer coaching records for reviewers with persistent drift on specific archetype types
- [ ] Credential registry showing ≥50% of Critical-tier data reviewers credentialed where external credentials exist

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| Monthly live calibration cadence met | ___ | ___ | monthly, on calendar | ☐ | |
| % Critical-tier data reviewers holding external AI-assurance or AI-data-governance credential | 0% | ___% | ≥50% (where credential exists) | ☐ | |
| Calibration results feeding scenario library within 30 days (% of drift-revealing rounds actioned) | ___% | ___% | 100% | ☐ | |
| ATLAS data-domain TTP contributions or confirmations per year | 0 | ___ | ≥1 where novel observations exist | ☐ | |

**Metric Collection Guidance:**
- **Live calibration cadence:** Program operations calendar; facilitator sign-off records per round. Count calendar months with a completed calibration round in the last 12 months.
- **External credentials:** HR credential registry; count of Critical-tier data reviewers with a recognized AI-data-governance credential divided by total Critical-tier data reviewer count.
- **Scenario library pipeline:** Change log of scenario library; count calibration rounds where drift was identified; count of those rounds that resulted in a new scenario added within 30 days.
- **ATLAS contributions:** ATLAS submission records for data-domain techniques. Source: ATLAS contribution log maintained by program lead.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No continuous live calibration in place)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

### Question 9: AI-data-handler certification contribution

**Q3.3:** Does the program contribute ≥2 substantive artifacts per year to industry AI-data-handler certification or curriculum working groups (CSA AI Safety, ISACA AI, IAPP AI data-governance, DAMA AI data-management, CIPP/E extensions for AI data processing), and ≥1 MITRE ATLAS data-domain TTP contribution or confirmation per year where novel observations exist?

**Evidence Required:**
- [ ] At least 2 substantive contributions per year to industry AI-data-handler certification or curriculum working groups, documented with contribution artifact, working group name, and date
- [ ] At least 1 MITRE ATLAS data-domain TTP contribution or confirmation per year where novel observations exist (training-data poisoning, retrieval-poisoning, or embedding inversion; submission artifact, ATLAS correspondence, or confirmed technique instance)
- [ ] Org's practitioner capstone aligned with certification-grade rubrics where external credentials exist
- [ ] Reviewer external-credential pursuit supported: study resources, exam fee reimbursement, time allocation policy
- [ ] Process documented for external working-group outputs to update internal curriculum within 30 days

**Outcome Metrics:**

| Metric | Baseline | Current | Target | Met? | Notes |
|--------|----------|---------|--------|------|-------|
| Contributions to industry AI-data-handler certification/curriculum working groups per year | 0 | ___ | ≥2 substantive | ☐ | |
| ATLAS data-domain TTP contributions or confirmations per year | 0 | ___ | ≥1 where novel observations exist | ☐ | |
| % Critical-tier data reviewers holding external credential | 0% | ___% | ≥50% by year 2 of L3 (where credential exists) | ☐ | |
| External adoption of curriculum artifacts (citations, forks, downloads) | 0 | ___ | tracked, trending up | ☐ | |

**Metric Collection Guidance:**
- **Industry contributions:** Contribution log maintained by program lead; each entry includes contribution type, working group name, submission date, acceptance or publication status.
- **ATLAS data-domain contributions:** MITRE ATLAS GitHub pull requests or working-group submissions for data-specific techniques (training-data poisoning, retrieval-poisoning, embedding inversion). Source: ATLAS contribution log.
- **External credentials:** Same methodology as Q3.2.
- **External adoption:** Repository analytics, citation tracker, working-group acknowledgment records. Trend tracked quarterly.

**Answer:**
- ☐ **Fully Mature** (Evidence complete + ≥3 metrics meet targets)
- ☐ **Implemented** (Evidence complete + 2 metrics meet targets)
- ☐ **Partial** (Evidence partially complete + <2 metrics meet targets)
- ☐ **Not Implemented** (No industry certification contributions)

**Evidence Location:** _______________________________________________
**Metric Validation Date:** _______________________________________________
**Notes:** _______________________________________________

---

## Summary Scorecard

| Level | Q1 | Q2 | Q3 | Avg | Level Achieved? |
|-------|----|----|-----|-----|-----------------|
| L1 | ___ | ___ | ___ | ___ | ☐ Yes ☐ No |
| L2 | ___ | ___ | ___ | ___ | ☐ Yes ☐ No |
| L3 | ___ | ___ | ___ | ___ | ☐ Yes ☐ No |

**Practice Maturity Statement:**

The organization's EG-Data practice is at **Level ___** with an average score of **___**.

- Level 1 achieved when all L1 questions score ≥0.67 (Implemented)
- Level 2 achieved when all L1 questions score 1.0 (Fully Mature) AND all L2 questions score ≥0.67
- Level 3 achieved when all L1–L2 questions score 1.0 AND all L3 questions score ≥0.67

---

**Document Version:** HAIAMM v3.0
**Practice:** Education & Guidance (EG)
**Domain:** Data
**Last Updated:** 2026-05-15
**Author:** Verifhai