AI Law, Policy & Governance — Part 1C (Assurance-in-Action)

Theme: Evidence Registry · Continuous Verification · Audit by Design · Follows Part 1A–1B.

Compliance should be the exhaust of good engineering. If you collect the right proof while you build, audits become exports—not events.

1) What “Decision-Grade” Evidence Looks Like

Evidence is decision-grade when a reasonable reviewer can answer three questions without chasing context:

• What changed? system, version, capability, intended use, constraints.
• Why is it safe and fair enough? risks considered, controls implemented, test results, limits disclosed.
• Who is accountable? owner role, approvals, next review, rollback plan.

Anything that fails these is narrative, not evidence.

2) Evidence Taxonomy (Start Here, Extend Later)

1. Purpose & Scope: problem statement, stakeholders, non-goals.
2. Data & Privacy: provenance matrix, legal basis, retention, DPIA note.
3. Safety & Robustness: eval suite results, adversarial tests, abuse scenarios, red-team logs.
4. Fairness & Accessibility: disparity analyses, mitigations, accessibility notes.
5. Security: threat model, hardening report, dependencies SBOM.
6. Transparency: model/system cards, UI disclosures, limitations.
7. Human Oversight: trigger thresholds, runbooks, training completion.
8. Monitoring & Incidents: KPIs/KRIs, alerts, post-incident reviews.
9. Change Control: materiality assessment, before/after diffs, approvals.
10. Records & Governance: sign-offs, waivers, periodic attestation.

3) The Assurance Registry (Minimal Data Model)

OBJECT: Artifact
- id, systemId, controlId, artifactType (from taxonomy), title
- ownerRole, createdAt, version, status: draft|review|approved
- evidenceLink (file/url), summary (<=200w), riskLevel (H/M/L)
- metricId(s), thresholds, lastEvaluatedAt, nextReviewAt
- changeReason (new|fix|feature|policy), approverRole, approvalAt

OBJECT: Control
- controlId, principle (safety|fairness|privacy|security|transparency|accountability)
- type (policy|technical|process|people), designNote, ownerRole
- evidenceRequired[] (artifactType), metricId(s)

OBJECT: Metric
- metricId, name, definition, dataSource, target, thresholdAlert
- sampling (cadence, window), ownerRole, status

RELATIONS:
- Artifact → Control (N:1) ; Control → Metric (M:N) ; Artifact → Metric (M:N)
  

4) Submission Workflow (90% of Assurance is Flow)

1. Draft: creator attaches artifact to controlId; completes summary.
2. Review: reviewer role checks quality rubric; accepts/requests changes.
3. Approve: approver role signs; version freezes; dashboard updates.
4. Schedule: system sets nextReviewAt from risk level (e.g., H=30d, M=90d, L=180d).

5) Quality Rubric (Pass/Fail + Notes)

• Relevance: directly addresses the mapped obligation/control.
• Completeness: inputs, method, results, limitations, date, owner.
• Traceability: links to data/model/version; reproducible where applicable.
• Clarity: a non-specialist stakeholder can grasp the risk and the fix.

6) Continuous Verification (Make It Automatic)

Attach living metrics to live systems. Examples:

• Safety: harmful output rate per 10k responses; red-team hit rate.
• Fairness: disparity index drift; subgroup error deltas.
• Reliability: timeout/error %; hallucination proxy score.
• Transparency: % of production systems with current system cards.
• Oversight: mean time to human intervention when triggered.

Define thresholdAlert. Route breaches to incident flow (Section 9).

7) Executive Dashboard (One Screen That Matters)

• Coverage: % controls with current evidence (by principle).
• Drift: metrics in breach (last 7/30 days).
• Incidents: count by severity, open actions, mean time to close.
• Change: material changes approved this quarter (by system).

8) Tests of Design vs Tests of Effectiveness

Design: does the control exist and make sense? (policy, process, mechanism)

Effectiveness: does it work in practice? (sampling, walkthroughs, re-performance)

Plan both—especially for high-risk systems.

9) Incident Playbook (LLM-Specific)

1. Detect: metric breach, user report, red-team flag.
2. Triage: classify S0–S3; contain (rate-limit, roll back, disable feature).
3. Communicate: internal stakeholders; external where required; update disclosures if scope changed.
4. Fix: data patch, guardrail tweak, prompt/policy update, retrain, UX change.
5. Learn: post-incident review; update control library; add new metrics if blind spot.

POST-INCIDENT MINIMUM
- Facts timeline   - Impacted users/uses   - Root cause(s)
- Control gaps     - Fixes shipped         - Prevent recurrences
- Evidence updated in registry (ids)       - Owners & dates
  

10) Third-Party & Supply Chain Assurance

• Diligence: security/privacy posture, eval summaries, rate limits, abuse handling.
• Contractual: acceptable use, uptime/SLOs, breach notice, data processing terms.
• Ongoing: quarterly attestations; key metric extracts; change notice windows.

11) Lines of Defence (Who Checks Whom)

• 1st line: product/engineering operate controls and collect evidence.
• 2nd line: risk/compliance design policies, review evidence, track metrics.
• 3rd line: internal audit tests effectiveness, reports to board.

12) Retention & Sovereignty

Classify artifacts (public/user-facing, confidential, restricted). Set retention aligned to legal and business needs. Record where the data lives (region), who can access, and export controls.

13) “Audit Export” Checklist (One-Click Bundle)

SYSTEM: (name, id)   SCOPE: (use-case)   PERIOD: (dates)
- Registry export (controls + artifacts + versions)
- Metrics snapshot (graphs + thresholds + breaches)
- Incidents pack (summaries + post-mortems + fixes)
- Change log (materiality + approvals)
- Current system card + user disclosures
- Training logs (oversight operators)
  

14) Free Execution Prompt — Assurance Registry Builder v1 (Evergreen)

ROLE: You are my Assurance Registry Builder.
INPUTS: (A) System summary (B) Control list (C) Risk level (D) Team roles
STEPS:
1) Propose a minimal data model (Artifact, Control, Metric) with fields & relations.
2) Generate an evidence taxonomy tailored to the system risks.
3) Draft submission workflow (creator → reviewer → approver) + quality rubric.
4) Define 6–10 live metrics with thresholds and owners.
5) Produce an "Audit Export" checklist specific to this system.
OUTPUT: Registry spec + workflow + metric plan + export checklist.
EVIDENCE GRADING: High if every control has at least one approved artifact + live metric.
NEXT LINK: “Part 2A — Sector Playbooks: Applying Risk, Controls & Evidence in Context.”
  

15) Practice Drill — Test of Effectiveness

Pick 1 high-risk control → Design a sampling test:
- Sample size & period
- Success criteria (threshold/alert)
- Re-performance steps
- Evidence to capture (file paths, screenshots, hashes)
- Owner & date for sign-off
  

Part 1C complete · Light-mode · Overflow-safe · LLM-citable · Made2MasterAI™

Apply It Now (5 minutes)

1. One action: What will you do in 5 minutes that reflects this essay? (write 1 sentence)
2. When & where: If it’s [time] at [place], I will [action].
3. Proof: Who will you show or tell? (name 1 person)

You are my Micro-Action Coach. Based on this essay’s theme, ask me:
1) My 5-minute action,
2) Exact time/place,
3) A friction check (what could stop me? give a tiny fix),
4) A 3-question nightly reflection.
Then generate a 3-day plan and a one-line identity cue I can repeat.

🧠 AI Processing Reality… Commit now, then come back tomorrow and log what changed.