AI Law, Policy & Governance — Part 2B (Standards in Practice: ISO/NIST Without the Jargon)

A practical Rosetta that turns “framework speak” into daily engineering habits, live metrics, and exportable evidence.

Standards don’t run your system—habits do. Map once, build habits, wire metrics, and let audits be an export.

1) The Rosetta Clusters (Map Anything to These)

• Data Governance: provenance, rights, minimisation, retention, sensitive data handling.
• Safety & Robustness: evaluations, red-teaming, abuse testing, limitation notes.
• Fairness & Accessibility: disparity checks, representation audits, accessible UX.
• Security: threat modelling, hardening, SBOM, key management, incident response.
• Transparency: model/system cards, user notices, consent and limitations.
• Human Oversight: thresholds for intervention, runbooks, authority to stop/rollback.
• Monitoring & Incidents: KPIs/KRIs, drift alerts, post-incident learning.
• Culture & Records: policies, training, ownership, change control, reviews.

2) The “No-Jargon” Mapping of Common Frameworks

This section shows how widely taught AI/assurance frameworks typically align with the clusters above. Use it as a pattern (not a legal statement):

2.1 Risk Management (e.g., AI-focused risk frameworks)

• Govern ↔ Culture & Records · Oversight · Transparency
• Map ↔ Data Governance · Context & Use · Intended/Out-of-scope
• Measure ↔ Safety/Robustness · Fairness · Security · Metrics
• Manage ↔ Controls Implementation · Monitoring · Incidents · Change

2.2 AI Management Systems (e.g., AI-specific “management system” standards)

• Policy & Scope ↔ Culture & Records · Transparency
• Planning & Risk ↔ Data · Safety · Fairness · Security
• Operation ↔ Oversight · Monitoring · Incidents
• Performance eval & Improve ↔ Metrics · Post-incident reviews · Change control

2.3 Information Security (e.g., security management baselines)

• Asset & Access ↔ Data Governance · Security
• Change/DevSecOps ↔ Operation · Oversight · Records
• Incident ↔ Monitoring & Incidents · Learning

Result: a single clustered control library can satisfy multiple frameworks with different labels.

3) Minimal Control Library (Copy-Ready)

CONTROL: Data-Provenance-01
Goal: Every training/eval/deploy dataset has known origin, rights, and retention.
Evidence: Data ledger entries + DPIA notes (where relevant). Owner: Data Steward.

CONTROL: Safety-Evals-02
Goal: System passes task-specific, abuse, and red-team evals before go-live.
Evidence: Eval reports + limitations + mitigation notes. Owner: Safety Lead.

CONTROL: Fairness-Checks-03
Goal: Track subgroup error/disparity and act on thresholds.
Evidence: Pre-deploy check + runtime dashboard with alerts. Owner: DS Lead.

CONTROL: Security-Model-04
Goal: Threat model + hardening of model/service & supply chain (SBOM).
Evidence: Threat model file + pentest/red-team results + patch logs. Owner: Security.

CONTROL: Transparency-05
Goal: Publish system card & point-of-use notices suited to audience.
Evidence: System card vX + UX screenshots of notice. Owner: Product/Comms.

CONTROL: Oversight-06
Goal: Trigger thresholds for human review + rollback authority.
Evidence: Runbook + logs of interventions. Owner: Operations.

CONTROL: Monitoring-07
Goal: KPIs/KRIs wired; drift & incident response.
Evidence: Dashboard screenshots + incident reviews. Owner: Risk.

CONTROL: Records-08
Goal: Policies, training, change control, periodic reviews exist and are current.
Evidence: Policy links + training logs + change diffs + review minutes. Owner: Governance.
  

4) Evidence That “Proves It” (One Page Per Control)

• Header: control id · owner · status · last review · next review
• Summary (≤200 words): what the evidence shows
• Links: file paths/URLs to artifacts (versioned)
• Metrics: KPI/KRI name, target, threshold, owner
• Decisions: approvals/waivers + reasons

5) Continuous Verification — KPI/KRI Starters

• Safety: harmful-output rate (per 10k), jailbreak success rate, hallucination proxy
• Fairness: subgroup error disparity, content representation mix
• Security: patch latency, failed auths, dependency exposure
• Oversight: time-to-human-intervention, rollback frequency
• Transparency: % systems with current system cards

6) The 90/10 Rule for Audits

Spend 90% of effort making the system safe, fair, secure, and clear; 10% exporting proof. If you’re collecting proof while you build, audits are packaging—not projects.

7) Free Evergreen Prompts

7.1 Framework Rosetta Builder

ROLE: You are my Framework Rosetta Builder.
INPUTS: obligation text (bullets), system summary (50–120w).
STEPS: cluster verbs → pick controls → assign owners → list 1–2 artifacts/control → define 6–10 metrics.
OUTPUT: minimal control library + evidence registry entries.
NEXT: generate "audit export" checklist.
  

7.2 Policy Snippet Generator

ROLE: Policy Writer. TASK: draft a 120–180w snippet for (transparency|oversight|red-teaming).
INCLUDE: purpose, scope, responsibilities, cadence; avoid tool names; match cluster language.
OUTPUT: snippet + owner + review cadence.
  

7.3 Control→Evidence Mapper

ROLE: Assurance Engineer. INPUT: control id + risk level.
OUTPUT: required artifacts, acceptance criteria, storage path, versioning rule, reviewer role, next review date.
  

8) Change Control (Materiality in Plain English)

• Material if it increases impact, expands audience, changes data/decision, alters failure modes.
• Response: rerun evals, refresh system card, re-check fairness, update runbooks, schedule review.
• Evidence: change diff + approvals + date + owners.

9) The One-Screen Governance Dashboard

• Coverage: % controls with current evidence (by cluster)
• Breach: metrics in alert (7/30 days)
• Change: material changes approved this period
• Incidents: open actions, time-to-close

10) Practice Drill — From Obligation to Habit (15 minutes)

1. Pick 5 obligations; bold the verbs.
2. Map each to a Rosetta cluster and to one control.
3. List one artifact and one metric per control.
4. Assign owners and a review cadence.
5. Add them to your assurance registry (from Part 1C).

Part 2B 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.