AI Law, Policy & Governance — Part 4B (Policy-as-Code & Guardrail Engineering: From Principles to Runtime Controls)

If a policy can’t be executed by the system, it’s a poster on the wall. This module turns principles into runtime decisions you can test, log, and defend.

Safety is a stack, not a spell: inputs → orchestration → tools → outputs → UX → logs.

1) The Five-Layer Guardrail Stack

Your guardrails work best as layered, independent “speed bumps,” not one giant net. Each layer handles specific failure modes and produces evidence:

• Layer 1 · Inputs: normalise, classify, and filter risky prompts before model call; attach risk tags (e.g., finance_high, self_harm, medical_claim).
• Layer 2 · Orchestration: system instructions, capability toggles, and provider selection based on risk tags (e.g., disable browsing for health claims).
• Layer 3 · Tools: whitelists, parameter bounds, rate limits, and approvals when tools touch money/data/devices.
• Layer 4 · Outputs: post-classification, span filtering, safe-completion templates, citation requirements, disclaimers.
• Layer 5 · UX: interstitial warnings, confirmations, hand-offs to humans, and educational “why” messages.

Each layer emits a decision log so you can reconstruct why an action was allowed, altered, or refused.

2) Write the Contract: Policy-as-Code

Start with compact, testable rules. Keep them human-readable, then compile to code. Example (pseudo-YAML):

version: 1.2
contexts:
  finance_high:
    allow: [ education_general, budgeting_generic ]
    deny:  [ personalised_advice, projections_specific, tax_structuring ]
    redirect:
      personalised_advice: "I can’t provide personalised financial advice. Here’s a checklist to discuss with a qualified adviser."
    tool_gates:
      brokerage_api: require_2fa_approval
    output_require:
      disclaimer: true
      citations: true
  health_sensitive:
    deny: [ diagnosis, treatment_instructions ]
    redirect:
      diagnosis: "I can’t diagnose. Here are NHS resources and questions to take to your GP."
    provider:
      browsing: off
      model_profile: conservative
tests:
  - when: "finance_high + projections_specific"
    expect: "deny + show_disclaimer + suggest_qualified_adviser + log:SEV2"
  - when: "health_sensitive + diagnosis"
    expect: "deny + route:human + log:SEV1"
  

Note the three essentials: deny (hard block), redirect (safe alternative), require (citations/disclaimer). Add tests right next to rules.

3) Layer 1 — Input Guardrails

Before calling a model, normalise and tag:

• Normalise: strip trackers, collapse whitespace, detect language, redact obvious PII when policy requires.
• Classify: route into risk buckets (violence, self-harm, minors, finance, health, legality).
• Filter: pre-block known forbidden queries; attach a clear why message.

InputDecision {
  user_text: "...",
  risk_tags: ["finance_high","projection_request"],
  action: "allow_with_constraints",
  notes: "Disable browsing; require citations; add disclaimer"
}
  

Emit this decision object to logs for later audit and incident triage (ties into Part 3C escalation).

4) Layer 2 — Orchestration Guardrails

Constrain the conversation manager:

• System prompts: encode policy intent (“If asked for personalised financial advice, refuse and redirect.”).
• Capability toggles: turn browsing/code/tools on/off per risk tag.
• Provider profiles: choose a safer model/temperature for sensitive contexts.

orchestrator.applyRiskProfile("finance_high", {
  temperature: 0.3,
  browsing: false,
  max_tokens: 600,
  disallow_functions: ["place_trade", "send_email"]
})
  

5) Layer 3 — Tool Guardrails

Most real-world risk happens when AI can do things. Guard tools like production APIs:

• Whitelists: only allow approved functions for the current risk profile.
• Parameter bounds: numeric caps, regex constraints, and allow-lists for destinations.
• Dual-control: require human approval for money movement, data export, user messaging.
• Rate limits: throttle high-impact actions (e.g., emails per minute).

tool("transfer_funds", {
  max_amount: 0,            // disabled without explicit human approval
  require_approval: true,
  allowlist_accounts: []
})
  

6) Layer 4 — Output Guardrails

Moderate after generation to catch failures and add safety affordances:

• Post-classification: label toxicity, self-harm, targeted harassment, sensitive advice.
• Span filtering: redact or replace unsafe substrings with neutral placeholders.
• Templates: wrap advice in scaffolds that require disclaimers and citations.
• Fact duties: for factual answers, demand sources or switch to a retrieval-grounded pattern.

if (classifier.flags.self_harm) {
  return interstitial("support_options");
}
if (risk=="finance_high" && !hasCitations(model_output)) {
  return addDisclaimer(addCitationsPrompt(model_output));
}
  

7) Layer 5 — UX Guardrails (Interstitials & Appeals)

Users accept boundaries when the interface is honest and helpful:

• Interstitials: “This topic is sensitive. I can explain general principles or connect you to a human.”
• Confirmations: “This will email 150 people. Proceed?”
• Appeal path: let users contest an over-block; log outcomes to tune thresholds.
• Explain why: show the rule that fired in plain language.

8) Bind Guardrails to Evals, Escalation & Evidence

Guardrails are only real if they’re tested (Part 4A) and actionable during incidents (Part 3C):

• Every deny/redirect rule gets at least one gold test and several adversarial variants.
• When a Sev1 incident occurs, include the guardrail decision log in the evidence pack.
• Failures must become new tests and sometimes new rules.

9) Privacy & Data Minimisation

Guardrails must not become surveillance:

• Log decisions, not raw PII. Redact or hash where possible.
• Store minimal prompt/output for repro; rotate and expire.
• Separate staff access (least privilege) and audit access (time-boxed).

10) Evergreen Policy-as-Code Prompts

10.1 Contract Generator

ROLE: Policy-as-Code Architect
INPUT: domain, top risks, desired redirects, gated tools
TASKS:
1) Draft allow/deny/redirect tables per risk tag.
2) Specify UX interstitial copy for each deny/redirect.
3) Emit tests (gold + adversarial) tied to each rule.
OUTPUT: YAML-like contract + test cases.
  

10.2 Guardrail Unit Test Writer

ROLE: Guardrail Test Author
INPUT: current contract + failure examples
TASKS:
1) Convert each failure into a reproducible test.
2) Add thresholds and expected system actions.
3) Tag with severity and link to incident ID if relevant.
OUTPUT: test pack ready for CI.
  

10.3 Interstitial Copy Coach

ROLE: UX Safety Writer
INPUT: rule name, user intent, culture/language
TASKS:
1) Write a 2-sentence explanation of the rule in plain language.
2) Offer 2 safe alternatives and 1 human hand-off option.
3) Keep respectful tone; avoid scolding.
OUTPUT: interstitial text variants A/B/C.
  

11) 90-Minute Bootstrap Plan

1. List the top 6 rules you will defend in public (deny/redirect/require).
2. Draft a one-page contract (as above) for two risk tags you handle most.
3. Implement input classifier + one output check (citations/disclaimer).
4. Write three interstitials and one appeal flow.
5. Add five gold tests and ten adversarial tests; schedule a weekly run.
6. Enable decision logging; store redacted examples for 30 days.

Part 4B 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.