What is an Assurance Pack?

“Trustworthy AI” often fails in the boring place: evidence. Teams can do good work—thoughtful evaluation, careful rollouts, honest risk reviews—and still struggle to explain it later.

An Assurance Pack is a lightweight, reviewable evidence bundle that answers one question:

What do we believe about this system, and what evidence supports that belief—today?

What’s inside

An Assurance Pack is intentionally small and structured. At minimum, it includes:

1. Intended use
   Scope, assumptions, constraints, and “don’t use it for X” boundaries.

2. Evaluation results
   Reproducible evaluations with clear pass/fail criteria, including known failure modes.

3. Risk register
   Hazards, mitigations, owners, and a review cadence—kept current as realities change.

4. Monitoring plan
   The live signals that tell you if the system is staying inside its intended behavior.

A simple structure (example)

You can think of it like a “README for assurance” with machine-checkable metadata:

system:
  name: "Support Copilot"
  version: "2026.01.2"
intended_use:
  in_scope: ["drafting replies", "summarizing tickets"]
  out_of_scope: ["medical advice", "legal advice"]
evaluations:
  - name: "hallucination-rate"
    dataset: "tickets_eval_v3"
    metric: "unsupported_claims_per_1k_tokens"
    threshold: "<= 0.8"
risks:
  - risk: "PII leakage"
    mitigation: "redaction + policy filter"
    owner: "security@org"
monitoring:
  - signal: "policy_blocks_rate"
    alert: "sudden increase"

Why it works

Most assurance efforts fail because evidence becomes stale. The trick is to make evidence change-friendly:

• When a model updates, the pack updates with it.
• When a dataset changes, the evaluation references the new version.
• When production behavior drifts, monitoring alerts are traceable back to the intended use and prior results.

An Assurance Pack is less about paperwork and more about closing the loop between what you tested and what you ship.

Who it’s for

Assurance Packs are useful whenever someone needs to review, approve, or operate a system:

• Engineers and ML teams who need reproducible evaluation + release criteria
• Compliance / risk teams who need a living risk register tied to evidence
• Operators who need “what should we watch?” and “what’s the escalation path?”

If you have ever said “we evaluated it, but I can’t find the results,” you need an Assurance Pack.