How to Use This Page
If you completed the self-assessment in Before You Continue, you evaluated your own readiness as a reader. This page gives you the tool for what follows: a framework to evaluate individual AI outputs every time you use them.
Before You Paste, Trust, or Automate
Seven questions to run through before acting on any AI result.
The Decision Path
Every AI output you’re about to act on splits into one of two paths — and which path it takes is up to you.
Before you act: have you matched this output to a level on the Risk Ladder below? Not sure which level applies? Round up rather than down.
Controlled Path — Yes
- Apply the level’s rule — verify, review, or use freely, as the ladder specifies.
- The output enters the real world with the review it needed.
Uncontrolled Path — No
- The output enters the real world unchecked.
- If it’s wrong, the consequence matches the tier you skipped — for example:
Every tier has its own examples and rule — see the full Risk Ladder below.
The Risk Ladder
Use this ladder to decide how much review, verification, and control an AI output needs before it affects the real world.
Every AI output carries a risk level. The ladder maps that level to a rule. When stakes are unclear, go up one level.
- Use case examples
- Secrets, credentials, sensitive personal data, privileged company data, automated execution authority
- Why it matters
- Exposure cannot be undone; access cannot be easily revoked.
- Rule
- Do not paste or connect unless policy, access controls, and monitoring already exist.
- Use case examples
- Payments, trading, hiring/firing, production deployment, safety-impacting decisions
- Why it matters
- Errors cause direct, potentially irreversible harm.
- Rule
- No autonomous action without formal controls, logging, and documented human approval.
- Use case examples
- Legal, medical, financial, HR, security, compliance, or regulated content
- Why it matters
- Errors carry professional or regulatory consequence.
- Rule
- Verify against primary sources or qualified experts.
- Use case examples
- Internal memos, customer emails, summaries, routine analysis
- Why it matters
- Errors reach others or influence decisions.
- Rule
- Review before sending or acting.
- Use case examples
- Brainstorming ideas, rewriting drafts, generating content
- Why it matters
- Errors have low consequence and are easily caught.
- Rule
- Use freely, with normal judgment.
A note on the examples: Knight Capital and Wells Fargo were conventional automation, not AI. They’re included because the same operating principles apply once a consequential decision is delegated to software.