Checklist: Audit an AI Vendor’s Copyright & Content Risk
Practical checklist to audit an AI vendor’s copyright and content-reuse risk. Key questions, evidence to request, quick tests, red flags, and next steps.
AI safety
Managed Agents for Non-Engineers: No‑Code Prototyping Guide
Step-by-step no-code guide to prototype a managed agent with clear tasks, human-review checkpoints, permission limits, test flows, prompts, and audit logs.
AI privacy checklist: 10 checks to test an AI tool in an hour
A non-technical, one-hour AI privacy checklist with 10 concrete tests, vendor questions, sample prompts, and a printable team takeaway to verify data safety.
Human-in-the-Loop Checks for Safe AI Automations — Playbook
A practical non-engineer playbook for human-in-the-loop AI checks: where to place review, lightweight workflows, decision thresholds, sampling, escalation and templates.
How Creators Can Stop AI Scraping and Reuse of Content
A step-by-step, non-technical playbook creators can use to detect scraping, reduce exposure, issue takedowns, and deploy practical defenses against AI reuse.
How to Link Apps to an AI Assistant Securely: Consent & Scopes
Practical guide to link apps to AI assistants securely: request minimal scopes, design clear consent, enable revocation, audit access, and test monitoring workflows.
Safely Connect LLMs to Your Apps: OAuth Scopes, Permissions
Step-by-step guide to secure LLM app integrations: choose minimal OAuth scopes, use short-lived tokens and revocation, monitor for anomalies, and vet third parties.
Vet an AI Tool’s Training Data & Privacy: A Practical Checklist
A practical, step-by-step AI training data checklist for small businesses — questions to ask vendors, contract language, simple tests for data leakage, and a scorecard.
Practical framework to audit an AI tool’s data handling
A compact, step-by-step 12‑point checklist to audit an AI tool’s data handling—collection, retention, sharing, training risk, vendor contracts, access controls, and tests.
Spot and Reduce LLM Hallucinations: A Practical Testing Toolbox
A hands-on guide with reproducible tests, prompt templates, verification steps, and production guardrails to reduce LLM hallucinations across workflows.