PII handling, vendor risk, model evaluations, and policies to meet GDPR/CCPA and internal standards.
Short answer: no AI cant be 100% trusted. Long answer: you can make AI reliably useful by changing the environment it works in – evidence rules, approvals on risky actions, privacy modes, tight retention, and simple evaluations. This post gives you a 6-rule operating manual you can roll out in weeks, not months. For deeper … Read more
Wrong answers are annoying. Compliance incidents are costly. This guide shows how to keep AI mistakes from reaching customers, contracts, or systems of record. You will set clear thresholds for auto vs review, require citations for risky work, and keep an audit trail that stands up to scrutiny. Deeper context lives in Evaluations & Guardrails, … Read more
Log enough to prove safety — not so much that you create risk. This guide shows exactly what to store for LLM workloads, how long to keep it, and how to purge safely. Two tracks: Manager Mode for policy decisions, Builder Mode for schemas, redaction, rotation, and audits. Quick Summary Keep minimal, structured logs: who/when/what … Read more
Need a plain-English EU AI Act guide? This post gives non-lawyers a practical path: map your AI use cases to risk tiers, add the right disclosures and controls, and keep lightweight proof. Two tracks: Manager Mode for decisions and rollout, Builder Mode for templates and fields. Quick Summary Most SME marketing and ops use cases … Read more
Quick take: You can run a fast, defensible DPIA for common AI use cases in one hour. This walkthrough gives you a time-boxed agenda, copy-paste templates, a simple risk matrix, and a sign-off checklist that satisfies GDPR expectations while keeping teams moving. Quick Summary 10 min – scope & lawful basis 10 min – data … Read more
