All field notes

GDPR shapes how AI can use personal data. The principles that matter—lawful basis, minimization, automated decisions—and how to build AI that respects them.

Healthcare AI touches protected health information, so HIPAA shapes the build. The safeguards that matter, and how to engineer compliant medical AI.

SOC 2 is a common security bar for AI vendors—but a report isn't a rubber stamp. What it covers, what it proves, and how to read one when choosing a partner.

AI bias is a data and design problem with real legal and commercial cost. Where it comes from, how to measure it, and practical ways to reduce it.

When an AI decision is questioned, can you explain it? How to build auditability and accountability into AI so decisions are traceable and defensible.

'The model said so' isn't good enough for high-stakes decisions. What explainability really means, and practical ways to make AI decisions understandable.

Ungoverned models drift, duplicate, and fail silently. What model governance is, and how to manage AI models as controlled assets across their lifecycle.

AI systems add new attack surfaces on top of normal software risk. The AI-specific threats—prompt injection, data leakage, model abuse—and how to defend against them.

Where your data lives is a legal and contractual question AI can't ignore. What data residency means, and how to build AI that respects it.

Banning AI drives it underground; no policy invites risk. How to write an AI acceptable use policy that enables safe adoption instead of blocking it.

AI systems fail in new ways—harmful outputs, leaks, silent degradation. How to build an incident response plan for AI, before you need it.

Should you build AI in-house or outsource it? A clear comparison of cost, speed, control, and capability—and when each actually makes sense.

A freelancer is cheaper; an agency is more reliable. The real trade-offs for AI projects—breadth, continuity, risk—and how to choose for your situation.

A consultant advises; an agency builds. The difference matters because strategy without execution is a deck. How to choose—or get both.

Deep learning is a kind of machine learning—not always the better one. What separates them, and when a simpler model wins on real business data.

Supervised learning predicts from labeled examples; unsupervised finds structure in unlabeled data. What each is for, and which your problem needs.

A chatbot answers questions; an AI agent takes actions. That difference changes everything about risk, control, and design. Which do you actually need?

Every AI in production today is narrow AI—built for specific tasks. What separates it from the general AI of headlines, and why that matters for planning.

AI and machine learning aren't the same thing—ML is one way to build AI. What the terms actually mean, and why the distinction helps you scope projects.

Should your AI run in the cloud or on-premise? The trade-offs in cost, control, privacy, and scale—and how data sensitivity usually decides.

Proprietary LLMs lead on capability; open-source models offer control and privacy. The real trade-offs, and how to choose—often both.

Fine-tuning sounds powerful but is often the wrong first move. When prompting is enough, when fine-tuning helps, and why to try the cheap option first.

Data scientists build models; ML engineers ship them. Confusing the roles is why models get built but never reach production. Which do you need?

A copilot helps a person work; an agent does the work. The distinction shapes control, trust, and design. Which does your use case call for?