A working definition of AI governance for engineers — what it means, how it differs from security and compliance, why you already own a slice of it, and the frameworks and lifecycle map that anchor the rest of this series.
The opener to a governance series for engineers: what AI governance is and how it differs from security and compliance, why engineers produce the evidence (docs, evals, monitors, audit trails), and the frameworks used throughout — NIST AI RMF, the EU AI Act, and ISO/IEC 42001.
Turning the four functions of the NIST AI Risk Management Framework — GOVERN, MAP, MEASURE, MANAGE — into something an engineering team can actually run: a risk taxonomy for LLM apps, a versioned risk register in code, and the eval hooks that keep MEASURE honest.
Make the NIST AI Risk Management Framework actionable: the Govern/Map/Measure/Manage functions in your own words, a risk taxonomy for LLM apps, and a versioned risk register (YAML/Python) capturing per-use-case risks, likelihood/impact, mitigations, and owners.
The evidence layer of AI governance — how model cards, datasheets, system cards, and automated FactSheets turn "trust us" into an auditable paper trail, and why the EU AI Act makes these artifacts the actual deliverable.
The transparency artifacts that make AI governable: model cards, datasheets, and system/use-case documentation — plus a Python snippet that auto-captures lineage (model version, prompt hash, dataset version, eval scores, git SHA) into a card at build time, so docs are generated not hand-maintained.
Governance is only as real as your ability to measure it. This is the MEASURE function of an AI risk program made concrete — a versioned eval set, the metric families that matter for an LLM system, and a CI gate that fails the build when quality regresses instead of just logging a warning.
Governance enforced through evaluation — the Measure function made real: build a versioned eval set, pick the metric families (quality, faithfulness, safety, bias, PII, cost/latency), and turn eval into a CI quality gate that fails the build on regression.
The three trustworthy-AI properties regulators and users press on hardest — where bias enters a system, why the fairness definitions contradict each other so you must choose one deliberately, and why an explanation you can read is not the same as an explanation you can trust.
The trustworthy-AI properties regulators care about: where bias enters, why fairness notions conflict (you must choose one), disaggregated evaluation, mitigation with Fairlearn/AIF360, and explainability (SHAP/LIME) — with the honest caveat that LLM rationales are not faithful explanations.
Governance doesn't stop at deploy. This is the NIST RMF MANAGE function in practice: what to monitor for an LLM system, how to detect the drift — including the silent kind where a provider swaps the model under you — and why the audit trail you log is the regulatory deliverable, not a debugging convenience.
Governance doesn't stop at deploy — the Manage function in production: what to monitor (operational, quality, safety), detecting drift including silent provider-model drift behind moving aliases, and the audit trail (versions + retrieved context, PII-redacted) that becomes regulatory evidence.
How an engineer should read AI regulation without a law degree — what the EU AI Act, ISO/IEC 42001, the NIST AI RMF, and sector rules actually ask for, and how each obligation maps to a control or artifact your pipeline can already produce.
Orient in AI regulation and translate it into controls (not legal advice): the EU AI Act's risk tiers and obligations, ISO/IEC 42001 as a certifiable AI management system, NIST AI RMF, and a crosswalk mapping each obligation to the artifact that satisfies it.
The capstone of this series — assembling roles, a use-case inventory, lifecycle gates, and policy-as-code into a right-sized governance program that produces evidence instead of paperwork, without crushing the velocity of a two-person team or failing an enterprise audit.
The capstone: assemble a right-sized governance program — roles and accountability, a model/use-case inventory as the backbone, lifecycle gates, and policy-as-code that blocks deploy when an owner, risk tier, model card, eval pass, or monitor is missing.
This series is part of a larger body of work by Pratik Dhanave, an Agentic AI Architect writing about production AI systems, distributed systems, and cloud-native engineering. Explore all course series, browse every post, or find topics via the tag index.