AI Governance and Responsible AI

Why Smarter. Most discussion of AI governance stays at the level of principles, because students have no system on which to try them. In Smarter, governance is concrete, and every control is a short YAML manifest that a policy, ethics, law or business student can read, change and apply without writing any code:

  • Guardrails that inspect every message. Smarter ships with eighteen built-in guardrails, for personal data, secrets, prompt injection, jailbreaks, moderation, self-harm, off-topic requests, regulated advice, invented citations and privacy requests. Each one states its stage (the user’s message, the model’s reply, or both), its strategy (keywords, regular expressions, detectors, semantic similarity, a moderation model or an LLM judge), its action (log, flag, redact, transform, block, or escalate to a person) and its mode: enforce, or monitor, which records what it would have done. The pipeline that runs them is software, not an instruction that a model may ignore.

  • Budgets that stop spending. A Budget limits dollars or tokens per hour, day, week or month, for an account, a user, an application, a provider or a self-hosted model, and blocks or warns at the limit. The seven example budgets include a monthly allowance for each student and a daily limit on one provider.

  • Access that follows ownership. Every resource belongs to an account and a user, and every query is filtered by owner. Provider keys and passwords are encrypted Secrets, never shown to users or models.

  • An audit trail that traces each answer. Every guardrail intervention is a guardrail event with its severity, confidence, rationale and review status; every prompt, tool call and plugin selection is recorded with the person and account that asked; and the journal records every change to a resource.

  • Data that stays in the network. With LLMHost, open models run on the organization’s own cluster, so a class can compare the governance of a commercial model with that of a self-hosted one.

Students can see how AI is controlled in practice, change those controls themselves, and find the parts of a policy that no software can enforce.

What the course covers. How AI systems fail, from invented citations to runaway costs; the NIST AI Risk Management Framework, ISO/IEC 42001, the OECD AI Principles and the EU AI Act; mapping risks for a real application; why a rule in a system prompt is a request while a guardrail is a control; the guardrail toolbox, and measuring controls by their false positives and false negatives; budgets and cost accounting as controls; access, secrets, personal data and data residency; audit, retention and the privacy of the audit trail itself; fairness and explainability; human oversight and incident response; and vendor and model risk.

Look at.

Each course plan lists every manifest and file that it uses under Smarter in the Source Code.

Read. Smarter Guardrail, Smarter Budget, Smarter Prompt, Cost Accounting, Smarter Journal, Security.

Exercises.

  • Write an acceptable use policy for AI in a fictional organization, then implement as much of it as possible as guardrail and budget manifests. Discuss which parts of the policy cannot be enforced by software, and why.

  • Compare a rule written in a system prompt with the same rule enforced by a guardrail. Try to get around each one, and explain the difference.

  • Run a new guardrail in monitor mode, read the events it records, and decide whether it is ready to enforce.

  • Pick one answer from the journal and trace it back through the guardrail checks, tool calls and model that produced it. Discuss what an auditor or a regulator would want to see.

Course plans. Three complete, ready-to-teach plans, each with a schedule, graded assignments and rubrics, two capstone projects, recommended texts, further study and technical prerequisites. See Course Formats and Grading for what the formats have in common.

See the Smarter Instructor Guide to install Smarter and set up a class.