AI Governance and Responsible AI: Classroom Bootcamp

An eight-hour classroom bootcamp that turns AI governance from principles into practice. Students write an AI policy, then enforce as much of it as they can with guardrails, budgets and access controls, and audit the results in Smarter’s journal. No programming is needed. See Course Formats and Grading for the standard bootcamp day, the grading scale and the course policies, and AI Governance and Responsible AI for an overview of the subject.

At a Glance

Students

Policy, ethics, law, compliance, risk and business students and professionals.

Deployment

A shared Smarter instance run by the instructor, with the built-in guardrails and budget examples applied, and an account per team so that teams can audit their own journal.

Cost

Expect less than $2 USD of model charges per student, capped by a per-student Budget.

Learning Objectives

By the end of the bootcamp, students can:

  1. Explain the main frameworks for AI governance (NIST AI RMF, the EU AI Act, ISO/IEC 42001) in plain language.

  2. Translate an AI use policy into enforceable controls, and identify the parts that software cannot enforce.

  3. Explain the difference between a rule in a system prompt and a rule enforced by a guardrail, and demonstrate it.

  4. Trace an AI answer back through the guardrails, tools and model that produced it, as an auditor would.

  5. Design spending controls and access rules for an organization.

Technical Prerequisites

  • Required: a laptop with a current web browser, and experience of using a chat assistant.

  • Required: reading a YAML file well enough to change a value (Learn YAML in Y minutes, 15 minutes).

  • Helpful: familiarity with a compliance regime such as GDPR, HIPAA, SOC 2 or a sector regulator’s guidance.

  • Not needed: programming.

Schedule

Block

Min

Content

1

45

Lecture 1: Why AI needs governance. How a language model works, in ten minutes. Real incidents: invented citations, leaked data, unauthorized promises, runaway costs. The NIST AI RMF functions (govern, map, measure, manage) and the risk tiers of the EU AI Act.

2

45

Lab 1: Map the risks. For an assistant chosen from the built-ins (customer_support, data_analyst or workplace_assistant), list what could go wrong, who would be harmed, and how likely it is. Produces Assignment 1.

3

45

Lecture 2: Instructions versus controls. A rule in a system prompt is a request that a model may ignore; a Guardrail is checked by software on every message. Live demonstration of prompt injection against a system prompt, then against a guardrail.

4

45

Lab 2: Try to get around it. Teams attack an assistant that has a rule only in its system prompt, then the same assistant with a guardrail, and record each attempt and result. Produces Assignment 2.

5

45

Lecture 3: The guardrail toolbox. Input and output stages, strategies (keyword, regular expression, detector, moderation, semantic similarity, LLM judge), enforce and monitor modes, and actions (block, redact, flag, escalate, transform). A tour of the built-in guardrails: personal data, secrets, regulated advice, off-topic, invented citations, self-harm.

6

45

Lecture 4: Money and access. Budgets per user, application and account; cost accounting; accounts, roles and API keys; encrypted Secrets. Who should be able to do what.

7

45

Lab 3: From policy to manifest. Write a one-page acceptable use policy for a fictional organization, then implement what you can as guardrail and budget manifests, and mark what you cannot. Produces Assignment 3.

8

45

Lecture 5: Audit and accountability. The journal and Prompt records: who asked what, which model answered, which tools ran and which guardrails intervened. What a regulator, an auditor or a court would ask for. Privacy of the audit trail itself.

9

45

Lab 4: Audit an answer. Pick one answer from the journal, and reconstruct the chain of events that produced it. Ungraded.

10

45

Lecture 6: What software cannot do. Bias, fairness and explainability; human oversight; accountability; incident response; vendor and model risk. Self-hosted models with LLMHost when data must not leave the network.

11

30

Capstone kickoff. Students choose a capstone and an organization, and outline their approach with the instructor’s feedback.

Graded Assignments

Assignment 1: Risk Register

Students produce a risk register for one built-in assistant: at least eight risks, each with the people affected, likelihood, impact and a proposed control.

Turn in: the risk register as a table (spreadsheet or document).

Criterion

Pts

Exemplary

Developing

Range of risks

6

Covers accuracy, privacy, security, fairness, cost and reputation.

Mostly one kind of risk.

Specificity

5

Risks are specific to this assistant’s job and users.

Generic risks of AI.

Rating

4

Likelihood and impact are rated consistently, with reasons.

Ratings without reasons.

Controls

5

Each control is realistic, and names whether it is technical or organizational.

Controls missing or vague (“monitor closely”).

Assignment 2: Prompt Rule versus Guardrail

Teams record at least eight attempts to get around a rule, first enforced only by the system prompt, then by a guardrail, and explain the difference.

Turn in: an attack log with the prompts and results, taken from the journal, and a one-page explanation.

Criterion

Pts

Exemplary

Developing

Attempts

5

Eight or more, using several techniques.

Repeats of the same attempt.

Evidence

5

Taken from the journal, including guardrail interventions.

Paraphrased from memory.

Explanation

6

Explains why one approach is enforceable and the other is not.

Describes results without explaining them.

Limits

4

Notes what got past the guardrail, or what it blocked wrongly.

Claims the guardrail is perfect.

Assignment 3: Policy to Controls

Students write a one-page acceptable use policy for AI in a fictional organization, and implement as much of it as possible by adapting built-in guardrail and budget manifests.

Turn in: the policy, the manifests, and a table that maps each policy clause to a control or explains why it cannot be enforced in software.

Criterion

Pts

Exemplary

Developing

Policy

5

Clear, specific clauses that an employee could follow.

Principles without concrete rules.

Controls

6

Manifests apply, and each enforces a stated clause.

Manifests unrelated to the policy, or fail to apply.

Mapping

5

Every clause is mapped to a control or a reason.

Clauses left unmapped.

Insight

4

Identifies which clauses need people, training or contracts instead.

Assumes software can enforce everything.

Capstone Projects

Students choose one. Capstones are due one week after the bootcamp.

Capstone A: An AI Governance Program for an Organization

Learning objective. Design a governance program that combines policy, technical controls and human oversight, mapped to a recognized framework.

Students choose an organization, such as a hospital, a university, a bank or a city council, and one AI assistant it plans to deploy. They write the policy, implement the controls in Smarter, and map both to the NIST AI RMF.

What students turn in:

  • A three-page governance document: scope, roles and responsibilities, the policy, oversight and incident response.

  • The guardrail and budget manifests, and a short demonstration (screenshots or video) of each control working.

  • A table mapping each control to an AI RMF function and category.

How it is graded (100 points):

Criterion

Pts

Exemplary

Developing

Fit to the organization

20

Controls reflect the sector’s real risks and rules.

Generic controls that would fit any organization.

Policy and roles

20

Clear owners, decisions and escalation paths.

No one is responsible for anything in particular.

Technical controls

25

Each control works, is shown working, and enforces a stated rule.

Controls applied but not demonstrated.

Framework mapping

20

Accurate mapping, with gaps acknowledged.

Mapping is superficial or wrong.

Writing

15

Clear and concise enough for a board to read.

Long, vague or jargon-heavy.

Capstone B: An AI Incident Investigation

Learning objective. Investigate an AI failure from the audit trail, find its root causes, and recommend controls that would prevent it.

The instructor stages an incident on the shared instance, for example an assistant that gave regulated financial advice or revealed a customer’s personal data. Students investigate from the journal and the manifests, and write an incident report.

What students turn in:

  • An incident report: summary, timeline, evidence from the journal, root causes, impact and recommendations.

  • The manifests that implement the recommended controls, with evidence that they stop a repeat.

  • A one-page briefing note for a non-technical executive.

How it is graded (100 points):

Criterion

Pts

Exemplary

Developing

Timeline and evidence

25

Complete, accurate, and every claim tied to a journal record.

Gaps in the timeline, or claims without evidence.

Root cause analysis

25

Distinguishes technical, process and human causes.

Stops at “the model made a mistake”.

Recommendations

20

Specific, proportionate, and implemented where possible.

Vague, or not implemented.

Verification

15

Shows that the new controls stop a repeat.

No test of the controls.

Executive briefing

15

One page, plain language, clear decisions requested.

Technical detail without a summary.

Further Study