AI Governance Training | Practical Governance for Responsible AI Use

AI Governance Foundations

What AI governance is and what it is not

Why AI governance focuses on decisions and accountability

The eight pillars of AI governance

Governance across the AI lifecycle

Governance expectations in government and enterprise contexts

Privacy-safe participation and responsible use of AI during activities

Practical activity: Participants use an AI tool to research governance themes, risks and relevant standards for an organisation or industry, then evaluate where the AI-generated response may be vague, incorrect or oversimplified.

Decision Authority and Accountability

Who should approve AI use cases

Accountability for AI-supported decisions

Risk ownership and decision ownership

Human oversight requirements

Escalation pathways and exception handling

Governance differences between government and enterprise

Accountability across the AI lifecycle

Practical activity: Participants complete a governance reflection to examine who currently approves AI use, who owns the associated risk, where human oversight is required and what should trigger escalation.

AI Risk Management

Common AI risks, including bias, model drift, data leakage and operational impacts

Risk appetite and approval thresholds

Governed versus ungoverned AI use

Identifying gaps in informal AI adoption

Assessing impact and likelihood

Selecting controls and assigning ownership

Documenting risk acceptance and escalation decisions

Practical activity: Participants identify missing controls and accountability in an ungoverned AI scenario.

Workshop: Participants complete a live AI risk assessment for a customer-facing AI assistant. They identify risk events, calculate risk scores, select controls, assign owners and make an approval, conditional approval, escalation or redesign decision.

Transparency and Assurance

Explainability and defensibility of AI-supported decisions

Transparency as an accountability mechanism

Audit trails, logging and documentation

Evidence required for internal and external assurance

Transparency expectations across different organisational contexts

Communicating AI decisions to non-technical stakeholders

Practical activity: Participants complete a transparency test by evaluating whether an AI-generated explanation is understandable, honest, complete and defensible if challenged.

Regulatory and Policy Alignment

Understanding the AI regulatory and policy landscape

Translating external obligations into internal policies

Government and enterprise governance requirements

Australian AI governance reference points

Using principles, standards and frameworks appropriately

Recognising where requirements are unclear or evolving

Confirming obligations with legal, risk and policy specialists

Practical activity: Participants use AI-supported research to identify regulatory and policy signals relevant to their organisation or industry, evaluate the credibility of the findings and determine what requires professional confirmation.

Data Governance for AI

Data classification and sensitivity

Data provenance and ownership

Privacy-by-design

Retention and deletion requirements

Secure handling of prompts, inputs and outputs

Defining acceptable data boundaries

Matching governance controls to data risk

Practical activity: Participants assess a workplace AI use case, classify the data involved and compare their judgement with risks identified by an AI tool.

Operating AI Responsibly

Integrating AI governance with existing operational practices

Incident and problem management

Controlled change and release management

Knowledge, configuration and asset management

Monitoring AI use after deployment

Assigning responsibility when an AI-related issue occurs

Establishing governance trigger points

Practical activity: Participants define the review, evidence, decision owner and escalation route required when an AI use case involves sensitive data, customer-facing outputs, employment decisions, financial or safety impacts, or significant vendor changes.

Practical Adoption and Value

Minimum viable AI governance

Risk-proportionate governance

Lightweight governance patterns

AI use-case triage

Balancing implementation speed with responsible oversight

Integrating governance into business value streams

Supporting innovation without unnecessary bureaucracy

Practical activity: Participants design minimum viable governance for an AI use case by defining its accountable owner, approval process, permitted data boundaries and review cadence.

Governance Toolkit and Artefacts

AI policies and acceptable-use rules

AI use-case registers and AI registries

Risk assessments

Governance committee charters

Approval workflows

Monitoring registers

Connecting governance artefacts across the AI lifecycle

Selecting governance artefacts proportionate to organisational risk and maturity

Participants examine how governance artefacts make AI decisions visible, repeatable and defensible, including what information should be captured in an AI registry.

AI Governance Board Simulation

Participants act as an AI Governance Board and review a proposed customer-facing AI assistant that uses customer conversation data.

The group must:

Identify the most significant risks

Define non-negotiable controls

Establish appropriate data boundaries

Determine required human oversight

Identify evidence required before approval

Assign an accountable owner

Define escalation and review requirements

Approve, approve with conditions, escalate, reject or redesign the proposed use case

Document a defensible decision rationale.

Skills and Workforce Expectations

AI literacy and safe-use training

Role-specific capability requirements

Technical, risk, ethical and communication skills

Critical thinking and responsible judgement

Workforce capability differences across organisational contexts

Continuous learning as part of ongoing governance

Participants consider where capability gaps exist and how targeted AI upskilling can reduce governance risk.

30-60-90 Day Governance Action Plan

Participants convert the course content into a practical implementation roadmap:

First 30 Days: Establish the Foundations

Identify accountable owners

Document current AI use

Begin an AI use-case register

Establish an initial policy or acceptable-use position

Days 31 to 60: Introduce Governance Controls

Introduce risk assessment and approval steps

Define data boundaries

Establish escalation pathways

Pilot governance processes on selected AI use cases

Days 61 to 90: Embed Operational Governance

Introduce monitoring and review cycles

Review approved use cases

Establish reporting arrangements

Embed governance and training into business-as-usual practices

Each participant identifies priority actions, accountable stakeholders, evidence of progress and key dependencies.