AI Governance Playbook for Australian Boards

Table of Contents

1. Introduction: Why AI Governance Matters Now
2. The Five-Pillar Governance Framework
3. Model Selection and Procurement Strategy
4. Claude vs. Competitor Models: A Board-Level Comparison
5. Building Your AI Risk Register
6. Incident Response and Escalation Protocols
7. Audit Readiness and Compliance Pathways
8. Board Reporting and Oversight Cadence
9. Implementation Roadmap: 90 Days to Governance
10. Summary and Next Steps

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Introduction: Why AI Governance Matters Now

Australian boards face a critical inflection point. Artificial intelligence is no longer a future technology—it’s embedded in operations today. Yet governance frameworks haven’t kept pace. Shadow AI adoption is rampant. Model selection decisions are being made without board visibility. Incident response playbooks don’t exist. Compliance pathways remain unclear.

This playbook addresses that gap. It’s built for directors and senior operators at ASX300 entities, mid-market businesses, and growth-stage companies who need to govern AI risk without strangling innovation.

The stakes are real. Boards have fiduciary duties to understand material risks. Regulators are watching. Customers demand transparency. Competitors are moving faster. The Australian Institute of Company Directors has published A Director’s Guide to AI Governance, signalling that AI governance is now a standard board responsibility, not a CTO footnote.

This framework is designed to be implemented in 90 days. It’s outcome-focused: reduce model risk, accelerate safe deployment, pass audits, and maintain board confidence. It’s Australian-specific, acknowledging regulatory expectations, local compliance pathways, and the reality that most boards don’t have AI experts in the room.

Let’s start with the fundamentals.

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The Five-Pillar Governance Framework

Effective AI governance rests on five pillars: discovery, architecture, protocols, oversight, and incident response. These aren’t sequential—they operate in parallel and reinforce each other.

Pillar 1: Discovery and Shadow AI Mapping

You can’t govern what you don’t know exists. The first step is comprehensive discovery: which AI tools and models are in use across the organisation today? This includes ChatGPT, Claude, Copilot, custom models, and vendor-embedded AI.

Start with an audit. Survey department heads, engineering teams, and operations. Ask three questions: What AI tools are active? Who has access? What data flows through them? The answers will shock you. Most boards discover 30-50% more AI usage than expected.

Document findings in a shadow AI register. Categorise by risk level: low-risk (public ChatGPT for brainstorming), medium-risk (proprietary data fed to third-party models), and high-risk (customer data, financial models, decision-making systems). This register becomes your baseline for governance.

Once you’ve mapped the landscape, you can make informed procurement decisions and close gaps. Without discovery, your governance framework is built on incomplete information.

Pillar 2: Governance Architecture

Who owns AI decisions? Most organisations lack clear accountability. This pillar establishes it.

Define three roles:

AI Steering Committee (quarterly): Board-level oversight. Includes CEO, CFO, General Counsel, Chief Risk Officer, and CTO/Head of Engineering. Agenda: strategic alignment, material risks, regulatory updates, major model procurement decisions.

AI Working Group (bi-weekly): Operational execution. Includes Head of Engineering, Security Lead, Compliance Officer, and department representatives. Agenda: model deployment, incident tracking, audit readiness, incident response drills.

Model Review Board (ad hoc): Technical evaluation. Includes engineering leads, security, and data governance. Agenda: new model assessment, procurement evaluation, performance benchmarking.

Clearly document decision rights. Who approves new models? Who escalates incidents? Who owns audit readiness? Written accountability eliminates friction and ensures decisions are documented.

Pillar 3: AI Governance Protocols

Protocols are the guardrails. They define what’s allowed, what requires approval, and what’s forbidden. They’re not bureaucratic—they’re enablers of safe speed.

Establish protocols for:

Model Procurement: Any model handling proprietary or customer data requires Model Review Board approval. Approval criteria: security audit, data handling guarantees, incident response SLA, cost transparency.

Data Handling: Define what data can flow to which models. Customer data? Proprietary algorithms? Financial records? Create a data classification matrix (public, internal, restricted, confidential) and map it against model access rights.

Incident Response: Document escalation thresholds. A model hallucination in a customer-facing system is different from a performance issue in internal analytics. Define who gets notified, when, and what actions are triggered.

Audit Trails: Require logging of all model inputs, outputs, and decisions for high-risk use cases. This supports both incident investigation and regulatory compliance.

Protocols should be written, shared, and updated quarterly. They’re not set-and-forget—they evolve as your AI footprint grows.

Pillar 4: Oversight and Board Reporting

Boards need visibility without drowning in data. Establish a monthly AI dashboard covering:

• Model inventory: Count, category, risk level, deployment status
• Incident tracking: New incidents, open items, resolution status
• Audit readiness: Progress against compliance checklist (SOC 2, ISO 27001 preparation)
• Cost and performance: Model spend, token consumption, latency metrics
• Regulatory updates: New guidance, competitor actions, stakeholder concerns

This dashboard is one page. It’s designed for the board, not technologists. Quarterly, present a deeper deep-dive: strategic model decisions, material risks, and forward plans.

Oversight isn’t about micromanagement. It’s about informed confidence. Directors should understand what’s at risk, what’s being done about it, and whether the pace of deployment matches the maturity of governance.

Pillar 5: Incident Response and Escalation

Incidents will happen. Models hallucinate. Data leaks occur. Vendors have outages. Boards need a playbook.

Define three severity levels:

Severity 1 (Critical): Customer data exposure, regulatory breach, material financial impact. Escalate to CEO and General Counsel immediately. Activate incident response team. Prepare external communications.

Severity 2 (High): Model producing incorrect outputs affecting decision-making, vendor security incident, significant performance degradation. Escalate to AI Steering Committee within 2 hours. Root cause analysis within 24 hours.

Severity 3 (Medium): Model performance issues, minor data handling violations, vendor service degradation. Track in incident register. Review in weekly AI Working Group.

Document a response template: incident description, timeline, root cause, impact assessment, remediation steps, and prevention measures. This forces rigorous thinking and creates an audit trail.

Conduct incident response drills quarterly. Test escalation paths, communication protocols, and decision-making under pressure. Most organisations fail their first real incident because they’ve never practised.

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Model Selection and Procurement Strategy

Model selection is a strategic decision. It affects security posture, cost, performance, and regulatory compliance. Yet many organisations treat it as a technical detail.

The Procurement Checklist

Before any model goes into production, evaluate:

Security and Data Handling

• Does the vendor guarantee data isn’t used for model training?
• What’s the data retention policy?
• Is encryption in transit and at rest included?
• What audit certifications does the vendor hold (SOC 2, ISO 27001)?
• Does the vendor have incident response SLAs?

Performance and Reliability

• What’s the uptime SLA?
• What’s the latency for your use case?
• How does performance degrade under load?
• What’s the cost per token or API call?
• Are there rate limits or throttling?

Regulatory and Compliance

• Does the vendor operate data centres in Australia or jurisdictions you trust?
• What’s their stance on data residency?
• Do they support audit logging for compliance frameworks like SOC 2 or ISO 27001?
• Are they transparent about model training data and potential biases?

Vendor Stability and Support

• What’s the vendor’s financial health and runway?
• Do they have a published roadmap?
• What’s their support SLA for production issues?
• Can you switch models if needed, or are you locked in?

Create a scoring matrix. Weight criteria by risk level. Document the decision and the rationale. This becomes your audit trail and your defence if something goes wrong.

Cost Governance

AI model costs are easy to underestimate. A single poorly optimised prompt can cost thousands monthly. Establish cost controls:

• Budget allocation: Assign model spend budgets by department or project
• Monitoring: Track token consumption and API costs in real time
• Optimisation: Review high-spend models monthly. Optimise prompts, batch requests, or switch models if cost-benefit doesn’t justify
• Forecasting: Project 12-month spend based on current trajectory and planned deployments

Cost governance isn’t penny-pinching. It’s ensuring that AI investments deliver ROI and that no single runaway model drains the budget.

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Claude vs. Competitor Models: A Board-Level Comparison

When evaluating models, most organisations compare Claude, GPT-4, Gemini, and Llama. This section cuts through the hype and provides board-level clarity.

The Contenders

Claude (Anthropic)

• Strengths: Strong reasoning, long context window (200K tokens), constitutional AI training reduces hallucinations, clear data handling commitments, SOC 2 Type II certified
• Weaknesses: Slightly higher cost per token than GPT-4, smaller ecosystem of integrations than OpenAI
• Best for: Complex reasoning tasks, document analysis, customer-facing applications where accuracy matters
• Data handling: Anthropic explicitly states they don’t train on API inputs. This is a material advantage for proprietary data.

GPT-4 (OpenAI)

• Strengths: Largest ecosystem, fastest iteration, strong performance across domains, mature API, extensive third-party integrations
• Weaknesses: Higher cost, OpenAI’s data handling policies have been opaque historically, context window smaller than Claude
• Best for: General-purpose tasks, rapid prototyping, leveraging ChatGPT ecosystem
• Data handling: OpenAI has improved transparency but still less explicit than Anthropic. Requires careful contract negotiation for proprietary data.

Gemini (Google)

• Strengths: Multimodal (text, image, video), strong performance on reasoning, Google Cloud integration, competitive pricing
• Weaknesses: Younger product, fewer production deployments in Australia, integration complexity
• Best for: Multimodal use cases, organisations already on Google Cloud
• Data handling: Google’s data handling policies are transparent. Data residency options available.

Llama (Meta)

• Strengths: Open-source, can be self-hosted, no vendor lock-in, competitive performance, cost-effective
• Weaknesses: Requires infrastructure investment, smaller context window, less mature ecosystem, support is community-driven
• Best for: Organisations with strong engineering teams, high-volume inference, full data control requirements
• Data handling: Self-hosted means complete data control. No vendor data sharing.

The Board Decision Framework

Choose your model based on three factors:

1. Use Case Risk Level

Low-risk (brainstorming, summarisation): Any model works. Optimise for cost.

Medium-risk (customer-facing, internal decision support): Claude or GPT-4. Both have strong performance and clear data handling. Favour Claude for proprietary data due to explicit non-training commitment.

High-risk (financial models, regulatory decisions, customer PII): Claude or self-hosted Llama. Explicit data handling guarantees are non-negotiable. Audit trails and explainability are critical.

2. Data Sensitivity

Public data: Any model. Cost and performance are the drivers.

Proprietary data (algorithms, business logic, customer lists): Claude or self-hosted. Explicit non-training guarantees. Contractual data handling terms.

Regulated data (customer PII, financial records, health information): Self-hosted Llama with full audit logging, or Claude with explicit data residency and audit commitments. Contracts must be ironclad.

3. Organisational Capability

Early-stage AI adoption: Claude or GPT-4. Vendor-managed simplifies deployment. Focus on governance, not infrastructure.

Mature AI operations: Evaluate self-hosted Llama if you have engineering capacity. Long-term cost and control benefits justify infrastructure investment.

Hybrid approach: Many successful organisations use Claude for high-risk, proprietary work and GPT-4 for general-purpose tasks. This balances risk, cost, and capability.

Procurement Red Flags

When evaluating vendors, watch for:

• Vague data handling policies: “We may use your data to improve our models” is unacceptable for proprietary or regulated data
• No audit certifications: SOC 2 Type II and ISO 27001 are table stakes
• Unclear incident response: If they can’t articulate their security incident SLA, walk away
• Lock-in contracts: Long minimum commitments with high exit costs reduce your flexibility
• No data residency options: If they won’t commit to Australian data centres for regulated data, escalate to General Counsel

Document your evaluation. Create a decision memo signed by the Model Review Board. This becomes your governance artefact and your defence in an audit.

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Building Your AI Risk Register

A risk register is your governance spine. It documents every material AI risk, who owns it, and what’s being done about it.

Risk Categories

Model Risk: Hallucinations, bias, performance degradation, adversarial attacks

Data Risk: Unauthorised access, leakage through model outputs, data retention violations, regulatory exposure

Operational Risk: Vendor outages, model unavailability, cost overruns, skill gaps

Compliance Risk: Regulatory changes, audit failures, contractual breaches, disclosure obligations

Reputational Risk: Customer backlash over AI use, media coverage of incidents, stakeholder concerns

The Risk Register Template

For each risk, document:

Risk ID and Title: A unique identifier and clear description

Category: Model, data, operational, compliance, or reputational

Description: What could go wrong? What’s the trigger?

Likelihood: High, medium, low (based on historical data and industry trends)

Impact: Financial, customer, regulatory, reputational. Quantify where possible.

Current Controls: What’s already in place to mitigate this risk?

Residual Risk: After controls, is the risk acceptable?

Owner: Who’s accountable for managing this risk?

Mitigation Actions: What’s being done to reduce likelihood or impact?

Target Completion: When will mitigation be complete?

Status: On track, at risk, delayed, closed

Review the register monthly. Update it as new risks emerge and old ones are retired. Share it with the AI Steering Committee quarterly.

Example Risks

Risk 1: Model Hallucination in Customer Communications

• Likelihood: High (models hallucinate ~5-10% of the time in certain domains)
• Impact: Regulatory breach if false information is provided to customers; reputational damage; customer acquisition cost increase
• Controls: Human review of all customer-facing outputs; automated fact-checking for claims; incident logging
• Residual risk: Medium (controls reduce but don’t eliminate)
• Owner: Head of Customer Operations
• Mitigation: Implement automated fact-checking by Q2; expand human review team; develop customer communication guidelines

Risk 2: Proprietary Data Leakage Through Model API

• Likelihood: Medium (depends on data handling practices and vendor policies)
• Impact: Competitive disadvantage; customer trust loss; potential regulatory breach
• Controls: Data classification policy; model selection criteria requiring non-training guarantees; audit logging
• Residual risk: Low (controls are strong, but residual risk from vendor breach remains)
• Owner: Chief Information Security Officer
• Mitigation: Implement data masking for proprietary inputs; contract negotiation to add explicit non-training clauses; quarterly vendor security audits

Risk 3: Vendor Outage Impacts Production

• Likelihood: Medium (vendor outages happen 2-3 times yearly on average)
• Impact: Service unavailability; customer SLA breaches; revenue impact
• Controls: Vendor SLA commitments; fallback to alternative models; load testing
• Residual risk: Medium (outages are inevitable; mitigation is about speed of recovery)
• Owner: VP of Engineering
• Mitigation: Implement multi-model architecture by Q3; develop failover playbook; conduct quarterly failover drills

A mature risk register has 15-30 active risks. It’s a living document, not a compliance checkbox.

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Incident Response and Escalation Protocols

Incidents reveal gaps in governance. Having a playbook before an incident happens is the difference between controlled response and chaos.

The Incident Response Playbook

Document these steps:

Detection: How do you detect an incident? Automated monitoring (model performance degradation, API errors), user reports, vendor notifications, security alerts.

Initial Response: Who gets notified first? For most organisations, it’s the engineering team and security lead. They assess severity and trigger escalation if needed.

Severity Assessment: Use your three-level framework (Critical, High, Medium). Document the assessment rationale.

Escalation: Based on severity, who gets notified? Critical incidents go to CEO and General Counsel immediately. High incidents go to the AI Steering Committee within 2 hours. Medium incidents are tracked in the incident register.

Root Cause Analysis: For Critical and High incidents, conduct a root cause analysis within 24 hours. Document what happened, why it happened, and what systemic issues it revealed.

Remediation: What actions stop the bleeding? For a hallucination incident, that might be pulling the model offline or adding human review. For a data leak, it’s vendor notification and customer communication.

Prevention: What changes prevent recurrence? This might be process changes, additional controls, or model changes.

Communication: Who needs to know? Customers? Regulators? Investors? Board? Document communication templates and approval workflows.

Post-Incident Review: After the incident is resolved, conduct a blameless post-mortem. What went well? What could improve? Update your playbook based on learnings.

Incident Response Drill Template

Conduct quarterly drills. Use realistic scenarios:

Scenario 1: Model Hallucination in Customer-Facing System

• Customer reports incorrect information provided by AI chatbot
• Severity: High
• Trigger: Escalation to AI Steering Committee
• Timeline: 2 hours to assess, 4 hours to implement fix
• Questions: Who communicates with the customer? What’s our liability? Do we need to notify regulators?

Scenario 2: Vendor Security Breach

• Model vendor discloses a data breach affecting API customers
• Severity: Critical
• Trigger: Immediate escalation to CEO and General Counsel
• Timeline: 1 hour to understand scope, 4 hours to notify customers
• Questions: Did our data get exposed? What was in it? What are our contractual obligations?

Scenario 3: Cost Explosion

• Token consumption spikes 500% overnight due to misconfigured prompt
• Severity: Medium
• Trigger: AI Working Group review
• Timeline: 1 hour to identify root cause, 2 hours to implement fix
• Questions: Who approved the prompt change? What cost controls failed? How do we prevent recurrence?

Drill results inform your playbook updates. If a drill reveals unclear escalation paths, fix them immediately.

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Audit Readiness and Compliance Pathways

Many Australian boards are pursuing SOC 2 or ISO 27001 compliance. AI governance is now part of those audits. Understanding the compliance landscape is essential.

SOC 2 Type II and AI

SOC 2 Type II audits assess your controls across five trust service criteria: security, availability, processing integrity, confidentiality, and privacy. AI systems touch all five.

Security: How do you control access to models and data? Are API keys rotated? Is audit logging in place? Do you monitor for unauthorised access?

Availability: What’s your model uptime? Do you have failover mechanisms? Can you recover from vendor outages?

Processing Integrity: Are model outputs accurate? Do you have controls to detect hallucinations or bias? Is there human oversight?

Confidentiality: Are proprietary data and customer PII protected when fed to models? Do you have data classification and access controls?

Privacy: Do you have consent for using customer data with AI? Are you transparent about AI use? Can customers opt out?

Auditors will ask for evidence: policies, logs, incident reports, testing results. Start documenting now, even if your audit isn’t scheduled for 12 months. The best time to prepare is before the auditor arrives.

For detailed guidance, consult the AI Governance for Australian Boards: Strategies for 2025 resource, which addresses the specific regulatory landscape Australian boards face.

ISO 27001 and AI

ISO 27001 is an information security management system standard. It’s more prescriptive than SOC 2 and covers more ground.

Key controls relevant to AI:

Access Control: Who can use which models? Are access rights documented and reviewed quarterly? Do you have role-based access control?

Cryptography: Is data encrypted in transit and at rest? Are encryption keys managed securely?

Vendor Management: Do your model vendors meet your security requirements? Are vendor agreements in place? Do you conduct vendor security assessments?

Incident Management: Do you have an incident response plan? Are incidents logged and reviewed? Do you conduct post-incident reviews?

Audit and Accountability: Are model inputs and outputs logged? Can you reconstruct what happened and when? Are logs protected from tampering?

ISO 27001 certification is a multi-month project. If you’re pursuing it, start with a gap assessment: which controls are in place, and which need to be built? Prioritise based on risk. Don’t try to boil the ocean.

Vanta and Compliance Automation

Vanta is a compliance automation platform that integrates with your tech stack and continuously monitors compliance posture. For boards pursuing SOC 2 or ISO 27001, Vanta reduces the burden significantly.

Vanta can:

• Automatically collect evidence (logs, policies, configuration snapshots)
• Monitor compliance status in real time
• Alert you to gaps before auditors do
• Streamline audit workflows
• Support continuous compliance, not just point-in-time audits

For AI-specific compliance, Vanta integrates with model vendors and security tools. If you’re planning an audit in the next 12 months, implementing Vanta now will save weeks of manual evidence collection.

For more on this, explore how AI agencies for enterprises in Sydney approach compliance and audit readiness. Many Sydney-based organisations are using compliance automation as a competitive advantage.

The Compliance Roadmap

If you’re not yet SOC 2 or ISO 27001 certified, here’s a phased approach:

Phase 1 (Months 1-3): Assessment and Planning

• Conduct gap assessment
• Define scope (which systems and data are in scope?)
• Identify quick wins (controls you can implement easily)
• Build compliance team (designate owner, allocate budget)

Phase 2 (Months 4-9): Control Implementation

• Document policies and procedures
• Implement technical controls (access logging, encryption, monitoring)
• Train staff
• Build evidence collection processes

Phase 3 (Months 10-12): Readiness and Audit

• Conduct internal audit
• Remediate findings
• Prepare for external audit
• Conduct external audit

This timeline assumes you’re starting from a foundation of basic security practices. If you’re starting from scratch, add 3-6 months.

For guidance specific to Australian regulatory expectations, review Leading the Future – New AI Governance Guidance for Australian Directors, which outlines the regulatory landscape and board obligations.

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Board Reporting and Oversight Cadence

Boards need visibility into AI governance without being overwhelmed by technical detail. Establish a reporting cadence that provides clarity and confidence.

Monthly AI Dashboard

One page, designed for the board. Key metrics:

Model Inventory

• Total models in use: 12
• By risk level: 3 high-risk, 5 medium-risk, 4 low-risk
• New models deployed this month: 1
• Models retired: 0

Incident Tracking

• New incidents this month: 2 (both Medium severity)
• Open incidents: 1 (High, 15 days old, on track for resolution)
• Incidents resolved: 2
• Average resolution time: 8 days

Audit Readiness

• SOC 2 target: Q3 2025
• Readiness: 60% (18 of 30 controls implemented)
• On track: Yes
• Key gaps: Vendor audit schedule, incident response drills

Cost and Performance

• Monthly model spend: $45,000 (on budget)
• Token consumption: 2.1B (up 15% month-over-month)
• Average model latency: 850ms (target: <1s)
• Model availability: 99.7% (target: 99.9%)

Regulatory Updates

• ASIC AI guidance: Published October 2024, no material changes to our governance
• Competitor actions: Thoughtworks published AI governance framework; Deloitte launched AI audit service
• Stakeholder concerns: Customer survey shows 60% want transparency on AI use

This dashboard takes 15 minutes to prepare. It gives directors what they need: are we safe, are we compliant, are we on track?

Quarterly Deep-Dive Presentation

Every quarter, present a 30-minute deep-dive covering:

Strategic Model Decisions: What major models are we considering? What’s the business case? What are the risks?

Material Risk Updates: Which risks have changed? Are new risks emerging? Are mitigation efforts on track?

Regulatory Landscape: What’s changed? What do we need to do differently?

Competitive Positioning: How are competitors approaching AI governance? What can we learn?

Forward Plan: What’s coming in the next quarter? New deployments? Compliance milestones? Capability builds?

This presentation is a conversation starter. It should prompt board questions and debate. If the board isn’t asking hard questions about AI, your governance isn’t working.

Annual Board Governance Review

Once yearly, conduct a comprehensive review of your AI governance framework:

• Is the framework still fit for purpose, or does it need updating?
• Have we experienced incidents that revealed gaps?
• Are we keeping pace with regulatory changes?
• Are we allocating enough resources?
• What should we prioritise next year?

Use this review to update your playbooks, protocols, and risk register. Governance isn’t static. It evolves.

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Implementation Roadmap: 90 Days to Governance

You don’t need to implement all of this overnight. Here’s a 90-day roadmap to get a functional governance framework in place.

Week 1-2: Establish Governance Structure

Actions:

• Define AI Steering Committee, AI Working Group, and Model Review Board
• Assign owners and meeting cadence
• Schedule kickoff meetings
• Communicate to the organisation

Deliverables:

• Governance charter (1 page)
• Meeting calendar
• Role descriptions

Week 3-4: Shadow AI Discovery

Actions:

• Survey departments on AI tool usage
• Document tools, access, and data flows
• Categorise by risk level
• Identify quick wins for control

Deliverables:

• Shadow AI register (spreadsheet)
• Risk categorisation
• Quick win action list

Week 5-6: Model Procurement Framework

Actions:

• Define procurement checklist
• Create vendor evaluation matrix
• Develop model selection criteria
• Document decision approval workflow

Deliverables:

• Procurement checklist (1 page)
• Vendor evaluation template
• Model selection decision log

Week 7-8: Risk Register and Protocols

Actions:

• Build AI risk register (20-30 risks)
• Document data handling protocols
• Create incident response playbook
• Define escalation thresholds

Deliverables:

• Risk register (spreadsheet)
• Protocols document (5-10 pages)
• Incident response playbook (3-5 pages)

Week 9-10: Compliance Baseline

Actions:

• Conduct SOC 2 / ISO 27001 gap assessment
• Identify quick wins
• Plan compliance roadmap
• Assign compliance owner

Deliverables:

• Gap assessment report (10 pages)
• Compliance roadmap (12-month plan)
• Compliance owner assigned

Week 11-12: Board Reporting and Drills

Actions:

• Create monthly AI dashboard template
• Conduct first incident response drill
• Prepare quarterly deep-dive presentation
• Brief the board

Deliverables:

• Monthly dashboard (1 page)
• Quarterly presentation (30 minutes)
• Incident response drill results
• Board briefing completed

After 90 days, you’ll have a functional governance framework. It won’t be perfect, but it will be in place. The next 6-12 months are about refinement, automation, and maturation.

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Summary and Next Steps

AI governance isn’t a one-time project. It’s a continuous practice. This playbook provides the framework. Your job is to implement it, adapt it to your organisation, and keep it alive.

Here’s what you should do this week:

1. Establish Governance Structure Schedule a meeting with your CEO, CFO, CTO, General Counsel, and Chief Risk Officer. Agree on the AI Steering Committee, AI Working Group, and Model Review Board. Assign owners. Schedule recurring meetings.

2. Conduct Shadow AI Discovery Survey your organisation. What AI tools are in use? Who has access? What data flows through them? Document findings. This is your baseline.

3. Define Model Procurement Criteria Create a checklist. What security, performance, and compliance criteria must new models meet? Document it. Use it for every new model decision.

4. Build Your Risk Register Identify 15-20 material AI risks. Document them. Assign owners. Create a mitigation plan. Review monthly.

5. Develop Incident Response Playbook Write it down. Define escalation thresholds. Document response steps. Conduct a drill. Refine based on results.

6. Plan Your Compliance Roadmap If you’re pursuing SOC 2 or ISO 27001, start now. Conduct a gap assessment. Build a 12-month roadmap. Assign a compliance owner.

7. Brief Your Board Schedule a board meeting. Present your governance framework. Agree on the roadmap. Get buy-in. Secure resources.

This playbook is Australian-focused. If you need additional context on local regulatory expectations, consult resources like A Director’s Guide to AI Governance from the Australian Institute of Company Directors, which provides foundational guidance tailored to ASX300 directors and Australian boards.

If you’re implementing AI strategy alongside governance, explore how AI agencies for enterprises in Sydney approach governance as part of broader AI transformation. Many Australian enterprises are using external partners to accelerate both governance maturity and AI capability.

For those seeking deeper operational guidance, AI agency consulting in Sydney can help translate governance frameworks into executable operational practices.

Final thought: AI governance isn’t about slowing down. It’s about going faster with confidence. A mature governance framework lets you deploy models quickly, scale safely, and pass audits. It’s not a cost centre. It’s a competitive advantage.

Start this week. Implement in 90 days. Refine over the next 12 months. In a year, you’ll have a governance framework that your board trusts, your organisation understands, and your auditors will respect.

The time to act is now. AI isn’t coming—it’s already here. Govern it well.