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AI Assurance – Transparency, Auditing & Reporting

Most Australian organisations will never train a frontier model. But almost every organisation will use AI – through SaaS tools, internal projects, or vendor platforms.

You can't control what happens inside the labs. You can control how clearly you explain:

  • What AI you're using
  • How you tested it
  • What could go wrong
  • What you're doing about those risks

That's the job of AI assurance: transparency, auditing and reporting for the AI systems you deploy.

Who this page is for

This page is for:

  • Risk, compliance and internal audit teams
  • CIOs, CTOs, CDOs and digital leaders
  • Governance and safety leaders who need evidence, not slogans

If you've been asked "Where's our AI risk view?" or "How do we know these tools are safe enough?", this is for you.

What this requires

The level of effort scales with your AI risk profile:

Very small teams (1-20 staff): - Start with a simple spreadsheet register (2-4 hours to set up) - One-page safety notes for critical systems (1-2 hours each) - Add AI questions to existing processes (minimal additional time)

Small-medium organisations (20-200 staff): - Structured register with risk ratings (1-2 days initial setup) - Formal safety notes for medium+ risk systems (half day each) - Quarterly board reporting (2-4 hours per quarter) - Annual internal review of high-risk systems (1-2 days per system)

Larger organisations (200+ staff): - Integrated register with workflow automation - Regular audit program - Dedicated governance resources

Most Australian SMEs will sit in the first two categories. The key is starting simple and building systematically as your AI use matures.

Why AI assurance matters now

AI is already showing up in:

  • Productivity tools (copilots, summarisation, email drafting)
  • Customer-facing services (chatbots, triage, recommendations)
  • Decision support (risk scoring, prioritisation, forecasting)

Most of this arrives quietly, bundled into platforms your staff already use. That creates three problems:

  1. Hidden AI – You're making decisions with AI in the loop, without a clear view of where or how.
  2. Unclear accountability – It's not obvious who "owns" the outcome when an AI-assisted decision goes wrong.
  3. Thin evidence – Boards and regulators ask "How do you know it's safe?", and teams have little to show.

The Australian Voluntary AI Safety Standard and international frameworks (ISO/IEC 42001, NIST AI RMF) all point in the same direction: be transparent about your AI, subject it to independent checks, and report honestly on how it behaves in the real world.

Recent research shows a significant gap between aspiration and practice: while 78% of Australian organisations believe they align with AI safety principles, only 29% have implemented the necessary practices. AI assurance helps close this gap by making your safety work visible and verifiable.

The regulatory landscape is evolving. Version 2 of the Voluntary AI Safety Standard is in development (expected late 2025) with enhanced developer guidance, and the government is considering mandatory guardrails for high-risk AI uses. Building good assurance practices now means you're ready when regulations arrive.

SafeAI-Aus turns these principles into practical patterns any organisation can use.

1. Transparency – know where AI is and how it behaves

You can't assure what you can't see. Transparency starts with a clear picture of where AI is used, what it does, and what its limits are.

1.1 AI system register

Set up a simple AI system register and make it part of your normal project and procurement process.

For each AI system, capture:

  • Name and owner – system name, business owner, technical owner
  • Purpose and scope – what it does; which users and customers it affects
  • Type of AI – vendor product, in-house model, embedded feature in an existing platform
  • Data – what goes in (inputs), what comes out (outputs), where logs are stored
  • Risk level – low / medium / high based on impact on safety, rights, finances, or critical services

Examples: - Low: Meeting transcript summarisation for internal use only - Medium: Customer inquiry routing that affects service quality - High: Credit assessment, automated resume screening, or AI that influences decisions about people's access to services or opportunities

  • Controls – human review, policy guardrails, technical filters, access controls, monitoring

Don't aim for perfection on day one. Most Australian organisations discover AI already running in their business—often in SaaS tools staff adopted without formal approval. Start by registering what you know about, then expand as you find more. You'll be surprised what turns up.

SafeAI-Aus resource: Use the AI Project Register Template as a ready-made AI system register with a simple risk rating guide.

1.2 AI safety note

For higher-impact systems, go one step further and create a short AI safety note – a one-page summary that answers:

  • What is this AI system used for?
  • What decisions does it directly influence?
  • What are its known limitations and failure modes?
  • What safety measures and human checks are in place?
  • How can staff raise concerns or report incidents?

This is the document you'd be comfortable sharing with:

  • Your executive team
  • Internal audit
  • A regulator asking about a specific system

SafeAI-Aus resource: Combine the AI Project Register Template with the AI Risk Assessment Checklist to generate AI safety notes aligned to the Voluntary AI Safety Standard guardrails.

1.3 Vendor transparency expectations

Most organisations consume AI via vendors. Build a basic AI transparency checklist into procurement and vendor reviews, for example:

  • Has the vendor documented what AI components are used and for what?
  • Do they provide model / system cards or equivalent?
  • How do they manage training data, privacy and security?
  • What testing have they done on bias, robustness and misuse?
  • What is their incident and vulnerability disclosure process?

You don't need to become a model evaluation lab. You do need to ask clear questions and record the answers.

SafeAI-Aus resource: Plug the AI Vendor Evaluation Checklist into your procurement workflow to standardise transparency questions.

2. Auditing – let someone else check the brakes

For higher-risk uses of AI, independent review is essential. That doesn't always mean hiring a specialist firm. Often it means:

  • Internal audit adding AI to their existing work program
  • Risk and compliance adding AI-specific tests to existing reviews
  • Using a structured checklist so reviewers ask consistent questions

2.1 When to audit AI

Prioritise audits for systems that:

  • Affect safety, health, financial outcomes, or legal rights
  • Make or shape decisions about people (hiring, credit, eligibility, enforcement, benefits)
  • Interact with critical infrastructure or essential services

You can still review low-risk tools, but you don't need the same depth.

2.2 What to audit

For each priority system, an audit should look at:

The checklist below isn't exhaustive, but it covers the critical questions most Australian organisations should be asking. You can tailor it to your context—skip sections that don't apply, add domain-specific questions where needed.

Governance and accountability

  • Is there a named owner?
  • Are roles and decision rights clear (who can change prompts, settings, models)?

Data and privacy

  • What data is processed? Is it appropriate and minimised?
  • How is sensitive or personal information handled?
  • Are retention and deletion controls clear?

Testing and evaluation

  • How was the system tested before going live?
  • Are there any checks for bias, robustness, error rates, harmful outputs?
  • Are performance metrics tracked over time?
  • Have failure modes and misuse scenarios been considered?

Controls and monitoring

  • Are there meaningful human checks where needed?
  • Are logs kept, and are they actually reviewed?
  • Are there guardrails to prevent obvious misuse?
  • Are configuration and prompt changes controlled and traceable?

Compliance alignment

  • How does the system align with internal policies and relevant laws?
  • How does it map to key guardrails in the Voluntary AI Safety Standard?

SafeAI-Aus resource: Pair the AI Risk Assessment Checklist with your internal audit program to create consistent AI assurance workpapers.

3. Reporting – tell the truth about what you find

Assurance only matters if findings reach the right people and drive decisions.

3.1 AI incident and near-miss reporting

Define what counts as an AI incident, for example:

  • AI output caused or significantly contributed to harm
  • AI output would likely have caused harm if a human hadn't caught it
  • AI system failed to operate when it should have (availability / reliability issues)
  • Misuse, abuse, or unexpected emergent behaviour of an AI tool

Then:

  • Add an AI incident category to your existing incident management system
  • Provide a simple AI incident form that captures:
  • System name
  • What happened (or nearly happened)
  • Impact or potential impact
  • Contributing factors and context
  • Immediate response and follow-up actions
  • Make sure staff know they can raise concerns about AI output, and that it's treated seriously

Connect this to existing whistleblower / speak-up channels so people feel safe reporting issues.

SafeAI-Aus resource: Use the AI Incident Report Form to log incidents and near misses consistently.

3.2 Board and executive reporting

Boards don't need every detail. They do need a clear, repeatable view of AI risk and assurance. A simple quarterly one- or two-slide update could include:

  • Inventory – number of AI systems; count by high / medium / low risk
  • Changes – new AI deployments and major changes to existing systems
  • Assurance – audits completed, key findings, remediation progress
  • Incidents – number and severity of AI-related incidents / near misses
  • Forward look – upcoming projects and planned assurance work

SafeAI-Aus resource: The AI Project Register Template captures the inventory, risk levels, and change history you can summarise for executives.

3.3 Learning and iteration

Assurance isn't a one-time exercise. As you collect findings from audits, incidents and monitoring:

  • Track patterns – Are the same types of issues appearing across multiple systems? That suggests a systemic gap in how you're deploying AI.
  • Update controls – When you find a problem, document what you changed and verify the fix actually works.
  • Refresh your register – As systems change or new ones arrive, keep your register current. Stale documentation is worse than no documentation.
  • Share lessons – When safe to do so, consider sharing your experience with the SafeAI-Aus community. Other Australian organisations face the same challenges.

Good assurance creates a feedback loop: transparency reveals risks, auditing finds gaps, reporting drives action, and the cycle repeats with better information each time.

"We're too small for this"

If you're using AI to make decisions that affect people—hiring, customer service, pricing, access to services—you're not too small. Size doesn't exempt you from explaining what you're doing and showing you've thought about the risks.

The templates SafeAI-Aus provides are designed for small teams. You don't need a governance department. You do need someone who owns the question "How do we know our AI is working safely?"—often your CTO, operations lead, or risk manager.

"Our vendor handles all this"

Your vendor may have excellent AI governance. But you are accountable for how you use their system. If your vendor's chatbot gives harmful advice to a customer, or your vendor's screening tool discriminates, it's your organisation that faces the consequences.

Vendor transparency (section 1.3) helps you ask the right questions. But you still need your own view of what the system does, what could go wrong, and what controls you've put in place.

Getting started this quarter

You don't need a perfect framework to start. In the next 3 months, you could:

  1. Create a basic AI system register and add your top 5–10 known systems.
  2. Draft a one-page AI safety note for one critical system and test it with your executive team.
  3. Add AI questions to project intake and procurement processes.
  4. Run a light assurance review on one high-impact system using a simple checklist.
  5. Add an AI category to your incident reporting system and communicate what to report.

That's enough to move from "we don't know" to "we're starting to show our work". It won't be perfect—and that's fine. The point is demonstrating honest effort to understand and manage your AI risks, not achieving theoretical completeness on day one.

How SafeAI-Aus can help

SafeAI-Aus supports AI assurance by:

  • Publishing open templates for AI registers, safety notes, audits and incident reports
  • Aligning those templates with the Voluntary AI Safety Standard and other key frameworks
  • Providing examples and patterns that small and medium Australian organisations can adopt without a large new governance function

You can adapt these artefacts to your own context, policy stack and risk appetite – and share improvements back with the community.

This page is part of SafeAI-Aus's governance framework. Related resources:

External resources