You’re probably focused on shipping features and getting customers. But there’s a governance framework that Australia released in September 2024 that you need to know about. Ignoring it might create compliance challenges later.
89% of Australian founders lack awareness of AI governance standards according to the startup AI ecosystem research. That’s a problem, because regulatory momentum is building.
The good news? Australia’s Voluntary AI Safety Standard is designed for resource-constrained teams—not enterprise governance departments.
There’s a distinction worth understanding: governance establishes the oversight policies; compliance operationalises regulatory adherence. You need both.
This guide covers the 6 Key Practices, how to classify your AI system’s risk level, what documentation you need, and where to find official resources.
What is the Voluntary AI Safety Standard in Australia?
The Voluntary AI Safety Standard was released in September 2024 by the Australian AI Safety Institute. It provides guidelines for responsible AI development and deployment applicable to AI systems of any risk level—high-risk, general-purpose AI, or low-risk.
The framework was streamlined in October 2025 when the Guidance for AI Adoption condensed the original 10 guardrails down to 6 Key Practices.
It’s currently voluntary, but establishes the foundation for proposed mandatory guardrails targeting high-risk AI systems.
The timeline: 2019 ethics principles → 2024 voluntary standard → proposed mandatory guardrails. It’s a regulatory philosophy that scales obligations proportionally to assessed risk levels.
Compare this to the EU’s approach. The EU AI Act is comprehensive and mandatory with binding requirements and penalties already in force. Australia is taking a more deliberate approach: voluntary standards first, mandatory guardrails later.
How does AI governance differ from AI compliance?
AI governance is a structured framework establishing policies, processes, and oversight mechanisms for responsible AI development throughout the lifecycle.
AI compliance is adherence to legal and regulatory standards governing AI technologies.
Governance is strategic and proactive—what you should do. Compliance is operational and reactive—what you must do.
Here’s a practical example. Your governance policy might establish that all AI systems need human oversight for decisions affecting individuals. Your compliance checklist then implements that policy by ensuring your automated decision-making system has a review queue and documented approval process.
Governance prevents issues. Compliance proves adherence when regulators like OAIC, the eSafety Commissioner, ASIC, or ACCC come asking.
You need both.
What are Australia’s 6 Key AI Practices for startups?
The 6 Key Practices streamline the original 10 guardrails into actionable steps applicable to all AI systems.
Practice 1 – Decide who is accountable: Establish end-to-end accountability with clear ownership.
Practice 2 – Understand impacts and plan accordingly: Conduct stakeholder impact assessment ensuring fair treatment.
Practice 3 – Measure and manage risks: Implement AI-specific risk management through systematic assessment.
Practice 4 – Share essential information: Ensure transparency so users understand AI use and impacts.
Practice 5 – Test and monitor: Maintain quality through continuous evaluation addressing model drift and algorithmic bias.
Practice 6 – Maintain human control: Ensure meaningful human oversight and prevent purely automated decision-making.
These are lightweight enough for 10-person teams while comprehensive enough for audit-ready compliance.
For resource-constrained implementation, focus on documentation outputs. For Practice 5, this means testing and monitoring protocols that detect model drift and bias before they become problems.
How do you classify if your AI system is high-risk?
Your risk assessment framework evaluates potential impacts on health, safety, and fundamental rights.
High-risk AI systems have significant potential to impact human rights, cause physical or psychological harm, or create substantial legal impacts. Examples include healthcare diagnostics, hiring systems, credit scoring, and law enforcement applications.
General-purpose AI systems are large language models and flexible AI handling a range of tasks with unpredictable capabilities. If you’re building or deploying LLMs, multimodal models, or flexible AI agents, you’re working with GPAIs requiring heightened governance scrutiny.
Low or minimal-risk systems face virtually no obligations beyond basic transparency.
Your risk classification determines your compliance obligations. High-risk systems trigger the 10 mandatory guardrails (proposed). This means you conduct a pre-development risk assessment before commencing development.
When classifying risk level, if your AI system could fall under more than one category, treat it as high-risk to stay safe. Better to implement stronger governance from the start than retrofit it later.
What documentation is required for AI accountability?
Record-keeping obligations cover AI system development, deployment, decision-making processes, and risk mitigation measures.
This is required under the Accountability principle—one of the 8 AI Ethics Principles and Practice 1 of the 6 Key Practices.
Development documentation includes data governance records tracking which datasets were used for training, including source, date acquired, licensing terms, model architecture decisions, and bias mitigation approaches.
Deployment documentation covers responsible disclosure to users, stakeholder impact assessments, and risk classification rationale.
Operational documentation includes testing and monitoring logs, human oversight records, and incident response actions.
The documentation enables contestability—individuals can challenge AI system use when significantly impacted. It creates audit-ready compliance when regulators inquire.
For resource-constrained startups, focus on a minimum documentation set. Document at each AI lifecycle phase: pre-development, development, and post-deployment.
Common challenges include unexplainability—AI algorithms making decision-making processes opaque—which you address through model documentation and output logging.
Where can Australian startups find official AI governance resources?
The Australian AI Safety Institute is the primary government authority providing resources, guidance, and oversight for AI safety. It’s housed within the National AI Centre.
The National AI Centre is your resource hub offering Guidance for AI Adoption, support programs, and AI literacy initiatives. Contact them at [email protected].
The Guidance for AI Adoption comes in two implementation levels: Foundations for organisations getting started in adopting AI, and Implementation Practices for governance professionals and technical experts.
There’s no AI-specific regulator in Australia yet, but existing federal regulators are active. The OAIC enforces Privacy Act AI provisions. The eSafety Commissioner handles Online Safety Act obligations. ASIC covers financial services. ACCC handles consumer protection.
For international certification, ISO/IEC 42001:2023 is the international standard for AI management systems offering an audit-ready certification path.
Use the Foundations guidance if you’re getting started. Use Implementation Practices if you need detailed technical guidance.
How does Australia’s approach compare to international AI frameworks?
If you’re serving global markets, understanding how Australia’s framework aligns with international standards helps you avoid duplicate compliance work.
Australia is taking a deliberate, phased approach: voluntary standards first, mandatory guardrails for high-risk systems later.
The EU AI Act is comprehensive and mandatory. It became legally binding on August 1, 2024, with requirements taking effect gradually through a phased rollout.
Both use risk-based regulatory approaches scaling obligations proportionally to assessed risk levels. The Australian framework is increasingly aligning with EU principles around risk classification, conformity assessment, and transparency requirements.
Implementing the Australian voluntary standard prepares you for future mandatory requirements and provides a head start on international compliance.
ISO 42001 certification provides additional competitive advantage. While EU AI Act compliance is mandatory, earning ISO 42001 certification shows customers you’re taking responsible AI seriously.
FAQ Section
Is AI governance mandatory for Australian startups in 2025?
No. The AI Safety Standard remains voluntary in 2025 for all AI systems. However, the Australian government released proposals in September 2024 for 10 Mandatory Guardrails targeting high-risk AI applications. Proactive implementation of the voluntary 6 Key Practices reduces future compliance friction when mandatory requirements commence.
What penalties exist for AI compliance failures in Australia?
There are no AI-specific penalties yet. Related laws carry penalties though. Privacy Act violations can result in penalties up to $50 million, or three times the benefit of a contravention, or 30% of domestic turnover for serious privacy interferences. Lower civil penalties of up to $3.3 million apply for non-serious interferences. Proposed mandatory guardrails will introduce AI-specific penalties for high-risk system non-compliance.
Do small startups need the same governance as large enterprises?
No. The 6 Key Practices are designed to be implementable by resource-constrained teams through lightweight frameworks. Focus on Practice 3 (risk management) to determine your AI system’s risk level, then implement proportional governance measures.
What’s the difference between AI ethics principles and AI safety standards?
The 8 AI Ethics Principles (established 2019) provide foundational values. The Voluntary AI Safety Standard (released 2024) converts these values into 6 actionable practices. Ethics define principles; standards define implementation.
How do I know if my AI product is a General-Purpose AI System?
General-purpose AI systems are developed to handle a range of tasks with flexibility to conduct activities not contemplated by the developer. Examples include large language models, foundation models, and multimodal models. If you’re building or deploying LLMs or flexible AI agents, you’re working with GPAIs requiring heightened governance scrutiny.
Can I use third-party AI services and still meet governance requirements?
Yes, but you remain accountable. Practice 1 (accountability) establishes end-to-end ownership regardless of vendor services. When evaluating AI service providers, assess their governance practices, documentation capabilities, and alignment with Australian standards. Our guide on vendor compliance requirements covers how different providers handle governance and compliance.
What happens if my AI system makes a discriminatory decision?
Practice 6 (human oversight) requires meaningful human control and review of AI decisions. If discriminatory outcomes occur, contestability obligations enable affected individuals to challenge decisions. Documentation requirements (Practice 4) must capture incident response actions. Practice 5 (testing and monitoring) should detect algorithmic bias before deployment.
How long does it take to implement basic AI governance for a 10-person startup?
Initial implementation of lightweight 6 Key Practices typically requires 2-4 weeks. This covers documentation framework establishment, risk assessment completion, and basic monitoring setup. Ongoing compliance involves continuous testing, monitoring, and documentation updates integrated into development workflows. Governance training for teams adds 1-2 days for foundational AI literacy and governance awareness.
Do I need external consultants or can we implement governance in-house?
Resource-constrained startups can implement basic governance in-house using official resources from the National AI Centre. The Guidance for AI Adoption provides two implementation levels specifically for this purpose. External consultants add value for high-risk AI systems requiring conformity assessment, ISO 42001 certification pursuit, or complex risk scenarios.
What’s the relationship between AI governance and my startup’s privacy obligations?
The Privacy Act contains automated decision-making disclosure obligations applicable to AI systems handling personal information. Practice 4 (transparency and explainability) operationalises Privacy Act requirements through responsible disclosure processes. The OAIC enforces Privacy Act provisions and provides AI-specific compliance guidance.
Will implementing voluntary standards protect us when mandatory requirements arrive?
Yes. The Voluntary AI Safety Standard and 6 Key Practices establish the foundation for proposed Mandatory Guardrails targeting high-risk systems. Startups implementing voluntary practices now will face minimal additional burden when mandatory requirements commence. Documentation created now proves historical compliance effort.
Where do I start if I’ve never considered AI governance before?
Start with Practice 3 (risk assessment): classify your AI system as high-risk, GPAI, or minimal-risk. Then implement Practice 1 (accountability) by designating clear ownership and creating basic governance documentation. Access free resources from the National AI Centre’s Guidance for AI Adoption. For broader context on how AI is transforming Australian startups, see our comprehensive ecosystem overview. Address the foundational awareness gap through team AI literacy training before attempting comprehensive implementation.
