In September 2024, former industry minister Ed Husic announced 10 mandatory guardrails for high-risk AI systems. Fast forward to December 2025 and Australia’s National AI Plan has ditched the lot. The replacement? “Technology-neutral” regulation using existing laws.
This is a philosophical backflip. Instead of AI-specific legislation, the government is betting that current frameworks—Privacy Act, Consumer Law, sector-specific rules—can handle whatever AI throws at them.
What drove the shift? The Productivity Commission’s $116 billion economic opportunity argument played a role. So did lobbying from DIGI, the industry group representing Apple, Google, Meta, and Microsoft. They argued for building on existing regulation rather than creating new AI-specific rules, and they won.
Critics are warning of regulatory gaps in deepfakes, algorithmic bias, and autonomous decision-making. The compliance pathways you thought you understood? Not so clear now.
This article examines who influenced the shift, what it means in practice, and how it compares internationally. You’ll understand the policy reversal and what it means for building and deploying AI systems in Australia.
What Were the 10 Mandatory AI Guardrails Proposed in September 2024?
Ed Husic’s September 2024 announcement laid out a framework targeting high-risk AI. All 10 guardrails are now abandoned. Here’s what they would have required.
Guardrail 1: Risk Management Plans. You’d have needed documented strategies identifying and mitigating system risks before deployment. Formal documentation proving you’d thought through what could go wrong.
Guardrail 2: Pre-Deployment Testing. Mandatory testing before public release to verify safety and accuracy. No shortcuts to production.
Guardrail 3: Post-Deployment Testing. Ongoing monitoring and verification after systems go live. Launch isn’t the finish line.
Guardrail 4: Complaints Mechanisms. Formal processes for users to report issues, harms, or incorrect outputs. An actual channel for when things go wrong.
Guardrail 5: Data Sharing After Adverse Incidents. Transparency requirement to share information following harmful outcomes. Not optional when something breaks badly.
Guardrail 6: Third-Party Assessment Rights. Independent auditors could evaluate systems for safety and compliance. External verification, not just trusting your internal testing.
The remaining guardrails covered transparency documentation, human oversight mechanisms, accountability frameworks, and impact assessments. The complete framework mirrored the EU AI Act’s risk-based model.
These requirements would have applied only to “high-risk” AI—systems affecting employment, healthcare, criminal justice, financial services, and education. Low-risk applications would have remained unregulated.
Think of it this way: mandatory guardrails were hardcoded specifications. You either implemented them or you didn’t. The technology-neutral approach that replaced them? That’s more like an abstraction layer—flexible, adaptable, and considerably less clear about what compliance actually requires.
Why Did Australia Abandon the Mandatory Guardrails?
The December 2025 National AI Plan’s regulatory pillar replaced guardrails with a “regulate as necessary but as little as possible” philosophy. Three things explain the reversal: economic arguments, industry pressure, and international positioning.
The economic opportunity argument came from the Productivity Commission. They estimated AI could add $116 billion to Australia’s economy over the next decade—$4,400 per capita. Their message: mandatory rules could stifle innovation and reduce international competitiveness.
Industry pressure came from DIGI—the Digital Industry Group representing Apple, Google, Meta, and Microsoft. Their position: existing laws already cover AI harms. Why add regulatory complexity when current frameworks work fine?
International alignment played a role too. The US under Trump shifted to lighter regulation. The EU started reconsidering its approach. Australia didn’t want regulatory divergence creating competitive disadvantage as it positions itself as an Indo-Pacific AI hub.
The government adopted the Productivity Commission’s approach: complete a regulatory gap analysis first, then regulate only proven deficiencies. Instead of assuming existing laws have gaps, prove the gaps exist before creating new rules.
Political considerations mattered. New minister Tim Ayres aligned more with business-friendly approaches than Ed Husic did. Treasurer Jim Chalmers’ August 2025 Productivity Roundtable became the venue where the “as little as possible” philosophy crystallised.
Critics argue this prioritises economic growth over public safety. Ed Husic himself warned of “whack-a-mole regulation”—a reactive patchwork creating unpredictability and gaps.
What Is Technology-Neutral Regulation and How Does It Work?
Technology-neutral regulation means applying existing laws across technologies rather than creating AI-specific legislation. The philosophy: regulate the outcome or harm, not the technology producing it.
Think of existing laws as an abstraction layer applying to any technology. The Privacy Act regulates data misuse whether committed via spreadsheet, database, or AI model. Consumer Law prohibits misleading conduct whether via human sales pitch or chatbot. The Copyright Act governs unauthorised reproduction regardless of technology.
In practice for AI, this means no special rules for AI systems, but existing legal obligations still apply. Deepfake creation could violate defamation, privacy, or fraud laws. Algorithmic hiring bias could breach anti-discrimination legislation. AI-generated misinformation could trigger consumer protection or online safety enforcement.
Proponents claim flexibility advantages. Technology-neutral approaches adapt as technology evolves without needing legislative amendments. They reduce compliance complexity and encourage innovation.
Critics identify problems. Existing laws were written before AI and don’t address novel harms. Dr Rebecca Johnson, AI ethicist at the University of Sydney, puts it this way: “It’s like trying to regulate drones with road rules: some parts apply, but most of the risks fly straight past.”
Other concerns? Enforcement agencies lack AI expertise. Gaps in accountability exist—who is liable when autonomous AI causes harm? And there are no proactive safety requirements.
The applicable Australian laws include the Privacy Act 1988, Australian Consumer Law, Copyright Act 1968, Online Safety Act 2021, and sector-specific health and finance regulations. Whether they cover AI-specific scenarios adequately is the live debate. For detailed guidance on what existing laws actually regulate AI in Australia, see our comprehensive compliance guide.
What Is the Regulatory Gap Analysis Approach and How Will It Work?
The Productivity Commission recommended a systematic audit methodology to identify true gaps in existing legal coverage. The philosophy: only create new AI-specific rules after proving existing laws insufficient.
Gap analysis methodology works like this: Map AI-specific harms, identify applicable existing laws, assess enforcement adequacy, document genuine gaps.
Here’s a concrete example using deepfakes. The harm: non-consensual intimate images created by AI. Existing laws: defamation, privacy torts, image-based abuse legislation in some states. Gap assessment: patchy state-level coverage, criminal law doesn’t cover synthetic images in all jurisdictions. Conclusion: potential gap requiring targeted legislative fix.
The AI Safety Institute gets the monitoring role. Launched early 2026 with $29.9 million funding, it will test systems, assess risks, and recommend targeted reforms using AISI’s regulatory gap analysis methodology. Australia joins the International Network of AI Safety Institutes, aligning with comparable efforts in the US, UK, Canada, South Korea, and Japan.
Timeline implications matter. Gap analysis and targeted reforms could take years. Contrast this with guardrails: immediate mandatory requirements versus reactive gap-filling after the fact.
For you? Compliance requirements remain uncertain until gap analysis completes. No clear bright-line rules for what’s permitted versus prohibited. You’re relying on general legal principles and need legal expertise to assess risk for your specific use case.
Critics argue gap analysis delays necessary protections while AI capabilities rapidly advance. The burden of proof shifted—regulators must prove harm after deployment rather than developers proving safety before deployment.
Who Influenced the Shift to Technology-Neutral Regulation?
The policy reversal came from three stakeholder groups: the Productivity Commission, the DIGI industry lobby, and international alignment pressures.
The Productivity Commission carries significant weight as an independent government advisory body. They warned mandatory guardrails could “stifle innovation.” Their position: existing laws are sufficient, prove gaps exist before creating new rules.
DIGI—Digital Industry Group Inc—represents the major tech companies. Their advocacy: “build on existing regulation” rather than create AI-specific rules. Critics argue DIGI represents corporate interests avoiding accountability.
Ed Husic’s criticism provides the counterpoint. He argued patchwork approaches create unpredictability and gaps. He advocated for a comprehensive AI Act similar to the EU model. He lost the internal debate.
Tim Ayres as new minister aligned with light-touch approaches. Treasurer Jim Chalmers convened the August 2025 Productivity Roundtable where the philosophy crystallised.
International context mattered. The US was pursuing lighter regulation. The EU started reconsidering its approach. Australia, seeking alignment with major trading partners, didn’t want to diverge.
Economic arguments won over safety concerns. That pattern will likely continue.
What Are the Arguments For and Against Technology-Neutral Regulation?
There’s a deep debate between innovation enablement versus public safety. Regulatory flexibility versus accountability gaps.
Arguments FOR the light-touch approach start with economic opportunity. The potential contribution could fund health, education, infrastructure. Mandatory rules risk stifling startups.
Regulatory flexibility matters. Technology-neutral approaches adapt without legislative amendments. Professor Niloufer Selvadurai from Macquarie Law School welcomes the “nuanced approach, premised on regulatory gap-analysis.”
Existing law sufficiency gets argued. Privacy, consumer protection, and copyright already cover many AI harms. International competitiveness concerns are real—heavy regulation could drive investment elsewhere.
Avoiding premature lock-in makes sense. Hard rules could become obsolete as AI rapidly evolves.
Arguments AGAINST come from documented gaps. Associate Professor Sophia Duan from La Trobe University puts it bluntly: “The absence of new AI-specific legislation means Australia still needs clearer guardrails to manage high-risk AI.”
Safety gaps exist where existing laws don’t address autonomous decision-making failures, deepfakes, or systemic algorithmic bias. No mandatory risk assessments, testing, or third-party audits.
Reactive versus proactive matters. Gap analysis means harms must occur before regulation responds.
Regulators lack AI technical expertise. DIGI lobby influence prioritised business interests over public protection.
Regulatory arbitrage becomes easier. Big tech uses legal uncertainty to delay compliance. Australia just became one of those jurisdictions.
Think compile-time versus runtime checks. Guardrails catch issues before deployment. Technology-neutral addresses issues after harm occurs.
What Does This Mean for Businesses Operating AI Systems in Australia?
Compliance pathways are less clear under a technology-neutral approach. You’ll need legal risk assessment for your specific situation.
Immediate implications: no mandatory guardrails to implement, but existing legal obligations still apply. You must assess which existing laws apply to your AI use cases.
Privacy Act if processing personal information—most AI systems do. Consumer Law if providing products or services. Copyright Act if training models on copyrighted material. Sector-specific regulation in health, finance, or employment if operating in those domains.
Strategic differences by business type matter.
For startups: lower immediate compliance burden, faster deployment possible. But regulatory uncertainty creates risk. Potential enforcement after deployment rather than clear requirements upfront. Monitor AI Safety Institute guidance when it launches. Consider implementing voluntary safety standards as defensive practice.
For established firms: existing compliance frameworks already apply to AI systems. Financial services face APRA prudential standards. Healthcare deals with TGA medical device regulation if AI is used for diagnosis. Treat AI as a technology layer within existing risk management frameworks.
High-risk AI applications need attention even though no formal definition exists. Employment decisions, credit scoring, healthcare diagnosis, criminal justice—areas of heightened legal risk. Consider voluntarily implementing the abandoned guardrails anyway. Risk management plans, testing, and complaints mechanisms demonstrate due diligence if legal issues arise.
Competitive implications exist. Light-touch regulation may attract international AI investment to Australia. But uncertainty about future regulation creates investment risk.
Practical compliance steps:
Map your AI systems to applicable existing laws—privacy, consumer protection, sector-specific. Conduct legal risk assessment with Australian law expertise. Implement data governance practices for Privacy Act compliance. Establish transparency and fairness practices for Consumer Law. Monitor AI Safety Institute guidance when available. Consider voluntary adoption of risk management, testing, and complaints mechanisms. Track international developments if operating multinationally—EU AI Act, US state regulations matter.
The government will likely incrementally amend existing regulation—Privacy Act, Australian Consumer Law, possibly Online Safety Act. Expect more industry guidance on safe and responsible AI development. For detailed implementation guidance on Privacy Act, Consumer Law, and other compliance requirements, including technical controls and risk matrices, see our practical compliance guide.
How Does Australia’s Approach Compare to the EU AI Act and Other International Models?
Australia chose technology-neutral existing laws. The EU implemented comprehensive AI-specific legislation. The approaches differ fundamentally. For a comprehensive analysis of how Australia’s approach compares to the EU AI Act and other international frameworks, see our detailed comparison guide.
The EU AI Act uses a risk-based mandatory framework. High-risk AI in employment, credit scoring, and law enforcement faces mandatory risk assessments, data governance, and human oversight. Prohibited AI includes social scoring and exploitative manipulation. Enforcement includes fines up to 7% of global turnover.
Australia versus EU comparison: The EU requires proactive compliance before deployment. Australia enforces existing laws after deployment. The EU has clear bright-line rules. Australia has general legal principles requiring interpretation.
If you’re operating in multiple jurisdictions, EU compliance may be stricter. Practical strategy: implement EU AI Act requirements globally, and your Australian operations automatically become compliant.
The United States takes a no-federal-legislation approach. No comprehensive federal AI-specific legislation exists. The US and Australia are aligned on a light-touch approach favouring innovation.
Strategic positioning matters. Australia is competing as an Indo-Pacific AI hub against Singapore and Japan. Regulatory environment affects data centre location, AI research facilities, and talent attraction. A light-touch approach may attract investment but creates compliance uncertainty.
Australia joins the International Network of AI Safety Institutes, aligning practice with US, UK, Canada, South Korea, and Japan efforts. This creates some international coordination despite regulatory divergence.
FAQ
What specific harms do critics say existing Australian laws don’t cover?
Critics identify three primary gaps: deepfakes with patchy state-level criminal coverage for synthetic intimate images, algorithmic bias where anti-discrimination laws don’t clearly apply to automated decisions, and autonomous AI failures where liability is unclear when no human is in the decision loop. Existing laws written before AI often require proving intent or human agency—difficult with machine learning systems.
Will the AI Safety Institute have enforcement powers?
No. The AI Safety Institute receives $29.9 million to monitor AI development, test systems, and advise government on regulatory gaps, but has no enforcement authority. Existing regulators—Privacy Commissioner, ACCC, sector-specific bodies—retain enforcement powers. The Institute’s role is advisory.
Can Australian businesses still voluntarily implement the abandoned guardrails?
Yes, and many may choose to for defensive legal practice. Implementing risk management plans, testing, complaints mechanisms, and third-party assessments demonstrates due diligence if legal issues arise. Voluntary adoption also prepares you for potential future regulation.
How long will the regulatory gap analysis take?
The government hasn’t specified a timeline. AI Safety Institute launches early 2026, but gap identification, analysis, consultation, and legislative process could take years. Critics warn AI capabilities evolve faster than regulatory processes.
Does technology-neutral regulation mean no AI regulation at all?
No. It means applying existing laws like Privacy Act, Consumer Law, Copyright Act, and sector-specific rules rather than creating AI-specific legislation. This approach forms the regulatory foundation of the National AI Plan released December 2025. AI developers must still comply with data protection, consumer rights, intellectual property, and industry regulations. The debate is whether existing laws sufficiently address AI-specific harms.
What happens if I deploy AI that later gets identified as a regulatory gap?
Legal risk depends on whether your system violates existing laws. If gap analysis identifies a deficiency and the government creates a new AI-specific rule, there’s typically a transition period for compliance. But if your AI already violates privacy, consumer protection, or other current laws, retrospective enforcement is possible.
How does this affect AI startups differently than established companies?
Startups benefit from lower immediate compliance burden—no mandatory guardrails to implement before launch. But you face uncertainty about future requirements and legal risk if existing laws are violated. Established companies, especially in regulated sectors, already have compliance frameworks that extend to AI. Both should monitor AI Safety Institute guidance.
Will Australia’s approach attract or deter international AI investment?
Mixed signals. Light-touch regulation may attract companies seeking faster deployment and lower compliance costs. But regulatory uncertainty creates investment risk. When you’re operating across borders, you may prefer jurisdictions with clear rules like the EU over flexible but unpredictable environments.
What is the “as necessary but as little as possible” philosophy in practice?
Emerged from August 2025 Productivity Roundtable. It means the government will only regulate where gaps in existing laws are proven, and only to the extent necessary to address a specific identified harm. Contrasts with a precautionary approach of establishing a comprehensive framework upfront. Critics call it reactive rather than proactive.
Are there any AI applications Australia has specifically prohibited?
No. Unlike the EU AI Act which bans social scoring, biometric categorisation, and emotion recognition in sensitive contexts, Australia has not prohibited any AI applications. Existing laws may make certain uses illegal—creating child exploitation material, defamatory deepfakes—but no AI-specific prohibitions exist.
How should you track regulatory developments under this approach?
Monitor three sources: AI Safety Institute guidance and gap analysis recommendations when launched in 2026, existing regulator enforcement actions from the Privacy Commissioner and ACCC applying current laws to AI, and international developments from EU AI Act implementation and US state regulations. Consider subscribing to legal updates from Australian law firms specialising in technology regulation.
What replaced the mandatory complaints mechanisms guardrail?
Existing complaint pathways: Privacy Commissioner for data issues, ACCC for consumer protection, industry ombudsmen for financial services and telecommunications, sector-specific regulators. No AI-specific complaint mechanism was created. Users must navigate existing fragmented complaint systems depending on the type of harm.
