AI Risks by Industry (Australia)¶

This page provides example AI-related risks tailored to fourteen major Australian industries, with particular emphasis on sectors with significant SME representation.

These examples help businesses identify context-specific risks before adapting them into their own AI risk registers. Each table includes example risks, brief descriptions, and potential impacts. Organisations can extend these tables with columns for control measures, likelihood, residual risk, and ownership to match the main AI Risk Register Template.

Industry Selection: The industries featured represent a mix of large enterprise sectors (mining, finance, energy) and SME-dominant sectors (retail, hospitality, professional services, construction) to reflect the diverse Australian business landscape. Priority has been given to sectors with active AI adoption and clear regulatory considerations.

1. Mining¶

Risk ID	Risk Name	Description	Potential Impact
M1	Automated Decision Errors in Safety Systems	AI-driven monitoring or predictive maintenance may fail to detect hazards or equipment faults.	Workplace accidents, injury, production downtime, legal exposure.
M2	Data Integrity in Remote Operations	Inaccurate or tampered IoT/sensor data can lead to poor operational decisions.	Production loss, safety incidents, environmental harm, financial losses.
M3	Environmental Impact Misrepresentation	AI-generated reporting may understate emissions or ecological effects.	State environmental regulator penalties, loss of licence, reputational damage.

2. Finance¶

Risk ID	Risk Name	Description	Potential Impact
F1	Algorithmic Bias in Credit Assessment	Machine learning models produce discriminatory lending or insurance outcomes.	Regulatory breaches (APRA/ASIC), customer complaints, loss of trust, legal action.
F2	Model Explainability & Regulatory Scrutiny	Limited model transparency may breach APRA/ASIC expectations.	Enforcement action, reputational harm, product suspension, increased compliance costs.
F3	Automated Fraud Detection Errors	AI systems over- or under-flag suspicious activity.	Financial loss, customer frustration, compliance failure, regulatory penalties.

3. Retail Trade¶

Risk ID	Risk Name	Description	Potential Impact
R1	Personalisation and Privacy Breaches	Excessive data use in recommendation engines breaches privacy obligations.	Privacy Act violations, customer attrition, OAIC complaints, data misuse claims.
R2	AI-Generated Marketing Misuse	AI creates inaccurate or deceptive content breaching Australian Consumer Law.	ACCC enforcement, fines, brand damage, customer loss.
R3	Inventory Forecasting Model Degradation	Model drift causes gradual decline in stock prediction accuracy over time.	Financial loss, supply chain disruption, excess inventory or stockouts, waste.

4. Professional, Scientific & Technical Services¶

Risk ID	Risk Name	Description	Potential Impact
P1	Confidential Client Data Exposure	AI tools used for analysis leak client or project data through inadequate security.	Contract breach, loss of client trust, reputational damage, legal liability.
P2	Reliance on Unverified AI Outputs	Consultants rely on unvalidated model outputs for client advice.	Misleading clients, professional liability exposure, negligence claims.
P3	Inadequate Human Oversight of AI Outputs	Insufficient review of AI-generated deliverables before client delivery.	Breach of professional codes, ethical violations, quality failures, legal risks.

5. Healthcare¶

Risk ID	Risk Name	Description	Potential Impact
H1	Diagnostic Model Errors	AI systems make incorrect or biased diagnostic suggestions.	Patient harm, litigation, regulatory sanctions, loss of medical registration.
H2	Data Privacy under Health Regulations	Training data or system design breaches My Health Record requirements and Privacy Act.	Legal penalties, loss of accreditation, OAIC enforcement, public backlash.
H3	Bias in Clinical Algorithms	Narrow or unrepresentative datasets produce unequal treatment outcomes across patient groups.	Health inequities, ethical breaches, discrimination claims, loss of patient confidence.

6. Construction¶

Risk ID	Risk Name	Description	Potential Impact
C1	Design or Structural Planning Faults	AI-assisted drafting introduces undetected structural errors or code violations.	Safety incidents, structural failure, project delays, legal liability, insurance claims.
C2	Worker Safety Monitoring Failures	AI vision tools or sensor systems miss unsafe conditions on construction sites.	Worker injury, state workplace safety regulator penalties, stop-work orders, reputational harm.
C3	Procurement or Supply-Chain Manipulation	Generative AI produces false supplier documentation or credentials.	Procurement fraud, compliance breaches, project delays, cost overruns, legal action.

7. Energy (including Renewables)¶

Risk ID	Risk Name	Description	Potential Impact
E1	Predictive Maintenance Model Failures	AI misclassifies equipment anomalies leading to breakdowns, outages or safety incidents.	Safety hazards, environmental harm, service disruption, financial losses.
E2	Cyber-Physical Security Breaches	AI-linked control systems in grid or plant operations expose new attack vectors.	Industrial sabotage, critical infrastructure risk, financial loss, national security concerns.
E3	Environmental Compliance Gaps	Automated emissions or incident reporting misses critical data or misrepresents environmental impact.	State environmental regulator enforcement, loss of licence, fines, reputational damage.

8. Wholesale Trade¶

Risk ID	Risk Name	Description	Potential Impact
W1	Supply Chain Forecasting Errors	Poor prediction accuracy in demand forecasting causes stock imbalances.	Lost revenue, increased holding costs, dissatisfied clients, supply chain disruption.
W2	Automated Product Data Errors	AI systems populate catalogues with inaccurate specifications, pricing or supplier information.	Customer disputes, contract breaches, brand harm, potential ACL violations.
W3	Third-Party AI Dependency	Heavy reliance on vendor optimisation or procurement tools reduces operational control.	Service disruption, data loss, vendor lock-in, compliance gaps.

9. Agriculture¶

Risk ID	Risk Name	Description	Potential Impact
A1	Sensor and Drone Data Reliability	Faulty AI analysis of crop, soil or livestock data drives incorrect farming decisions.	Reduced yields, wasted resources, animal welfare issues, financial loss.
A2	Algorithmic Over-Application of Resources	AI irrigation, fertiliser or chemical application models recommend excessive use.	Environmental breaches, increased costs, state environmental regulator penalties, sustainability failures.
A3	Biosecurity Data Misclassification	Models miss or misreport pest, disease or weed patterns critical to biosecurity.	Crop loss, livestock disease, export restrictions, biosecurity incident response failures.

10. Manufacturing¶

Risk ID	Risk Name	Description	Potential Impact
MN1	Quality Control Model Drift	Changing materials, processes or sensors reduce AI fault detection accuracy over time.	Quality failures, product recalls, safety risks, warranty claims, brand damage.
MN2	Intellectual Property Leakage	Design or process data used in external AI models or cloud services exposes proprietary information.	Competitive disadvantage, IP infringement risk, legal action, loss of trade secrets.
MN3	Human-Robot Interaction Failures	AI-controlled machinery acts unpredictably due to inadequate safeguards or oversight.	Workplace injury, production halt, state workplace safety regulator penalties, liability exposure.

11. Hospitality & Tourism¶

Risk ID	Risk Name	Description	Potential Impact
HT1	Dynamic Pricing Discrimination	AI pricing algorithms produce outcomes that unfairly discriminate based on protected attributes.	Discrimination complaints, reputational damage, regulatory investigation, loss of customers.
HT2	AI Chatbot Misinformation	Customer service chatbots provide incorrect booking details, pricing or policy information.	Customer complaints, booking errors, ACL breaches, refund costs, reputational harm.
HT3	Guest Data Privacy Breaches	AI systems processing customer preferences or behaviour data breach privacy obligations.	Privacy Act violations, OAIC complaints, loss of customer trust, data breach notification costs.

12. Education & Training¶

Risk ID	Risk Name	Description	Potential Impact
ED1	AI Assessment Bias or Errors	Automated marking or plagiarism detection produces unfair or inaccurate student outcomes.	Student complaints, academic appeals, discrimination claims, accreditation risk.
ED2	Academic Integrity Erosion	Inadequate detection or policy around AI-generated student work undermines learning outcomes.	Qualification credibility loss, employer complaints, ASQA/TEQSA concerns, reputational damage.
ED3	Student Data Privacy Failures	AI learning platforms or analytics breach student privacy requirements.	Privacy Act violations, regulatory sanctions, loss of enrolments, parental concerns.

13. Transport & Logistics¶

Risk ID	Risk Name	Description	Potential Impact
TL1	Route Optimization Safety Failures	AI routing algorithms prioritize efficiency over safety considerations or compliance.	Vehicle accidents, driver fatigue, Chain of Responsibility breaches, insurance claims.
TL2	Predictive Maintenance Errors	Incorrect AI predictions about vehicle or equipment maintenance needs.	Vehicle breakdowns, service disruption, safety incidents, increased repair costs.
TL3	Driver Monitoring Privacy Issues	AI systems monitoring driver behaviour breach privacy or workplace surveillance obligations.	Privacy complaints, employee relations issues, Fair Work concerns, trust erosion.

14. Real Estate & Property Services¶

Risk ID	Risk Name	Description	Potential Impact
RE1	Property Valuation Model Bias	AI valuation tools produce systematically inaccurate estimates for certain property types or locations.	Financial losses, lending errors, client disputes, professional liability claims.
RE2	Discriminatory Property Matching	AI recommendation engines produce discriminatory outcomes in tenant or buyer matching.	Discrimination complaints, Fair Trading breaches, reputational damage, legal action.
RE3	AI-Generated Marketing Misrepresentation	Automated property descriptions or virtual staging contain false or misleading information.	ACL violations, consumer complaints, regulatory enforcement, loss of licence risk.

Using These Risk Examples¶

For SMEs: - Start with 3-5 risks most relevant to your current or planned AI use - Adapt the descriptions to your specific systems and context - Add your own control measures and assign ownership - Review quarterly as your AI use evolves

For Larger Organisations: - Use these as a starting point for comprehensive risk registers - Expand with additional columns (likelihood, controls, residual risk, review dates) - Integrate with existing enterprise risk management frameworks - Consider cross-referencing with the Australian Voluntary AI Safety Standard

Important Note: These examples represent common risk patterns but are not exhaustive. Every organisation should conduct its own risk assessment considering its specific AI applications, data, stakeholders and regulatory environment.

This content is for general guidance only and does not constitute legal or professional advice. Organisations should seek appropriate professional advice for their specific circumstances.