The 15 AI Risks Small Businesses Can’t Ignore in 2025
Artificial intelligence is no longer an edge. It is a baseline capability. The payoff is real for Australian SMEs: faster service, leaner operations, better insights. Yet adoption without guardrails creates exposure that can undo gains overnight. This article outlines the top 15 AI risks for small businesses, explains why they matter in the Australian economy, and gives you a practical playbook to reduce harm without losing momentum.
You will notice that we reference widely accepted frameworks and current policy signals. The National Institute of Standards and Technology’s AI Risk Management Framework is becoming the global lingua franca for structured risk management. ISO or IEC guidance increasingly aligns controls with broader enterprise risk. Australian regulators, including the OAIC and ASIC, are setting clear expectations about privacy and governance. And the EU AI Act is shaping a de facto international standard. This matters to Australian SMEs because clients, partners, and supply chains require proof of responsible AI. Read on to see how to prepare.
- Data leakage and accidental disclosure
The most immediate AI risks for small businesses involve data leakage. Staff paste customer details or confidential contracts into a chatbot. If the tool is misconfigured, that data may be stored, used for model improvement, or accessed by others. In Australia, an accidental disclosure of personal information can become a notifiable data breach under the Privacy Act. The impact is reputational damage, regulatory notifications, and potential class actions.
How to reduce it
- Use enterprise versions of AI tools with strict data controls.
- Configure settings to disable training on your data.
- Create a red list: never paste personally identifiable information, financial records, or secrets into public models.
- Train staff. Short, scenario-based microlearning works.
- Weak cybersecurity in AI workflows
AI brings new attack surfaces. Model prompt injection, malicious outputs, and compromised plugins can lead to credential theft and malware. The Australian Cyber Security Centre has issued guidance on engaging with AI securely. For SMEs with limited resources, ignoring this is one of the most underestimated AI risks for small businesses.
How to reduce it
- Treat AI tools like any other SaaS: patch, control access, log events.
- Use least-privilege access to connected data sources.
- Monitor for unusual activity and keep admin access separate from daily users.
- Add AI-specific security checks to procurement.
- Privacy breaches and unlawful processing
AI thrives on data. However, Australian privacy law requires consent, purpose limitation, and secure handling. New guidance for commercially available AI products clarifies that using personal information with AI can be a high-risk activity. Poor practices invite investigations, fines, and loss of customer trust. That is why privacy is central when discussing AI risks for small businesses.
How to reduce it
- Complete a privacy impact assessment for AI use cases.
- Prefer de-identified data and data minimisation.
- Maintain a register of AI systems and their personal information flows.
- Update privacy notices to explain AI use in plain English.
- Intellectual property and copyright exposure
Training data and generated content can create IP conflicts. If your team uses AI to draft content or design assets, you must understand where the material comes from and who owns the outputs. The global debate about training on copyrighted content has real commercial consequences. This is one of the fastest-moving AI risks for small businesses, particularly in creative industries.
How to reduce it
- Use providers that document lawful data sourcing and licensing.
- Keep a log of prompts and outputs for essential assets.
- Run legal reviews for public-facing campaigns and product collateral that rely on AI-generated content.
- When in doubt, commission original work or purchase licences.
- Biased outputs and unfair decisions
AI can replicate or amplify bias in data. That can show up in hiring screens, marketing segmentation, credit assessments, and customer support prioritisation. In Australia, biased outcomes may breach anti-discrimination laws and consumer law. Addressing bias is central to managing AI risks for small businesses because even small teams now make scaled decisions through automation.
How to reduce it
- Avoid using AI for automated decisions affecting rights or access to services.
- Build human-in-the-loop checks for consequential outcomes.
- Test outputs for demographic skew.
- Keep documentation of data sources and evaluation results.
- Hallucinations and factual errors
Generative systems can produce confident, wrong answers. Relying on them for legal, financial, or medical advice can backfire. This risk appears in customer emails, policy drafts, and sales proposals in day-to-day operations, including hidden errors. The result is lost deals and increased liability. This is one of the most visible AI risks for small businesses.
How to reduce it
- Use retrieval augmented generation with a vetted knowledge base.
- Require citations to internal sources for anything material.
- Set up a mandatory review for external communications drafted by AI.
- Track error rates and tune prompts or switch providers if needed.
- Over-reliance and skill atrophy
When teams lean too heavily on AI, core skills fade. Writing, data analysis, and client reasoning suffer. That creates brittle operations. If an AI service fails or becomes too costly, productivity drops. As adoption spreads, over-reliance becomes one of the quieter AI risks for small businesses, with cultural and capability impacts.
How to reduce it
- Set a policy: AI assists, humans own outcomes.
- Keep regular practice of fundamental skills.
- Pair junior staff with senior reviewers to maintain standards.
- Rotate tools to avoid lock-in to a single provider.
- Compliance drift and governance gaps
Regulation is evolving quickly. ASIC has warned about financial service governance gaps as AI adoption outpaces risk frameworks. The EU AI Act has begun its phased effect, and it will shape international expectations for risk classification, transparency, and documentation. Falling behind is one of the most serious AI risks for small businesses that work with regulated clients or export to the EU.
How to reduce it
- Map your AI systems against a common framework such as the NIST AI RMF.
- Create a single AI policy. Keep it short and practical.
- Assign an AI owner who reports to leadership each quarter.
- Track horizon changes like the EU AI Act’s milestones and local guidance.
- Supplier and model dependency
Many AI capabilities rely on third-party models, plugins, or datasets. Your business can be stuck if a supplier changes pricing, limits features, or ceases service. Concentration risk is often overlooked when cataloguing AI risks for small businesses.
How to reduce it
- Prefer providers that offer clear data portability and export options.
- Keep a basic plan B: an alternative tool and migration checklist.
- Avoid building core processes on a single proprietary feature.
- Hidden costs and unstable unit economics
Early use feels cheap. Context windows, image processing, and API calls increase costs at scale. Shadow AI adds to the bill. Without measurement, you cannot see the true cost-to-serve. Cost blowouts are practical AI risks for small businesses with thin margins.
How to reduce it
- Instrument usage. Track tokens, images, and minutes by team and project.
- Put quotas on non-essential use.
- Compare open-weight and hosted options where appropriate.
- Regularly benchmark providers for price and performance.
- Safety and misuse in content generation
Image and text tools can create deepfakes, phishing content, or unsafe advice. Even if your team operates responsibly, a compromised account can be used to generate harmful material under your brand. This reputational damage ranks high among AI risks for small businesses.
How to reduce it
- Enforce multi-factor authentication for all AI tools.
- Set content policies and block unsafe prompts where the tool allows it.
- Monitor brand mentions and take down fakes promptly.
- Add human sign-off for public posts generated with AI.
- Poor change management and staff anxiety
Rapid AI rollouts can cause fear and resistance. When people think AI will replace them, they disengage. Productivity falls and errors rise. Culture and adoption are core AI risks for small businesses because small teams feel changes immediately.
How to reduce it
- Communicate the why, where, and how of AI adoption.
- Involve staff in testing and selecting tools.
- Focus on augmentation, not replacement.
- Recognise wins that come from human judgement plus AI assistance.
- Quality, safety, and reliability shortcomings
For some use cases, AI becomes part of a product or service. If the output fails, safety and reliability are at stake. International frameworks emphasise testing, evaluation, verification, and validation. Skipping this work is one of the structural AI risks for small businesses that provide digital products or work in health, transport, or built environment sectors.
How to reduce it
- Define acceptance criteria for each AI feature.
- Test against edge cases and stress conditions.
- Keep post-deployment monitoring to detect drift.
- Document changes and keep version histories.
- Cross-border regulatory exposure
Even if you are Australia-based, your customers or partners may be in jurisdictions covered by the EU AI Act or US state laws. If your tool touches those markets, you inherit obligations. International exposure is becoming one of the strategic AI risks for small businesses with global ambitions.
How to reduce it
- Classify your AI systems against the EU AI Act’s risk tiers if you operate in or sell to the EU.
- Maintain region-specific settings for data, consent, and disclosures.
- Add export controls if you fine-tune with sensitive data.
- Seek legal advice for high-risk categories.
- Strategic distraction and shiny-object syndrome
The final and often invisible entry in the list of AI risks for small businesses is distraction. Constant tool-chasing can pull focus from fundamentals: customer value, cash flow, and delivery. Experimentation is healthy, but only with guardrails.
How to reduce it
- Tie pilots to a measurable business case with a 90-day review.
- Set a small AI budget and prioritise 2 to 3 use cases.
- Stop, start, and continue decisions each quarter.
- Align AI work to your strategy, not the news cycle.
A practical, 90-day roadmap for SMEs
To turn these AI risks for small businesses into manageable work, follow a compact plan.
Weeks 1 to 4: Visibility and quick wins
- Build a simple AI system register: tool name, owner, purpose, data used, risk level.
- Publish a one-page AI policy covering privacy, security, and approvals.
- Turn off model training on your data where possible.
- Start a short training series focused on data handling and prompt hygiene.
Weeks 5 to 8: Controls and assurance
- Run privacy impact assessments for anything touching personal information.
- Map your controls to NIST AI RMF functions: Govern, Map, Measure, Manage.
- Introduce human review for external content and any consequential decisions.
- Set usage metrics and quotas.
Weeks 9 to 12: Sustain and scale
- Select 2 or 3 high-value use cases and define success metrics.
- Test outputs for bias and accuracy.
- Revisit supplier contracts for data use, uptime, and exit terms.
- Prepare a short board or owner update on risk posture and return.
This approach keeps you moving while reducing the most material AI risks for small businesses.
The Australian context: why it matters right now
Australia’s regulatory posture is tightening. Privacy guidance emphasises caution when using commercially available AI products. Cyber authorities have published advice for safe engagement with AI systems, focusing on sound configuration and secure operations. Financial regulators have flagged governance gaps as adoption races ahead of risk frameworks. Meanwhile, international laws such as the EU AI Act are entering force and setting expectations that influence supply chains and procurement.
For local SMEs, the message is simple. Treat AI like any other critical system. Document how you use it. Protect data. Make a responsible person accountable. These steps will satisfy clients, reduce incidents, and keep you ahead of regulatory shifts. Most importantly, they will help you capture the upside while managing the AI risks for small businesses that could slow your growth.
What a right-sized control set looks like
Many small businesses think AI governance means heavy paperwork. It does not. You can capture the essentials without bureaucracy and still address the key AI risks for small businesses.
- Policy: a one-page statement that sets boundaries for data, suppliers, and staff behaviour.
- Register: a short spreadsheet that lists AI tools, owners, data, and risk level.
- Assessments: privacy and risk checklists applied only to higher-risk use cases.
- Assurance: human review for external content and consequential decisions.
- Training: short, scenario-based videos or lunch-and-learn sessions.
- Monitoring: monthly usage and cost reports, quarterly risk review.
- Supplier terms: clauses on data use, IP, uptime, and exit.
This is enough to cover most AI risks for small businesses while keeping the team productive.
Frequently asked questions from Australian SMEs
Is generative AI safe to use with client information?
It depends. Use enterprise tools with strong assurances, disable data training, and keep sensitive information off public models. Conduct a privacy assessment. This reduces the privacy and disclosure AI risks for small businesses.
Do I need a formal risk framework?
You need a lightweight approach that borrows from NIST’s AI RMF or ISO 23894. Even a basic mapping helps show that you address core AI risks for small businesses.
What about the EU AI Act if we do not sell in Europe?
It still matters. Large clients and partners may require compliance statements aligned with the Act, especially for high-risk categories. Plan to reduce future AI risks for small businesses tied to export and procurement.
Will AI replace my staff?
AI replaces tasks, not whole roles. Focus on augmentation. Invest in training. This minimises cultural and operational AI risks for small businesses.
How do I stop costs from blowing out?
Measure usage, set quotas, and benchmark providers. As adoption scales, cost control is one of the most predictable AI risks for small businesses.
Your next steps with SBAAS
SBAAS helps ambitious Australian SMEs adopt AI responsibly. We start with a quick scan of your current tools and workflows. We then design a right-sized control set, aligned with your strategy and risk appetite, and deliver targeted training. The goal is not paperwork. It is faster growth, safer operations, and fewer surprises. If you would like tailored support, book an appointment to discuss your needs, or learn more about us on our About Us page. When you are ready, we can help you turn AI risks for small businesses into a practical advantage.
This guide is designed for decision-makers who want the upside of AI without the headaches.
- Keep it practical.
- Keep it proportionate.
- Keep it aligned to a simple strategy.
If you need a partner who understands the Australian context and global standards, SBAAS is ready to help.
Sources
Note: The following sources informed this article. No in-text citations are used by design.
- Australian Cyber Security Centre. (2024, January 24). Engaging with artificial intelligence. https://www.cyber.gov.au/resources-business-and-government/governance-and-user-education/artificial-intelligence/engaging-with-artificial-intelligence
- Australian Cyber Security Centre. (2024, January 24). New guidance for engaging with artificial intelligence. Business.gov.au. https://business.gov.au/news/new-guidance-for-engaging-with-artificial-intelligence
- Australian Government Digital Transformation Agency. (2024). AI in government policy and Our next steps for safe, responsible AI in government.
- European Commission. (2024, August 1). AI Act enters into force. https://commission.europa.eu/news-and-media/news/ai-act-enters-force-2024-08-01_en
- ISO/IEC. (2023). ISO/IEC 23894:2023 Information technology – Artificial intelligence – Guidance on risk management. https://www.iso.org/standard/77304.html;
- MinterEllison. (2024, October 29). ASIC warns governance gap could emerge in first report on AI adoption by licensees and (2025). ASIC urges stronger AI governance for AFS and credit licensees. https://www.minterellison.com/articles/asic-urges-stronger-ai-governance-for-afs-and-credit-licensees
- NIST. (2024, July 26). AI Risk Management Framework: Generative AI Profile. https://www.nist.gov/itl/ai-risk-management-framework;
- AI RMF 1.0 primary document. https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf;
- AI Resource Center. https://airc.nist.gov/
- OAIC. (2024–2025). Guidance on privacy and the use of commercially available AI products and commentary. https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-the-use-of-commercially-available-ai-products;
- Stanford HAI. (2025). The 2025 AI Index Report. https://hai.stanford.edu/ai-index/2025-ai-index-report;
- Summary explainers. EU AI Act overview. https://artificialintelligenceact.eu/high-level-summary/; Clifford Chance. (2024). The EU AI Act: Overview of key rules and requirements. https://www.cliffordchance.com/content/dam/cliffordchance/PDFDocuments/the-eu-ai-act-overview.pdf;
- ITPro. (2025, August). Second enforcement deadline and Code of Practice. https://www.itpro.com/business/policy-and-legislation/the-second-enforcement-deadline-for-the-eu-ai-act-is-approaching-heres-what-businesses-need-to-know-about-the-general-purpose-ai-code-of-practice;
Reuters. (2024, October 21). Comparing EU and US AI legislation. https://www.reuters.com/legal/legalindustry/comparing-eu-us-ai-legislation-dj-vu-2020-2024-10-21/

Eric Allgood is the Managing Director of SBAAS and brings over two decades of experience in corporate guidance, with a focus on governance and risk, crisis management, industrial relations, and sustainability.
He founded SBAAS in 2019 to extend his corporate strategies to small businesses, quickly becoming a vital support. His background in IR, governance and risk management, combined with his crisis management skills, has enabled businesses to navigate challenges effectively.
Eric’s commitment to sustainability shapes his approach to fostering inclusive and ethical practices within organisations. His strategic acumen and dedication to sustainable growth have positioned SBAAS as a leader in supporting small businesses through integrity and resilience.
Qualifications:
- Master of Business Law
- MBA (USA)
- Graduate Certificate of Business Administration
- Graduate Certificate of Training and Development
- Diploma of Psychology (University of Warwickshire)
- Bachelor of Applied Management
Memberships:
- Small Business Association of Australia –
International Think Tank Member and Sponsor - Australian Institute of Company Directors – MAICD
- Institute of Community Directors Australia – ICDA
- Australian Human Resource Institute – CAHRI
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