Successful AI adoption starts with laying the right foundation of data. Without prepared, high-quality and unbiased data, organisations risk a raft of compliance issues, flawed outputs and potentially harmful consequences.
Traditional governance models fall short when it comes to AI, which demands a continuous, lifecycle approach to manage risks like bias, model drift and opacity. As AI use increases, it’s become evident that setting a clear purpose, ongoing evaluation and alignment with standards such as ISO’s AI Management System (AIMS) are essential for creating accountable and risk-based governance for organisations.
In this article, we’ll share a practical checklist you can use to assess your current state and build the data confidence needed to leverage the use of responsible AI.
Our checklist for assessing data readiness
1. Get your governance in order
Governance is the backbone of responsible AI, ensuring accountability, clarity and control. It’s also where data readiness begins.
- Set up a clear AI data governance framework: Assign roles for data owners, privacy leads, and AI system stewards, including all third-party systems.
- Know your data rights: Track intellectual property, privacy, Indigenous Data Sovereignty and contractual terms.
- Follow privacy and security standards: Apply the Australian Privacy Principles and Essential Eight and use ISO 27701 and 27001 for privacy and security.
2. Make sure your data is high quality and traceable
AI systems rely on data, so if the data is flawed, biased or incomplete, the system will be too. Understanding data quality, sources and gaps leads to better outcomes for AI.
- Set data quality standards: Define what ‘good data’ means for your AI system, specifically in terms of accuracy, completeness and consistency.
- Track where your data comes from: Record sources, collection methods, transformations and metadata.
- Check for bias and gaps: Assess who’s represented and who’s missing, and document mitigation strategies.
3. Prepare your data
AI needs clean, structured data to perform – poor preparation leads to poor performance. Getting the right structure and preparing data leads to much better outcomes.
- Document your prep steps: Include cleaning, encoding, sampling, and why you chose each method.
- Check if the data fits the task: Make sure data is relevant to the AI system’s purpose and ensure its reassessed if the system changes.
- Keep good records: Use datasheets and model cards to explain what the data is for, what it can’t do, and any ethical concerns.
4. Protect privacy and stay compliant
Being proactive about privacy and security helps to minimise the risk of AI making inferences or misusing personal data.
- Design for privacy from the start: Use de-identification, aggregation and differential privacy.
- Be ready for breaches: Have a response plan and follow the Notifiable Data Breach scheme.
- Secure your data end-to-end: Use access controls, encryption and audit logs, and be sure to monitor for any internal misuse.
5. Keep AI systems under control
AI systems evolve so you need to monitor them, retrain them, and make sure they stay aligned with your goals over time.
- Log everything: Record system outputs, interactions, and anomalies.
- Watch for drift: Monitor AI performance over time, and retrain your AI when things change.
- Check your suppliers: Make sure third-party AI tools meet your governance and privacy standards.
Adopt AI with confidence
Our AI services are designed to help organisations adopt responsible AI with confidence. We focus on strengthening governance and oversight, conducting privacy risk assessments, implementing robust security controls, ensuring ethical deployment and performing due diligence on third-party AI solutions.
To complement this, we recommend embedding human oversight over automated decisions. It’s important to maintain transparency in public-facing AI (such as clearly labelling chatbots) and avoid the use of personal data in public AI tools to reduce privacy risks. Together, these measures create a framework that balances innovation with trust, safety, and accountability.
AI risk management must be incorporated with organisational frameworks, not siloed as a technical issue. This means updating incident response plans to address AI-specific risks, ensuring strong vendor assurance processes, and placing equal emphasis on people. Building AI comprehension across the organisation, from executives to frontline staff, is essential. Equally, it’s important to integrate ethics into educational training and ability to foster a culture where concerns can be raised safely.
By treating AI governance as an ongoing commitment, organisations can turn compliance into a competitive advantage by ensuring AI is developed and deployed in a way that is ethical, sustainable and trusted. By getting data and governance right from the start, organisations can reduce risk, accelerate adoption and ensure AI delivers lasting value.
Ready to leverage responsible AI?
We can help prepare your data for AI and build the governance foundation your organisation needs to adopt AI with confidence.