§24 · Lane 7 — Australian Regulatory Primary Instruments
OAIC — Privacy and Generative AI Training
OAIC (2024) · OAIC Guidance
Bibliographic data
- Title
- OAIC Guidance (October 2024) — Privacy and developing and training generative AI models
- Authors / Issuing body
- Office of the Australian Information Commissioner (OAIC)
- Venue / Publisher
- Office of the Australian Information Commissioner
- Year
- 2024
- Designation
- Guidance
- Licence
- Stable URL — refer to publisher for full licence terms.
How to cite
OAIC (2024). OAIC Guidance (October 2024) — Privacy and developing and training generative AI models. Office of the Australian Information Commissioner. https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-developing-and-training-generative-ai-models.
The OAIC's October 2024 position on the privacy obligations of entities developing or training generative AI models using personal information. Sets out OAIC expectations on data collection, training-data composition, lawfulness, and the application of APP 3 (collection) and APP 6 (use and disclosure) to model training.
Why it matters for NETEVO
The development-side counterpart to §23 — together the two OAIC guidance documents saturate the privacy surface for AI both as procurement target and as build target.
First, this guidance binds NETEVO clients who train their own models on data containing personal information — typically large AU enterprises with proprietary training datasets. The guidance is the AU regulatory anchor for any in-house model-development governance control.
Second, the guidance is the AU privacy-law counterweight to global model-training-data critiques. Where the EU AI Act (§33) and US scholarship address training-data lineage in part as a copyright matter and in part as a privacy matter, the OAIC guidance specifies the AU privacy reading directly.
Third, the guidance pairs with §11 (42005 impact assessment), §12 (23894 risk management), and §29 (DISR Voluntary AI Safety Standard) for the development-lifecycle governance stack — design-stage controls anchored on AU regulator expectations rather than on importing US/EU norms.
Where NETEVO applies this
- AI Governance in ANZ Whitepaper — AU answer to training-data lineage question
- Agent Infrastructure Whitepaper — agentic-AI training-data controls