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Statement Number 1. Define an operational model

Recommended

• Criterion 1: Identify a suitable operational model to design, develop, and deliver the system securely and efficiently.
• Criterion 2: Consider the technology impacts of the operating model.  
• Criterion 3: Consider technology hosting strategies.

Statement Number 2. Define the reference architecture

Required

• Criterion 4: Evaluate existing reference architectures.

Recommended

• Criterion 5: Monitor emerging reference architectures to evaluate and update the AI system.

Statement Number 3. Identify and build people capabilities

Required

• Criterion 6: Identify and assign AI roles to ensure a diverse team of business and technology professionals with specialised skills.
• Criterion 7: Build and maintain AI capabilities by undertaking regular training and education of end users, staff, and stakeholders.

Recommended

• Criterion 8: Mitigate staff over reliance, under reliance, and aversion of AI.

Statement Number 4. Enable AI auditing

Required

• Criterion 9: Provide end-to-end auditability.
• Criterion 10: Perform ongoing data-specific checks across the AI lifecycle.
• Criterion 11: Perform ongoing model-specific checks across the AI lifecycle.

Statement Number 5. Provide explainability based on the use case

Required

• Criterion 12: Explain the AI system and technology used, including the limitations and capabilities of the system.

Recommended

• Criterion 13: Explain outputs made by the AI system to end users.
• Criterion 14: Explain how data is used and shared by the AI system.

Statement Number 6. Manage system bias 

Required

• Criterion 15: Identify how bias could affect people, processes, data, and technologies involved in the AI system lifecycle.
• Criterion 16: Assess the impact of bias on your use case.
• Criterion 17: Manage identified bias across the AI system lifecycle.

Statement Number 7. Apply version control practices

Required

• Criterion 18: Apply version management practices to the end-to-end development lifecycle.

Recommended

• Criterion 19: Use metadata in version control to distinguish between production and non-production data, models, and code.
• Criterion 20: Use a version control toolset to improve useability for users.
• Criterion 21: Record version control information in audit logs.

Statement Number 8. Apply watermarking techniques

Required

• Criterion 22: Apply visual watermarks and metadata to generated media content to provide transparency and provenance, including authorship.
• Criterion 23: Apply watermarks that are WCAG compatible where relevant.
• Criterion 24: Apply visual and accessible content to indicate when a user is interacting with an AI system.

Recommended

• Criterion 25: For hidden watermarks, use watermarking tools based on the use case and content risk.
• Criterion 26: Assess watermarking risks and limitations.

Statement Number 9. Conduct pre-work

Required

• Criterion 27: Define the problem to be solved, its context, intended use, and impacted stakeholders.
• Criterion 28: Assess AI and non-AI alternatives.
• Criterion 29: Assess environmental impact and sustainability.
• Criterion 30: Perform cost analysis across all aspects of the AI system.
• Criterion 31: Analyse how the use of AI will impact the solution and its delivery.

Statement Number 10. Adopt a human-centred approach

Required

• Criterion 32: Identify human values requirements.
• Criterion 33: Establish a mechanism to inform users of AI interactions and output, as part of transparency.
• Criterion 34: Design AI systems to be inclusive, ethical, and meets accessibility standards using appropriate mechanisms.
• Criterion 35: Design feedback mechanisms.
• Criterion 36: Define human oversight and control mechanisms.

Recommended

• Criterion 37: Involve users in the design process.

Statement Number 11. Design safety systemically

Required

• Criterion 38: Analyse and assess harms.
• Criterion 39: Mitigate harms by embedding mechanisms for prevention, detection, and intervention.

Recommended

• Criterion 40: Design the system to allow calibration at deployment.

Statement Number 12. Define success criteria

Required

• Criterion 41: Identify, assess, and select metrics appropriate to the AI system.

Recommended

• Criterion 42: Reevaluate the selection of appropriate success metrics as the AI system moves through the AI lifecycle.
• Criterion 43: Continuously verify correctness of the metrics.

Statement Number 13. Establish data supply chain management processes

Required

• Criterion 44: Create and collect data for the AI system and identify the purpose for its use.
• Criterion 45: Plan for data archival and destruction.

Recommended

• Criterion 46: Analyse data for use by mapping the data supply chain and ensuring traceability.
• Criterion 47: Implement practices to maintain and reuse data.

Statement Number 14. Implement data orchestration processes

Required

• Criterion 48: Implement processes to enable data access and retrieval, encompassing the sharing, archiving, and deletion of data.

Recommended

• Criterion 49: Establish standard operating procedures for data orchestration.
• Criterion 50: Configure integration processes to integrate data in increments.
• Criterion 51: Implement automation processes to orchestrate the reliable flow of data between systems and platforms.
• Criterion 52: Perform oversight and regular testing of task dependencies.
• Criterion 53: Establish and maintain data exchange processes.

Statement Number 15. Implement data transformation and feature engineering practices

Recommended

• Criterion 54: Establish data cleaning procedures to manage any data issues.
• Criterion 55: Define data transformation processes to convert and optimise data for the AI system.
• Criterion 56: Map the points where transformation occurs between datasets and across the AI system.
• Criterion 57: Identify fit-for-purpose feature engineering techniques.
• Criterion 58: Apply consistent data transformation and feature engineering methods to support data reuse and extensibility.

Statement Number 16. Ensure data quality is acceptable

Required

• Criterion 59: Define quality assessment criteria for the data used in the AI system.

Recommended

• Criterion 60: Implement data profiling activities and remediate any data quality issues.
• Criterion 61: Define processes for labelling data and managing the quality of data labels.

Statement Number 17. Validate and select data

Required

• Criterion 62: Perform data validation activities to ensure data meets the requirements for the system’s purpose.
• Criterion 63: Select data for use that is aligned with the purpose of the AI system.

Statement Number 18. Enable data fusion, integration and sharing

Recommended

• Criterion 64: Analyse data fusion and integration requirements.
• Criterion 65: Establish an approach to data fusion and integration.
• Criterion 66: Identify data sharing arrangements and processes to maintain consistency.

Statement Number 19. Establish the model and context dataset

Required

• Criterion 67: Measure how representative the model dataset is.
• Criterion 68: Separate the model training dataset from the validation and testing datasets.
• Criterion 69: Manage bias in the data.

Recommended

• Criterion 70: For generative AI, build reference or contextual datasets to improve the quality of AI outputs.

Statement Number 20. Plan the model architecture

Required

• Criterion 71: Establish success criteria that cover any AI training and operational limitations for infrastructure and costs.
• Criterion 72: Define a model architecture for the use case suitable to the data and AI system operation.
• Criterion 73: Select algorithms aligned with the purpose of the AI system and the available data.
• Criterion 74: Set training boundaries in relation to any infrastructure, performance, and cost limitations.

Recommended

• Criterion 75: Start small, scale gradually.

Statement Number 21. Establish the training environment

Required

• Criterion 76: Establish compute resources and infrastructure for the training environment.
• Criterion 77: Secure the infrastructure.

Recommended

• Criterion 78: Reuse available approved AI modelling frameworks, libraries, and tools.

Statement Number 22. Implement model creation, tuning and grounding

Required

• Criterion 79: Set assessment criteria for the AI models, with respect to pre-defined metrics for the AI system.
• Criterion 80: Identify and address situations when AI outputs should not be provided.
• Criterion 81: Apply considerations for reusing existing agency models, off-the-shelf, and pre-trained models.
• Criterion 82: Create or fine-tune models optimised for target domain environment.

Recommended

• Criterion 83: Create and train using multiple model architectures and learning strategies.

Statement Number 23. Validate, assess and update model

Required

• Criterion 84: Set techniques to validate AI trained models.
• Criterion 85: Evaluate the model against training boundaries.
• Criterion 86: Evaluate the model for bias, implement and test bias mitigations.

Recommended

• Criterion 87: Identify relevant model refinement methods.

Statement Number 24. Select trained models

Recommended

• Criterion 88: Assess a pool of trained models against acceptance metrics to select a model for the AI system.

Statement Number 25. Implement continuous improvement frameworks

Required

• Criterion 89: Establish interface tools and feedback channels for machines and humans.
• Criterion 90: Perform model version control.

Statement Number 26. Adapt strategies and practices for AI systems

Required

• Criterion 91: Mitigate bias in the testing process.
• Criterion 92: Define test criteria approaches.

Recommended

• Criterion 93: Define how test coverage will be measured.
• Criterion 94: Define a strategy to ensure test adequacy.

Statement Number 27. Test for specified behaviour

Required

• Criterion 95: Undertake human verification of test design and implementation for correctness, consistency, and completeness.
• Criterion 96: Conduct functional performance testing to verify the correctness of the AI System Under Test (SUT) as per the pre-defined metrics.
• Criterion 97: Perform controllability testing to verify human oversight and control, and system control requirements.
• Criterion 98: Perform explainability and transparency testing as per the requirements.
• Criterion 99: Perform calibration testing as per the requirements.
• Criterion 100: Perform logging tests as per the requirements.

Statement Number 28. Test for safety, robustness, and reliability

Required

• Criterion 101: Test the computational performance of the system.
• Criterion 102: Test safety measures through negative testing methods, failure testing, and fault injection.
• Criterion 103: Test reliability of the AI output, through stress testing over an extended period, simulating edge cases, and operating under extreme conditions.

Recommended

• Criterion 104: Undertake adversarial testing (red team testing), attempting to break security and privacy measures to identify weaknesses.

Statement Number 29. Test for conformance and compliance

Required

• Criterion 105: Verify compliance with relevant policies, frameworks, and legislation.
• Criterion 106: Verify conformance against organisation and industry-specific coding standards.
• Criterion 107: Perform vulnerability testing to identify any well-known vulnerabilities.

Statement Number 30. Test for intended and unintended consequences

Required

• Criterion 108: Perform user acceptance testing (UAT) and scenario testing, validating the system with a diversity of end-users in their operating contexts and real-world scenarios.

Recommended

• Criterion 109: Perform robust regression testing to mitigate the heightened risk of escaped defects resulting from changes, such as a step change in parameters.

Statement Number 31. Undertake integration planning

Recommended

• Criterion 110: Ensure the AI system meets architecture and operational requirements with the Australian Government Security Authority to Operate (SATO).
• Criterion 111: Identify suitable tests for integration with the operational environment, systems, and data.

Statement Number 32. Manage integration as a continuous practice

Recommended

• Criterion 112: Apply secure and auditable continuous integration practices for AI systems.

Statement Number 33. Create business continuity plans

Required

• Criterion 113: Develop plans to ensure critical systems remain operational during disruptions.

Statement Number 34. Configure a staging environment

Recommended

• Criterion 114: Ensure the staging environment mirrors the production environment in configurations, libraries, and dependencies for consistency and predictability.
• Criterion 115: Measure the performance of the AI system in the staging environment against predefined metrics.
• Criterion 116: Ensure deployment strategies include monitoring for AI specific metrics, such as inference latency and AI output accuracy.

Statement Number 35. Deploy to a production environment

Required

• Criterion 117: Apply strategies for phased roll-out.
• Criterion 118: Apply readiness verification, assurance checks and change management practices for the AI system.

Recommended

• Criterion 119: Apply strategies for limiting service interruptions.

Statement Number 36. Implement rollout and safe rollback mechanisms

Recommended

• Criterion 120: Define a comprehensive rollout and rollback strategy.
• Criterion 121: Implement load balancing and traffic shifting methods for system rollout.
• Criterion 122: Conduct regular health checks, readiness, and startup probes to verify stability and performance on the deployment environment.
• Criterion 123: Implement rollback mechanisms to revert to the last stable version in case of failure.

Statement Number 37. Establish monitoring framework

Recommended

• Criterion 124: Define reporting requirements.
• Criterion 125: Define alerting requirements.
• Criterion 126: Implement monitoring tools.
• Criterion 127: Implement feedback loop to ensure that insights from monitoring are fed back into the development and improvement of the AI system.

Statement Number 38. Undertake ongoing testing and monitoring

Required

• Criterion 128: Test periodically after deployment and have a clear framework to manage any issues.
• Criterion 129: Monitor the system as agreed and specified in its operating procedures.
• Criterion 130: Monitor performance and AI drift as per pre-defined metrics.
• Criterion 131: Monitor health of the system and infrastructure.
• Criterion 132: Monitor safety.
• Criterion 133: Monitor reliability metrics and mechanisms.
• Criterion 134: Monitor human-machine collaboration.
• Criterion 135: Monitor for unintended consequences.
• Criterion 136: Monitor transparency and explainability.
• Criterion 137: Monitor costs.
• Criterion 138: Monitor security.
• Criterion 139: Monitor compliance of the AI system.

Statement Number 39. Establish incident resolution processes

Required

• Criterion 140: Define incident handling processes.
• Criterion 141: Implement corrective and preventive actions for incidents.

Statement Number 40. Create a decommissioning plan

Required

• Criterion 142: Define the scope of decommissioning activities.
• Criterion 143: Conduct an impact analysis of decommissioning the target AI system.
• Criterion 144: Proactively communicate system retirement.

Statement Number 41. Shut down the AI system

Required

• Criterion 144: Proactively communicate system retirement.
• Criterion 146: Disable computing resources or components specifically dedicated to the AI system.
• Criterion 147: Securely decommission or repurpose all computing resources specifically dedicated to the AI system, including individual and shared components.

Statement Number 42. Finalise documentation and reporting

Required

• Criterion 148: Securely decommission or repurpose all computing resources specifically dedicated to the AI system, including individual and shared components.