Summary of requirements in the standard

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.

Summary of requirements in the standard

Summary of requirements in the standard

Lifecycle stages

Alternative table option providing greater visibility of the individual statements

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