A practical operating model for establishing data governance and using governed, trusted data as the foundation for responsible AI adoption.
Data Governance → Trusted Data → AI Readiness → Responsible AI → Measurement → Scale
Status: Maintained reference implementation
Last reviewed: October 2026
This repository is designed for technology, data, security, risk and business leaders who need to move beyond policy statements and create governance that can actually operate, produce evidence and improve over time.
This repository is the broader implementation library for Data Governance and Responsible AI.
For a focused practical toolkit connecting data classification, lineage and governance decision rights, see the Data Governance Toolkit.
The two repositories are complementary:
- Data Governance Toolkit — focused practical guidance for Classify → Trace → Govern
- Data Governance + Responsible AI Framework — broader operating model, implementation guidance, templates, metrics and Responsible AI controls
Data governance often fails because it becomes a documentation exercise. AI governance can fail for the same reason when it is treated as a separate compliance silo.
This framework takes a simpler approach:
- identify the data that matters;
- assign clear business ownership;
- define minimum standards;
- implement proportionate controls;
- produce evidence;
- measure and improve;
- apply those same foundations to AI use.
Responsible AI depends on governed data.
Discover → Define → Assign → Control → Measure → Improve
| Stage | Purpose |
|---|---|
| Discover | Identify critical data, systems, domains, flows and governance gaps. |
| Define | Set minimum expectations for ownership, quality, classification, access, lineage and lifecycle. |
| Assign | Make business ownership, stewardship and technical custodianship explicit. |
| Control | Implement practical controls and retain evidence. |
| Measure | Track whether governance is operating and where risk remains. |
| Improve | Use issues, incidents, audits, business change and metrics to mature the model. |
Intake → Triage → Assess → Approve → Pilot → Monitor → Scale / Restrict / Retire
AI governance is treated as an extension of the data-governance model rather than a second bureaucracy.
Start with:
- 3–5 critical data domains
- a named Data Owner and Data Steward for each
- a simple classification model
- a handful of measurable data-quality rules
- a repeatable access review
- a data issue register
- an AI register
- an AI use-case intake and risk assessment
- an approved AI tools register
- an incident / exception process
See QUICKSTART.md for the implementation sequence.
data-governance-framework/
├── README.md
├── QUICKSTART.md
├── INDEX.md
├── CHANGELOG.md
├── DISCLAIMER.md
├── data-governance/
├── responsible-ai/
├── templates/
├── examples/
└── reference/
| Module | Focus |
|---|---|
| 01 — Governance Principles | Practical principles that guide governance decisions. |
| 02 — Operating Model | Governance layers, forums, decision rights and escalation. |
| 03 — Ownership & Stewardship | Business owners, stewards, custodians and consumers. |
| 04 — Data Classification | Simple risk-based classification and handling. |
| 05 — Data Quality | Fitness for purpose, quality rules and issue management. |
| 06 — Access & Security | Business approval, IAM, least privilege and evidence. |
| 07 — Lineage & Metadata | Proportionate traceability for critical data and decisions. |
| 08 — Retention & Lifecycle | Collection, use, sharing, retention and disposal. |
| 09 — Third-Party Data | Supplier processing, shared responsibility and exit. |
| 10 — Implementation Roadmap | Minimum viable governance and 30/60/90-day rollout. |
| 11 — Metrics & Reporting | Practical KPIs/KRIs and executive reporting. |
| 12 — Maturity Model | Risk-based progression from uncontrolled to adaptive. |
| 13 — Framework Alignment | High-level alignment to Australian guidance, NIST, ISO and privacy/security controls. |
The Responsible AI package extends the same operating model into AI adoption and assurance.
- Responsible AI Overview
- AI Principles
- AI Operating Model
- AI Use-Case Lifecycle
- AI Risk Tiering
- AI Data Readiness
- AI Register & Approved Tools
- AI Third-Party Due Diligence
- Human Oversight
- Testing & Monitoring
- Transparency & Contestability
- Incidents & Exceptions
- Generative AI Controls
- Implementation Roadmap
- Metrics & Reporting
The templates folder contains practical registers, assessments, checklists and governance artefacts. CSV versions are included for several working registers.
The examples folder contains illustrative examples for:
- a customer data domain;
- an internal generative-AI tender assistant;
- AI risk-tiering scenarios.
This library draws on high-level concepts from:
- Australian Government / National AI Centre — Guidance for AI Adoption
- NIST AI Risk Management Framework
- NIST Generative AI Profile
- ISO/IEC 42001 — AI management systems
- Australian Privacy Principles
- ISO/IEC 27001 — information security management
This repository does not reproduce proprietary ISO clause text and does not claim certification or formal compliance with any standard.
We start by identifying the data that matters, assign accountable owners, define quality, access, lifecycle and lineage expectations, and put evidence around those controls. Responsible AI then sits on top of that model: each use case has an owner, a risk tier, a data-readiness assessment, human oversight, testing and monitoring. Governance effort is deliberately scaled to the risk rather than becoming bureaucracy.
Business Problem → Practical Pattern → Control → Evidence → Outcome
The objective is not to maximise governance activity. It is to make ownership, decisions, controls and evidence visible enough that the organisation can use data and AI confidently.
This repository is a practical reference framework and should be adapted for organisational size, maturity, risk appetite, sector, jurisdiction, technology environment and regulatory obligations.
It is not legal, privacy, regulatory or certification advice.
Rakesh Randeria
Technology Executive | Strategy | Transformation | Cybersecurity | AI & Digital Enablement
- Website: https://rakeshranderia.com.au
- GitHub: https://github.com/rakeshranderia
- LinkedIn: https://www.linkedin.com/in/rakeshranderia/