As Captain Compliance has grown to be a leader in data privacy and compliance software we are also getting asked more and more for AI Governance. While it’s a broad ask we wanted to provide additional resources for those who want to pair our software with any of the resources that we regularly use. For those taking the AIGP exam from the IAPP we provide a free study guide here.
Across industries, regions, and regulatory environments, AI governance professionals consistently raise the same concern: the sheer volume of information they are expected to track, interpret, and operationalize. From new technical standards and regulatory guidance to emerging audit tools and risk frameworks, the challenge is no longer a lack of material, but determining which resources are credible, relevant, and practical for a given role or organization.
This complexity affects both experienced practitioners and those newly entering the field. Understanding which AI governance vendors and AI Governance resources exist, how they differ, and when to apply them has become a core competency of the profession.
Many trusted organizations around the world are producing high-quality tools, templates, and research that directly support day-to-day governance work. Highlighting and organizing these external resources allows practitioners to focus less on discovery and more on implementation.
This collection brings together a curated set of third-party AI governance resources that offer concrete practices, evaluation methods, and policy guidance. While the Captain Compliance’s privacy experts continue to develop original research and professional resources, effective AI governance requires a collective, ecosystem-wide effort. Together, these contributions help define, mature, and strengthen the practice of AI governance globally.
AI Governance Tools
| Tool | Organization | Description | Link |
|---|---|---|---|
| Artificial Intelligence Impact Assessment Tool | Australian Government | Supports structured assessment of AI system risks, impacts, and mitigation measures across public-sector use cases. | Visit |
| Model Cards Overview & Guidebook | Hugging Face | Provides standardized documentation practices for describing model purpose, performance, limitations, and ethical considerations. | Visit |
| Data Labelling | Data Nutrition Project | Introduces “data nutrition labels” to improve transparency, quality assessment, and responsible dataset use. | Visit |
| Human Rights AI Impact Assessment | Ontario Human Rights Commission | Evaluates AI systems through a human-rights lens, focusing on discrimination, equity, and social impact. | Visit |
| Assessing Risks and Impacts of AI | NIST | Guidance for identifying, measuring, and managing AI risks aligned with U.S. federal standards. | Visit |
| Responsible Practices for Synthetic Media | Partnership on AI | Best practices and applied case studies for the responsible creation and deployment of synthetic media. | Visit |
| Decoding AI Governance Toolkit | Partnership on AI | A practical toolkit for navigating evolving AI norms, standards, and regulatory expectations. | Visit |
| AI Alliance Projects | Data & Trusted AI Alliance | Collaborative initiatives focused on trustworthy AI deployment and governance practices. | Visit |
| Health AI Implementation Toolkit | Vector Institute | Guidance for implementing AI responsibly in healthcare settings. | Visit |
| What-If Tool | Google (Open Source) | Interactive tool for analyzing model behavior, fairness, and performance without coding. | Visit |
AI Governance Templates
| Template | Organization | Description | Link |
|---|---|---|---|
| AI Governance Framework Template | Institute of Community Directors Australia | Board-level governance template for overseeing AI strategy and risk. | Visit |
| Responsible AI Impact Assessment Template | Microsoft | Structured assessment for evaluating ethical, legal, and operational AI risks. | Visit |
| Responsible AI Policy Template | Responsible AI Institute | Baseline policy framework to formalize responsible AI commitments. | Visit |
| Algorithmic Impact Assessment in Healthcare | Ada Lovelace Institute | Healthcare-specific assessment for algorithmic risk and fairness. | Visit |
| Consequence Scanning | doteveryone | Workshop-based method for identifying intended and unintended impacts of technology. | Visit |
AI Governance Guidelines, Resources, and Repositories
| Resource | Organization | Focus Area | Link |
|---|---|---|---|
| NIST AI Risk Management Framework | NIST | Risk-based framework for trustworthy AI design, deployment, and oversight. | Visit |
| AI Incident Database | AI Incident Database | Public repository documenting real-world AI failures and harms. | Visit |
| Tools and Metrics for Trustworthy AI | OECD | Catalog of metrics, tools, and policy resources for AI trustworthiness. | Visit |
| AI Verify Toolkit | AI Verify Foundation | Testing framework for assessing AI governance and risk controls. | Visit |
| ATLAS Risk Matrix | MITRE | Threat and risk mapping framework for AI-enabled systems. | Visit |
| GenAI Security Project | OWASP | Security risks and mitigations specific to generative AI systems. | Visit |
Utilizing These AI Governance Resources
The AI governance ecosystem spans regulators, standards bodies, technology companies, academic institutions, civil society groups, and independent researchers—each addressing different dimensions of risk, ethics, and accountability. This diversity is a strength, but it can also be overwhelming.
The goal of this resource is to provide practical entry points into that ecosystem. It is not intended to be exhaustive, nor does it endorse any specific approach. Instead, it offers a starting point for identifying tools and references that can support real-world governance responsibilities.
This collection will continue to evolve over time. As AI governance matures and new resources emerge, additional tools and frameworks will be incorporated to help practitioners stay effective in a rapidly changing field.
