OrganisationKYDAV Demo Org
Assessed byDemo (Organization)
Emaildemo-org@kydav.com
Assessment dateJun 15, 2026
Submissions on record1
Executive Summary
fw_ai_governance · June 2026
Significant governance gaps exist that require a structured remediation plan.
Ownership gaps present. A targeted governance framework addressing key pain points is recommended. To progress to Defined maturity, focus on 9 dimensions below target.
Overall Score
2.3
out of 6
39% of optimum
Extensive
🎯
Governance Maturity
Repeatable (Level 2/5)
⚠️
Dimensions Requiring Action
9 / 20
📅
Assessments Completed
1 on record
Risk Score
2.31
/ 6 · 39% of optimum
Maturity Level
Repeatable
InitialOptimised
High Risk
5
4 medium · 0 low
Standards alignment
This assessment addresses the following recognized standards and regulatory frameworks. Badges reflect current alignment — clause-level mapping is being rolled out progressively.
✓
Bill C-27
✓
EU AI Act Title III
✓
ISO/IEC 42001 Clauses 4-10
✓
NIST AI RMF GOVERN, MAP
Risk Distribution by Dimension
›AI Accountability
0%
High Risk
›AI Strategy and Governance
17%
High Risk
›AI Transparency and Explainability
17%
High Risk
›AI Regulatory Compliance
33%
High Risk
›AI Ethics and Fairness
33%
High Risk
›Human Oversight of AI
50%
Medium Risk
›AI Data Governance
50%
Medium Risk
›AI Risk Management
50%
Medium Risk
›AI Monitoring and Continuous Improvement
67%
Medium Risk
›AI Incident Management
0%
Not assessed
›Third-Party AI Oversight
0%
Not assessed
›AI Model Inventory & Registry
0%
Not assessed
›AI Governance Committee & Decision Rights
0%
Not assessed
›Generative AI & Large Language Model Governance
0%
Not assessed
›AI System Decommissioning & Sunset Process
0%
Not assessed
›Shadow AI Discovery & Governance
0%
Not assessed
›AI Governance Budget & Resourcing
0%
Not assessed
›AI Impact on Workforce & Employment
0%
Not assessed
›Cross-Jurisdictional AI Policy Consistency
0%
Not assessed
›AI Governance Maturity & Continuous Improvement
0%
Not assessed
Dimension scores
AI Accountability
1.0
/ 7
0%
● High Risk
AI Strategy and Governance
2.0
/ 7
17%
● High Risk
AI Transparency and Explainability
2.0
/ 7
17%
● High Risk
AI Regulatory Compliance
3.0
/ 7
33%
● High Risk
AI Ethics and Fairness
3.0
/ 7
33%
● High Risk
Human Oversight of AI
4.0
/ 7
50%
● Medium Risk
AI Data Governance
4.0
/ 7
50%
● Medium Risk
AI Risk Management
4.0
/ 7
50%
● Medium Risk
AI Monitoring and Continuous Improvement
5.0
/ 7
67%
● Medium Risk
AI Incident Management
0.0
/ 7
0%
● Not assessed
Third-Party AI Oversight
0.0
/ 7
0%
● Not assessed
AI Model Inventory & Registry
0.0
/ 7
0%
● Not assessed
AI Governance Committee & Decision Rights
0.0
/ 7
0%
● Not assessed
Generative AI & Large Language Model Governance
0.0
/ 7
0%
● Not assessed
AI System Decommissioning & Sunset Process
0.0
/ 7
0%
● Not assessed
Shadow AI Discovery & Governance
0.0
/ 7
0%
● Not assessed
AI Governance Budget & Resourcing
0.0
/ 7
0%
● Not assessed
AI Impact on Workforce & Employment
0.0
/ 7
0%
● Not assessed
Cross-Jurisdictional AI Policy Consistency
0.0
/ 7
0%
● Not assessed
AI Governance Maturity & Continuous Improvement
0.0
/ 7
0%
● Not assessed
1
Does your organisation have named accountable owners for each AI system in production, with clear responsibility for its performance, outcomes, and any harm it may cause?
Assign a named owner and document the current state this week.
2
Does your organisation have a formal AI governance framework that defines roles, responsibilities, principles, and standards for the development and deployment of AI systems?
Assign a named owner and document the current state this week.
2
Are the decisions and outputs of AI systems used in your organisation explainable to the people affected by them, with clear documentation of how models work and what factors influence their outputs?
Assign a named owner and document the current state this week.
3
Does your organisation actively monitor and comply with applicable AI regulations, standards, and guidelines — including sector-specific requirements — and maintain documentation sufficient for regulatory scrutiny?
Assign a named owner and document the current state this week.
3
Does your organisation have documented ethical principles and a review process to ensure AI systems are fair, unbiased, and do not discriminate against individuals or groups based on protected characteristics?
Assign a named owner and document the current state this week.
4
Does your organisation ensure that high-risk AI decisions are subject to meaningful human review, with the ability to override, correct, or halt AI systems when outputs are incorrect or harmful?
Define and communicate a standard process across all affected teams.
4
Are the data sets used to train, validate, and operate AI systems subject to the same data governance standards as other critical data assets — including quality, lineage, consent, and access controls?
Define and communicate a standard process across all affected teams.
4
Does your organisation formally assess and manage the risks associated with AI systems — including bias, hallucination, security vulnerabilities, and unintended consequences — before and after deployment?
Define and communicate a standard process across all affected teams.
5
Are AI systems in production continuously monitored for performance degradation, drift, and unintended behaviour, with a documented process to retrain, update, or retire models when required?
Define and communicate a standard process across all affected teams.
?
When an AI system produces an incorrect or harmful output in production, does your organisation have a documented process to contain the issue, notify affected stakeholders, and prevent recurrence — distinct from your general IT incident process?
Define and communicate a standard process across all affected teams.
?
For third-party or vendor-supplied AI systems already in production, does your organisation conduct periodic reviews of their performance, compliance, and risk posture — not just the initial vendor selection due diligence?
Define and communicate a standard process across all affected teams.
?
Does your organisation maintain a single, authoritative inventory of every AI model and system in production — including who owns it, what it does, and what data it uses — rather than each team keeping its own untracked list?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a standing AI governance committee with defined membership, a regular meeting cadence, and clear authority to approve, pause, or reject AI initiatives — distinct from simply having written AI principles?
Define and communicate a standard process across all affected teams.
?
Does your organisation have governance specific to generative AI and large language models — covering output review before external use, prompt data handling, and hallucination risk — separate from your general AI governance framework?
Define and communicate a standard process across all affected teams.
?
When an AI system is retired or replaced, does your organisation have a documented process to formally decommission it — including data disposal, access revocation, and removal from the model inventory — rather than simply letting it fall out of use?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a process to actively discover AI tools that employees have adopted without formal approval — so-called shadow AI — and bring them under governance rather than remaining invisible to oversight?
Define and communicate a standard process across all affected teams.
?
Does your organisation allocate dedicated budget and headcount specifically to the AI governance function, distinct from general data governance funding, sufficient to keep pace with the organisation's AI adoption?
Define and communicate a standard process across all affected teams.
?
When an AI system is expected to significantly change or eliminate roles within your organisation, is there a formal governance step — impact assessment, workforce consultation, transition planning — before deployment proceeds?
Define and communicate a standard process across all affected teams.
?
If your organisation operates across multiple countries or provinces, does it apply a consistent baseline of AI governance standards everywhere, with only necessary local adjustments — rather than governance varying by region based on local initiative alone?
Define and communicate a standard process across all affected teams.
?
Does your organisation periodically and formally assess the maturity of its own AI governance programme — not just individual AI systems — and use the results to set a concrete improvement roadmap?
Define and communicate a standard process across all affected teams.
Priority Actions
| Dimension |
Score |
Status |
Recommended next step |
| AI Accountability |
1.0/7 (0%) |
High Risk |
Assign a named owner and document the current state this week. |
| AI Strategy and Governance |
2.0/7 (17%) |
High Risk |
Assign a named owner and document the current state this week. |
| AI Transparency and Explainability |
2.0/7 (17%) |
High Risk |
Assign a named owner and document the current state this week. |
| AI Regulatory Compliance |
3.0/7 (33%) |
High Risk |
Assign a named owner and document the current state this week. |
| AI Ethics and Fairness |
3.0/7 (33%) |
High Risk |
Assign a named owner and document the current state this week. |
| Human Oversight of AI |
4.0/7 (50%) |
Medium Risk |
Define and communicate a standard process across all affected teams. |
| AI Data Governance |
4.0/7 (50%) |
Medium Risk |
Define and communicate a standard process across all affected teams. |
| AI Risk Management |
4.0/7 (50%) |
Medium Risk |
Define and communicate a standard process across all affected teams. |
| AI Monitoring and Continuous Improvement |
5.0/7 (67%) |
Medium Risk |
Define and communicate a standard process across all affected teams. |