OrganisationKYDAV Demo Org
Assessed byDemo (Organization)
Emaildemo-org@kydav.com
Assessment dateJun 15, 2026
Submissions on record1
Executive Summary
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 Risk Assessment
0%
Not assessed
AI Ethics Review
0%
Not assessed
Model Explainability
0%
Not assessed
Bias Detection & Mitigation
0%
Not assessed
AI Accountability Framework
0%
Not assessed
AI Incident Management
0%
Not assessed
Model Monitoring & Drift Detection
0%
Not assessed
AI Use Policy Enforcement
0%
Not assessed
Third-Party AI Oversight
0%
Not assessed
AI Transparency Reporting
0%
Not assessed
AI Strategy Alignment
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 Risk Assessment
0.0
/ 7
0%
● Not assessed
AI Ethics Review
0.0
/ 7
0%
● Not assessed
Model Explainability
0.0
/ 7
0%
● Not assessed
Bias Detection & Mitigation
0.0
/ 7
0%
● Not assessed
AI Accountability Framework
0.0
/ 7
0%
● Not assessed
AI Incident Management
0.0
/ 7
0%
● Not assessed
Model Monitoring & Drift Detection
0.0
/ 7
0%
● Not assessed
AI Use Policy Enforcement
0.0
/ 7
0%
● Not assessed
Third-Party AI Oversight
0.0
/ 7
0%
● Not assessed
AI Transparency Reporting
0.0
/ 7
0%
● Not assessed
AI Strategy Alignment
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.
?
Does your organisation have a formally documented and actively implemented approach to ai risk assessment — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to ai ethics review — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to model explainability — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to bias detection & mitigation — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to ai accountability framework — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to ai incident management — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to model monitoring & drift detection — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to ai use policy enforcement — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to third-party ai oversight — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to ai transparency reporting — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
Define and communicate a standard process across all affected teams.
?
Does your organisation have a formally documented and actively implemented approach to ai strategy alignment — with defined ownership, regular review, and evidence of consistent application across all relevant data environments?
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. |