🏛️ AI Governance, Security & Ethics
Readiness Assessment — Evaluate your organization across all three AI assurance pillars
🏛️ AI Governance
🔒 AI Security
⚖️ AI Ethics & Compliance
How to use: For each control below, select the current implementation status in your organization.
Rate honestly — the assessment generates actionable guidance tailored to your actual maturity level across all three pillars.
📊 Assessment Progress
🏛️ AI Governance
0 / 5 controls rated
🔒 AI Security
0 / 5 controls rated
⚖️ AI Ethics & Compliance
0 / 5 controls rated
AI Governance
Who owns AI decisions, how models are approved, and how risks are managed
AI Policy Framework
Documented policies governing AI development, deployment, and decommissioning
AI Ownership & Accountability
Named owners for each AI system with clear accountability for outcomes and risk
Model Lifecycle Management
Processes for model approval, versioning, monitoring, and decommissioning
Risk & Decision Oversight
Structured processes to review, escalate, and mitigate AI-related risks
Shadow AI Prevention
Controls to detect and prevent unauthorized AI systems operating outside governance
AI Security
Protect AI models, data, and infrastructure from misuse, attacks, and unauthorized access
Data Security & Encryption
Encryption, access control, and secure storage for training data and model artifacts
Access Control & Authentication
RBAC/ABAC with strong authentication for all AI infrastructure access
Usage Monitoring & Logging
Continuous monitoring of AI system usage, prompt logs, and inference activity
AI Threat Detection
Detection of adversarial attacks, prompt injection, and model extraction attempts
Model Abuse & Leakage Prevention
Controls to prevent model theft, jailbreaking, and data exfiltration via model outputs
AI Ethics & Compliance
Ensure AI systems are fair, transparent, and aligned with legal and societal expectations
Fairness Assessment
Systematic evaluation of AI outputs for discriminatory outcomes across demographic groups
Bias Detection & Mitigation
Processes to identify and remediate bias in training data, models, and outputs
Transparency & Explainability
Mechanisms for stakeholders to understand how AI decisions are made
Regulatory Compliance
Adherence to applicable AI regulations, privacy laws, and industry standards (EU AI Act, GDPR, etc.)
Decision Auditing
Immutable audit trails for AI-driven decisions, especially in high-stakes domains
Costs 350 Gold Coins — comprehensive guidance across all 15 controls
🏛️ GSE Readiness Assessment Report
📥 Download Your GSE Assessment Report
Save and share this report with your AI governance board and leadership team
×