GRC Assessment Cockpit
Model Posture Mapping Prototype
Overall score: 68% (Defined)
🛡️ Governance, Risk & Compliance Checkoff
| Sub-Category | Maturity Question | Response Option | Score | Control Mapping |
|---|---|---|---|---|
| Policies & Standards | Do you maintain formally approved AI governance policies aligned with enterprise frameworks? |
|
2 / 2 | ISO 42001 / NIST AI RMF |
|
Security Directive
Successfully established. AI governance policies are actively operationalized and reviewed annually.
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| Risk Management | Do you perform formal AI threat modeling and risk assessments prior to staging model updates? |
|
1 / 2 | NIST AI RMF (MAP) |
|
Required Action
Deploy structured, pre-deployment threat modeling logs for neural networks using specialized attack vector frameworks.
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| Compliance Audit | Do you map all integrated machine learning workloads to regulatory frameworks like GDPR or the EU AI Act? |
|
0 / 2 | EU AI Act / GDPR |
|
Critical Gap Remediation
Establish a centralized AI compliance map indexing sensitive training pipelines to limit regulatory liability under GDPR.
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🔍 Automated AI Security Insights
- ⚠️ Warning Pre-deployment model risk assessments are inconsistently executed across squads.
- ❗ Critical Regulatory classification mapping is currently incomplete on production endpoints.
- ✓ Healthy Model weight permissions and encryption controls are passing standard baselines.
📋 Strategic Priorities Roadmap
- 1. Establish a formal corporate AI GRC oversight board chaired by security stakeholders.
- 2. Integrate automated data privacy scans inside ML training and ETL pipeline runtimes.
- 3. Deploy token-rate controls and payload filters on all external model API gates.