What is AI Governance?
AI Governance means:
Ensuring AI models and data-driven decisions are accurate, explainable, secure, compliant, and aligned with business policies.
In enterprise data platforms like SAP BDC, governance becomes critical because:
- AI models depend on trusted data
- Cross-domain data is combined
- Regulatory compliance is mandatory
- Decisions impact finance, HR, and operations
Scenario
A company builds:
- Revenue forecasting model
- Workforce attrition prediction
- Spend anomaly detection
All powered by BDC data foundation.
Without governance:
- Wrong KPI definitions → wrong predictions
- Biased HR data → compliance risk
- Uncontrolled access → data leakage
AI Governance Layers in SAP BDC
🔹 1️⃣ Data Governance (Foundation Layer)
BDC ensures:
- Harmonized business semantics
- Certified data products
- Data quality validation rules
- Version-controlled KPI definitions
👉 Garbage in = Garbage out. Governance starts at data layer.
🔹 2️⃣ Access & Security Governance
- Role-based access control (RBAC)
- Domain-based data ownership
- Masking of sensitive fields (HR, salary, PII)
- Audit logging
Prevents unauthorized AI training on sensitive datasets.
🔹 3️⃣ Model Governance
For AI/ML models built on BDC:
- Model version tracking
- Training dataset documentation
- Bias detection checks
- Performance monitoring
- Re-training control
Ensures transparency and explainability.
🔹 4️⃣ Compliance & Regulatory Governance
Important for:
- Financial reporting
- HR analytics
- Cross-border data movement
Governance ensures:
- Data lineage traceability
- KPI reconciliation capability
- Audit-ready documentation
🔹 5️⃣ Federated Governance in Data Mesh
If enterprise adopts Data Mesh:
- Domains own AI use cases
- Central team defines governance standards
- Shared metadata catalog
- Global KPI policies
Balance between autonomy and control.
Governance Flow in SAP BDC
Source Systems ↓ Certified Data Foundation ↓ Governed Data Products ↓ AI / ML Models ↓ Monitoring & Audit Controls
Risks Without AI Governance
❌ Biased predictive models
❌ Financial misreporting
❌ Regulatory penalties
❌ Data privacy violations
❌ Loss of executive trust
Interview-Ready Answer (Short Version)
In SAP Business Data Cloud, AI governance is implemented through harmonized business semantics, certified data products, strict access control, lineage tracking, and model monitoring to ensure AI-driven decisions are accurate, compliant, and explainable.
Architect-Level Add-On (If Interviewer Goes Deeper)
You can say:
AI governance in SAP BDC is not just model governance; it starts at the semantic data layer. By standardizing KPIs and enforcing domain ownership with federated governance, the platform ensures AI models are trained on trusted and compliant enterprise data.
You can also checkout ebooks for SAP BDC – Quick Revision – using the link :
Part 1 : https://topmate.io/vartika_gupta11/1954785
Part 2 : https://topmate.io/vartika_gupta11/1956232
Also can schedule a mock interview either by me or my team at topmate for SAP BDC – 35+ Minutes : https://topmate.io/vartika_gupta11/1962923
You can reach out to me or follow my profile for more such helpful content : Vartika Gupta | LinkedIn