IBM WatsonX Enterprise Demo
Enterprise AI Governance & Compliance Workflow
$ llmswap providers
╭─────────────┬──────────┬──────────────────────┬─────────────────────╮
│ Provider │ Status │ Model │ Issue │
├─────────────┼──────────┼──────────────────────┼─────────────────────┤
│ WATSONX │ ✓ Ready │ llama-3-1-70b │ │
│ ANTHROPIC │ ✓ Ready │ claude-3-5-sonnet │ │
│ OPENAI │ ✓ Ready │ gpt-4o │ │
╰─────────────┴──────────┴──────────────────────┴─────────────────────╯
$ llmswap chat --provider watsonx
🤖 Starting chat with watsonx.ai (llama-3-1-70b) | Enterprise-grade AI
💼 Data governance: ON | Compliance logging: ENABLED
You: I need to analyze our customer churn data while ensuring GDPR compliance. Can you help me design a privacy-preserving analytics pipeline?
WatsonX: I'll help you design a GDPR-compliant customer churn analytics pipeline. Here's a comprehensive approach that prioritizes data privacy:
## 1. Data Minimization & Purpose Limitation
**Data Collection Strategy:**
```python
# Only collect essential features for churn prediction
essential_features = [
'customer_tenure_months',
'monthly_charges_normalized',
'service_usage_metrics',
'support_ticket_count',
'contract_type',
# NO direct identifiers like email, phone, address
]
# Pseudonymization at ingestion
def pseudonymize_customer_data(df):
df['customer_id'] = hash_customer_id(df['original_id'])
df.drop(['original_id', 'email', 'phone'], axis=1, inplace=True)
return df
2. Privacy-Preserving Feature Engineering
Differential Privacy Implementation:
import diffprivacy as dp
def add_noise_to_aggregates(data, epsilon=1.0):
"""Add calibrated noise for differential privacy"""
mechanism = dp.LaplaceMechanism(epsilon=epsilon)
# Add noise to aggregated metrics
data['avg_monthly_spend'] = mechanism.add_noise(data['monthly_charges'].mean())
data['churn_rate_segment'] = mechanism.add_noise(data.groupby('segment')['churned'].mean())
return data
3. Federated Learning Architecture
Multi-Region Compliance:
# watsonx-federated-config.yaml
federated_learning:
regions:
- eu-west:
data_residency: "EU_GDPR"
encryption: "AES-256"
audit_level: "FULL"
- us-east:
data_residency: "US_CCPA"
encryption: "AES-256"
audit_level: "STANDARD"
model_aggregation:
method: "secure_averaging"
min_participants: 3
privacy_budget: 1.0
4. Explainable AI for Regulatory Requirements
Model Interpretability:
# Generate GDPR Article 22 compliant explanations
from watsonx import ExplainabilityService
def generate_churn_explanation(customer_features):
explanation = ExplainabilityService.explain(
model=churn_model,
instance=customer_features,
explanation_type="LIME",
regulatory_framework="GDPR_Article_22"
)
return {
'prediction': explanation.prediction,
'confidence': explanation.confidence,
'top_factors': explanation.feature_importance[:5],
'explanation_id': explanation.audit_id,
'model_version': explanation.model_version
}
Key Benefits of WatsonX for Enterprise:
- ✅ Data Governance: Built-in lineage tracking and audit trails
- ✅ Regulatory Compliance: GDPR, CCPA, HIPAA ready
- ✅ Enterprise Security: End-to-end encryption, VPC deployment
- ✅ Model Risk Management: Automated bias detection and mitigation
- ✅ Hybrid Cloud: On-premises + cloud flexibility
Would you like me to elaborate on any specific aspect of this privacy-preserving pipeline?
You: This looks great! Can you also help me set up automated bias monitoring for our model?
WatsonX: Absolutely! Here’s a comprehensive bias monitoring setup using WatsonX’s built-in fairness toolkit:
Automated Bias Detection Pipeline
1. Fairness Metrics Configuration:
from watsonx.ai import FairnessMonitor
from watsonx.ai.fairness import BiasMetrics
# Define protected attributes and fairness criteria
fairness_config = {
'protected_attributes': ['gender', 'age_group', 'geographic_region'],
'favorable_outcome': 'no_churn',
'fairness_thresholds': {
'demographic_parity': 0.1, # Max 10% difference between groups
'equalized_odds': 0.15, # Max 15% TPR/FPR difference
'calibration': 0.05 # Max 5% calibration difference
}
}
# Initialize bias monitor
bias_monitor = FairnessMonitor(
model_endpoint='churn-prediction-v2',
configuration=fairness_config,
evaluation_frequency='daily'
)
2. Real-time Monitoring Dashboard:
# Automated bias detection with alerts
def setup_bias_alerts():
monitor = bias_monitor.create_subscription(
metrics=['demographic_parity', 'equalized_odds'],
alert_thresholds={
'demographic_parity': 0.08, # Alert if bias > 8%
'equalized_odds': 0.12 # Alert if EO difference > 12%
},
notification_channels=['slack', 'email', 'pagerduty']
)
return monitor
# Custom bias remediation workflow
@bias_monitor.on_bias_detected
def bias_remediation_workflow(bias_report):
"""Triggered automatically when bias exceeds thresholds"""
# 1. Log incident with full context
incident_id = audit_logger.log_bias_incident({
'timestamp': bias_report.timestamp,
'affected_groups': bias_report.affected_groups,
'bias_magnitude': bias_report.bias_score,
'model_version': bias_report.model_version
})
# 2. Automatic model rollback if critical bias detected
if bias_report.bias_score > 0.15: # Critical threshold
model_manager.rollback_to_previous_version()
alert_manager.send_critical_alert(
f"Model rolled back due to bias incident: {incident_id}"
)
# 3. Trigger retraining with bias mitigation
retraining_pipeline.trigger_bias_aware_training(
bias_report=bias_report,
mitigation_strategy='adversarial_debiasing'
)
3. Bias Mitigation Strategies:
# Pre-processing: Data balancing
def balance_training_data(df, protected_attr='gender'):
"""Ensure balanced representation across protected groups"""
from watsonx.ai.preprocessing import FairSampler
sampler = FairSampler(strategy='stratified_sampling')
balanced_df = sampler.balance_dataset(
data=df,
protected_attributes=[protected_attr],
target_variable='churned'
)
return balanced_df
# In-processing: Fairness-aware training
def train_fair_model(X_train, y_train, protected_features):
from watsonx.ai.algorithms import FairClassifier
fair_model = FairClassifier(
base_estimator='gradient_boosting',
fairness_constraints={
'demographic_parity': {'weight': 0.3},
'equalized_odds': {'weight': 0.4}
},
protected_attributes=protected_features
)
fair_model.fit(X_train, y_train)
return fair_model
# Post-processing: Threshold optimization
def optimize_fair_thresholds(model, X_test, y_test, protected_attr):
from watsonx.ai.postprocessing import FairThresholdOptimizer
optimizer = FairThresholdOptimizer(
fairness_metric='equalized_odds',
optimization_target='balanced_accuracy'
)
fair_thresholds = optimizer.fit(
predictions=model.predict_proba(X_test),
true_labels=y_test,
protected_attributes=X_test[protected_attr]
)
return fair_thresholds
4. Regulatory Reporting:
# Generate automated compliance reports
def generate_fairness_report(model_id, reporting_period='monthly'):
report = bias_monitor.generate_compliance_report(
model_id=model_id,
period=reporting_period,
include_sections=[
'bias_metrics_summary',
'protected_group_analysis',
'model_performance_by_group',
'remediation_actions_taken',
'regulatory_attestation'
]
)
# Auto-submit to compliance team
compliance_portal.submit_report(
report=report,
attestation_required=True,
reviewer_groups=['AI_Ethics_Board', 'Legal_Compliance']
)
return report
Enterprise Advantages:
- 🔍 Continuous Monitoring: 24/7 bias detection across all model versions
- 🚨 Automated Alerts: Real-time notifications when fairness thresholds exceeded
- 📊 Regulatory Reports: Auto-generated compliance documentation
- 🔄 Bias Remediation: Automated rollback and retraining workflows
- 📈 Trend Analysis: Historical bias metrics and improvement tracking
This setup ensures your churn model stays fair and compliant with evolving regulations. Want me to dive deeper into any specific fairness metric or remediation strategy?
You: /stats
📊 Session Statistics: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Messages: 6 Provider: watsonx.ai (llama-3-1-70b-instruct) Total tokens: 1,456 (789 input, 667 output) Total cost: $0.18 Session duration: 8m 32s ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🏢 Enterprise Features Used: • Data governance and audit logging • GDPR/CCPA compliance frameworks • Automated bias detection and monitoring • Federated learning across regions • Regulatory reporting automation
💰 Cost Comparison (Enterprise Workloads): • WatsonX: $0.18 (includes governance overhead) • Azure OpenAI: $0.24 (+ compliance tooling costs) • AWS Bedrock: $0.21 (+ additional security setup) • Google Vertex: $0.19 (+ audit logging fees)
🎯 Value Delivered: ✅ Privacy-preserving churn analysis pipeline ✅ Automated bias monitoring with alerts ✅ Complete regulatory compliance framework ✅ Enterprise-grade security and auditability
Ready for production deployment with full enterprise governance! 🚀 ```