Securing Data During System Migrations
Data migration projects represent one of the highest-risk phases for data exposure. Whether you're moving to the cloud, consolidating systems, or upgrading platforms, RedactionAPI ensures sensitive information is protected throughout the migration process with automated PII detection, real-time redaction, and comprehensive audit capabilities.
The Data Migration Security Challenge
Enterprise data migrations involve moving massive volumes of data between systems, often across network boundaries, through temporary staging environments, and into new platforms with different security models. Each step presents opportunities for data exposure through misconfigured permissions, logging, caching, or human error.
Traditional approaches to migration security rely on encryption in transit and at rest, access controls, and careful planning. While essential, these measures don't address the fundamental risk: the data itself still contains sensitive information that could cause harm if exposed. Redaction during migration provides defense in depth by ensuring that even if data is exposed, the sensitive content has been removed or pseudonymized.
Common Migration Scenarios
On-Premise to Cloud
Moving legacy systems to AWS, Azure, or GCP requires data to traverse networks and enter new environments. Redaction ensures PII doesn't leak during transfer or persist unnecessarily in cloud storage.
System Consolidation
Merging multiple systems into a unified platform often requires data cleansing. Incorporating redaction into consolidation removes redundant PII while maintaining operational data integrity.
Production to Dev/Test
Creating realistic development and testing environments requires production-like data without actual PII. Redaction generates safe datasets that preserve data characteristics without privacy risks.
Vendor Data Sharing
Sharing data with analytics vendors, research partners, or outsourced processing requires removing identifying information before transfer. Automated redaction ensures consistent protection.
Architecture for Migration Redaction
RedactionAPI integrates into migration pipelines as a processing layer that examines data in transit and applies redaction rules before data reaches its destination. This architecture ensures unredacted data never persists in the target system.
Migration Pipeline Architecture
┌─────────────┐ ┌──────────────────┐ ┌─────────────────┐ ┌─────────────┐
│ Source │────▶│ Extract Layer │────▶│ RedactionAPI │────▶│ Target │
│ System │ │ (ETL/Streaming) │ │ Processing │ │ System │
└─────────────┘ └──────────────────┘ └─────────────────┘ └─────────────┘
│
▼
┌─────────────────┐
│ Audit Logs │
│ & Mappings │
└─────────────────┘
Streaming vs. Batch Processing
RedactionAPI supports both streaming and batch processing modes, allowing you to choose the approach that best fits your migration timeline and infrastructure:
Streaming Mode
- Process records as they're extracted
- Minimal memory footprint
- Real-time progress visibility
- Ideal for continuous migration
- Supports CDC (Change Data Capture)
Batch Mode
- Process data in configurable chunks
- Optimized for bulk operations
- Parallel processing across workers
- Ideal for one-time migrations
- Supports scheduled processing windows
Maintaining Data Relationships
One of the most challenging aspects of migration redaction is preserving referential integrity. When PII appears across multiple tables linked by foreign keys, redaction must be consistent to maintain JOIN relationships in the target system.
Deterministic Tokenization
Our deterministic tokenization feature generates consistent replacement values based on cryptographic hashing. The same input always produces the same output within a migration project, ensuring relationships are preserved:
// Configuration for deterministic tokenization
{
"redaction_mode": "tokenize",
"tokenization_config": {
"salt": "project-specific-secret-key",
"preserve_format": true,
"format_preserving_encryption": true
},
"consistency_scope": "migration_project"
}
// Source: customers table
// Original: [email protected]
// Tokenized: [email protected]
// Source: orders table
// Original: [email protected] (same email)
// Tokenized: [email protected] (same token)
Format-Preserving Encryption
For fields where format matters for application compatibility, our format-preserving encryption (FPE) generates tokens that match the original data format:
| Data Type |
Original |
Tokenized |
| SSN |
123-45-6789 |
847-29-1563 |
| Phone |
(555) 123-4567 |
(555) 847-2915 |
| Credit Card |
4111-1111-1111-1111 |
4111-8472-9156-3421 |
| Name |
John Smith |
Alan Davis |
Integration with ETL Tools
RedactionAPI provides native integrations with popular ETL and data integration platforms:
Apache Spark Integration
from redactionapi_spark import RedactionTransformer
# Initialize transformer with API credentials
redactor = RedactionTransformer(
api_key="your-api-key",
batch_size=10000,
parallelism=8
)
# Read source data
source_df = spark.read.jdbc(
url="jdbc:postgresql://source-db:5432/production",
table="customers"
)
# Apply redaction transformation
redacted_df = redactor.transform(
df=source_df,
columns=["name", "email", "ssn", "address"],
mode="tokenize"
)
# Write to destination
redacted_df.write.jdbc(
url="jdbc:postgresql://target-db:5432/analytics",
table="customers_anonymized",
mode="overwrite"
)
Apache Airflow Operator
from redactionapi_airflow import RedactionOperator
redact_customers = RedactionOperator(
task_id="redact_customer_data",
source_conn_id="source_postgres",
target_conn_id="target_snowflake",
source_table="customers",
target_table="customers_anonymized",
redaction_config={
"mode": "tokenize",
"columns": {
"email": {"type": "email", "mode": "tokenize"},
"ssn": {"type": "ssn", "mode": "mask"},
"name": {"type": "name", "mode": "pseudonymize"}
}
},
checkpoint_interval=100000
)
AWS Glue Integration
import boto3
from awsglue.transforms import *
from redactionapi_glue import RedactionTransform
# Read from source
datasource = glueContext.create_dynamic_frame.from_catalog(
database="production",
table_name="customers"
)
# Apply RedactionAPI transform
redacted_frame = RedactionTransform.apply(
frame=datasource,
transformation_ctx="redact_pii",
api_key_secret="redactionapi/key",
pii_columns=["customer_name", "email", "phone"]
)
# Write to destination
glueContext.write_dynamic_frame.from_catalog(
frame=redacted_frame,
database="analytics",
table_name="customers_safe"
)
Database-Specific Connectors
Our database connectors are optimized for bulk operations and understand the specific requirements of each platform:
SQL Databases
- PostgreSQL
- MySQL / MariaDB
- SQL Server
- Oracle
- SQLite
NoSQL Databases
- MongoDB
- DynamoDB
- Cassandra
- Redis
- Elasticsearch
Data Warehouses
- Snowflake
- BigQuery
- Redshift
- Databricks
- Azure Synapse
Schema Discovery and PII Detection
Before migration begins, our schema analysis feature scans source databases to identify columns likely containing PII. This automated discovery reduces manual effort and catches PII that might be missed:
# Schema analysis results
{
"database": "production",
"tables_analyzed": 47,
"pii_columns_detected": 23,
"findings": [
{
"table": "customers",
"column": "full_name",
"detected_type": "person_name",
"confidence": 0.97,
"sample_patterns": ["John Smith", "Jane Doe", "Robert Johnson"],
"recommendation": "pseudonymize"
},
{
"table": "orders",
"column": "billing_email",
"detected_type": "email",
"confidence": 0.99,
"sample_patterns": ["*@*.com", "*@*.org"],
"recommendation": "tokenize"
},
{
"table": "support_tickets",
"column": "description",
"detected_type": "free_text_with_pii",
"confidence": 0.82,
"pii_types_found": ["phone", "email", "ssn"],
"recommendation": "redact_entities"
}
]
}
Progress Monitoring and Reporting
Our dashboard provides real-time visibility into migration progress with detailed metrics:
Failure Recovery and Checkpointing
Long-running migrations require robust failure handling. Our checkpoint system enables recovery without data loss or duplication:
# Checkpoint configuration
{
"checkpointing": {
"enabled": true,
"storage": "s3://migration-checkpoints/project-123/",
"interval_records": 100000,
"sync_mode": "async",
"retention_days": 30
},
"recovery": {
"auto_resume": true,
"max_retries": 3,
"backoff_strategy": "exponential"
}
}
# Resume from checkpoint after failure
migration_client.resume(
project_id="migration-2024-01",
checkpoint_id="chk_20240115_143022"
)
Compliance Audit Trails
Every redaction decision is logged for compliance verification. Audit records include:
- Record Identification: Source table, primary key, field name
- Detection Details: PII type detected, confidence score, detection method
- Redaction Action: Transformation applied (mask, tokenize, pseudonymize)
- Timing: Timestamp, processing duration
- Operator Context: Migration job ID, operator identity, configuration version
Sample Audit Record
{
"audit_id": "aud_9f8e7d6c5b4a",
"timestamp": "2024-01-15T14:30:22.847Z",
"migration_job_id": "mig_2024_01_15",
"source": {
"database": "production",
"table": "customers",
"primary_key": "12847291",
"column": "email"
},
"detection": {
"type": "email",
"confidence": 0.99,
"original_hash": "sha256:a8f5f167..."
},
"redaction": {
"action": "tokenize",
"token_hash": "sha256:7d8e9f01...",
"format_preserved": true
}
}
Migration Best Practices
Recommended Migration Workflow
- 1. Discovery Phase: Run schema analysis on source to identify all PII columns and create redaction rules.
- 2. Configuration Review: Review auto-generated rules with stakeholders and customize as needed.
- 3. Sample Migration: Process a representative sample (1-5%) to validate redaction accuracy and performance.
- 4. Full Migration: Execute full migration with checkpointing enabled and monitoring active.
- 5. Validation: Compare source and target counts, verify referential integrity, confirm no PII leakage.
- 6. Documentation: Export audit logs and generate compliance reports for records.
Enterprise Case Study
Global Bank: Core Banking Migration
A major financial institution migrated 15 years of customer data from legacy mainframe systems to a modern cloud data warehouse. The migration involved:
- • 2.3 billion records across 847 tables
- • 47 distinct PII field types identified
- • Maintained referential integrity across 1,200+ foreign key relationships
- • Completed in 72-hour migration window
- • Zero PII incidents in post-migration audit
"RedactionAPI enabled us to meet regulatory requirements while maintaining the analytical value of our historical data. The deterministic tokenization preserved our ability to perform customer journey analysis without exposing sensitive information."
Ready to Secure Your Data Migration?
RedactionAPI provides enterprise-grade data protection for migrations of any scale. Our team can help you plan and execute a secure migration strategy.
