Automated Data Protection: Securing Financial Data Lakes on AWS
By Braincuber Team
Published on February 6, 2026
In the financial sector, a data lake is both a treasure trove and a liability. While it fuels analytics and AI, it also risks exposing Sensitive Personal Information (SPI) or Payment Card Industry (PCI) data if not rigorously governed.
Manual compliance checks scale poorly. In this guide, we'll architect an automated defense system for "SecureCapital Bank". We will implement a multi-layered protection strategy using Amazon Macie for discovery, AWS Glue for remediation, and S3 Object Lambda for dynamic masking.
Compliance at Scale:
- Discovery: Automatically find credit card numbers in petabytes of S3 data.
- Redaction: Mask PII in real-time streams before they hit storage.
- Access Control: Dynamically redact sensitive fields for lower-privileged users.
Step 1: Automated Discovery with Amazon Macie
Macie uses machine learning to recognize sensitive data patterns (e.g., Credit Card numbers, Social Security Numbers) within your S3 buckets.
resource "aws_macie2_account" "secure_capital_macie" {
finding_publishing_frequency = "FIFTEEN_MINUTES"
status = "ENABLED"
}
resource "aws_macie2_classification_job" "periodic_scan" {
job_type = "SCHEDULED"
name = "Daily-PCI-Scan"
s3_job_definition {
bucket_definitions {
account_id = "123456789012"
buckets = ["secure-capital-datalake-raw"]
}
}
schedule_frequency {
daily_schedule = true
}
}Step 2: Remediation with AWS Glue
Once Macie identifies a sensitive file, or as part of a standard ingestion pipeline, AWS Glue's Detect PII Transform can automatically redact or hash the data.
# Detect and Redact PII
def redact_pii(glueContext, frame):
return glueContext.detect_pii(
frame=frame,
entity_types_to_detect=["CREDIT_CARD", "SSN", "EMAIL"],
# Options: REDACT, HASH, or PARTIAL_REDACT
output_mode="REDACT_WITH_STRING",
redaction_string="[REDACTED]"
)
datasource0 = glueContext.create_dynamic_frame.from_catalog(...)
redacted_frame = redact_pii(glueContext, datasource0)
glueContext.write_dynamic_frame.from_options(
frame=redacted_frame,
connection_type="s3",
connection_options={"path": "s3://secure-capital-datalake-clean/"},
format="parquet"
)Step 3: Dynamic Masking on Consumption
Sometimes you can't scrub the source data because downstream applications need it raw. However, human analysts typically shouldn't see it. S3 Object Lambda solves this by scrubbing data on-the-fly as it is requested.
import boto3
import csv
import io
def lambda_handler(event, context):
s3 = boto3.client('s3')
# Get the object from standard S3
r = s3.get_object(Bucket=event['configuration']['payload']['supportingAccessPointArn'],
Key=event['userRequest']['url'])
original_content = r['Body'].read().decode('utf-8')
# Redact sensitive columns on the fly
transformed_content = ""
reader = csv.DictReader(io.StringIO(original_content))
fieldnames = reader.fieldnames
output = io.StringIO()
writer = csv.DictWriter(output, fieldnames=fieldnames)
writer.writeheader()
for row in reader:
# Mask Account Number
if 'AccountNumber' in row:
row['AccountNumber'] = '****' + row['AccountNumber'][-4:]
writer.writerow(row)
transformed_content = output.getvalue()
# Write back to the requestor
s3.write_get_object_response(
RequestRoute=event['userRequest']['outputRoute'],
RequestToken=event['userRequest']['outputToken'],
Body=transformed_content
)
return {'status_code': 200}Conclusion
By automating detection with Macie and enforcement with Glue and Lambda, SecureCapital Bank moved from reactive "fire-fighting" audits to proactive data protection. Compliance is no longer a manual checklist—it's code.
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