Data Classification Architectural Logic

[RSEA0651]

• Integrate with data classification tool API to collect a list of data sources within your organization
• Use the data classification tool's API to classify data in each data source
• Check the classification results for compliance with your organization's data classification policy
• Determine compliance based on the existence, configuration, and active monitoring of the EPP or XDR tool:
  • If the data is classified appropriately, mark the application as compliant.
  • If not, mark the application as non-compliant

Pseudo Code

import data_classification_tool_api
import psycopg2
import json

#Replace with your data classification tool's API client
data_class_api_client = data_classification_tool_api.ApiClient()

#Replace with your PostgreSQL connection settings
conn = psycopg2.connect(database="your_db", user="your_user", password="your_password", host="your_host", port="your_port")

def get_data_sources():
"""
Get a list of data sources (e.g., databases, file storage) in your organization.
"""
# Implement logic to fetch data sources from your organization
return data_sources

def classify_data(data_sources):
"""
Use the data classification tool's API to classify data across your organization.
"""
classification_results = {}
for data_source in data_sources:
classification_result = data_class_api_client.classify_data(data_source)
classification_results[data_source] = classification_result

return classification_results

def analyze_classification_results(classification_results, data_classification_policy):
"""
Analyze classification results to check for compliance with the data classification policy and ensure that sensitive data is properly identified and protected.
"""
non_compliant_data_sources = []
for data_source, result in classification_results.items():
if not result['compliant']:
non_compliant_data_sources.append({
'data_source': data_source,
'violations': result['violations']
})

return non_compliant_data_sources

def generate_reports_and_notifications(non_compliant_data_sources):
"""
Generate reports and notifications based on the analysis of data classification results.
"""
# Implement report generation and notification logic here

def save_results_to_database(non_compliant_data_sources):
"""
Save the analysis results to a PostgreSQL database.
"""
cur = conn.cursor()

for data_source in non_compliant_data_sources:
    # Insert non-compliant data source data into your database table
    insert_query = """INSERT INTO your_non_compliant_table (data_source, violations) VALUES (%s, %s)"""
    cur.execute(insert_query, (data_source['data_source'], json.dumps(data_source['violations'])))

conn.commit()
cur.close()

def main():
data_sources = get_data_sources()
classification_results = classify_data(data_sources)

data_classification_policy = {}  # Define your organization's data classification policy
non_compliant_data_sources = analyze_classification_results(classification_results, data_classification_policy)

generate_reports_and_notifications(non_compliant_data_sources)
save_results_to_database(non_compliant_data_sources)

if name == "main":
main()