Just finished a security audit. The auditor loved our Netskope reports. Template attached.

Just had our annual security audit. The external team spent a week digging. Their main positive feedback was on the clarity and depth of our Netskope reporting. Said it cut their evidence collection time in half.

I'm sharing the exact report template we used. It's a simple script that pulls from the Netskope API and formats the key data the auditors always ask for.

```python
#!/usr/bin/env python3
"""
Netskope Audit Report Generator
Exports key findings for last 30 days.
Requires: requests, pandas, python-dotenv
"""

import os
import requests
import pandas as pd
from datetime import datetime, timedelta
from dotenv import load_dotenv

load_dotenv()
BASE_URL = "https://.goskope.com"
API_TOKEN = os.getenv('NETSKOPE_API_TOKEN')
HEADERS = {'Netskope-Api-Token': API_TOKEN}

# Date range for audit
end_date = datetime.utcnow()
start_date = end_date - timedelta(days=30)

def get_alert_logs():
"""Fetch High/Critical alerts."""
url = f"{BASE_URL}/api/v2/events/alerts"
params = {
'starttime': start_date.isoformat() + 'Z',
'endtime': end_date.isoformat() + 'Z',
'alert_type': 'malware,policy',
'severity': 'critical,high'
}
response = requests.get(url, headers=HEADERS, params=params)
return response.json().get('data', [])

def get_policy_violations():
"""Fetch top policy violations by user."""
url = f"{BASE_URL}/api/v1/events/policy"
params = {
'starttime': start_date.isoformat() + 'Z',
'endtime': end_date.isoformat() + 'Z',
'limit': 100
}
response = requests.get(url, headers=HEADERS, params=params)
return response.json().get('data', [])

# ... (additional functions for app instances, DLP events)

if __name__ == "__main__":
alerts = get_alert_logs()
violations = get_policy_violations()

# Create summary DataFrames and export to CSV/Excel
df_alerts = pd.DataFrame(alerts)
df_violations = pd.DataFrame(violations)

with pd.ExcelWriter('netskope_audit_report.xlsx') as writer:
df_alerts.to_excel(writer, sheet_name='High_Critical_Alerts', index=False)
df_violations.to_excel(writer, sheet_name='Policy_Violations', index=False)
```

Key outputs the auditors reviewed:
* High/Critical alert timeline and containment actions.
* Top 10 users by policy violation count.
* Breakdown of blocked vs. allowed activities by app category.
* DLP incidents with data patterns matched.

Ran the script, gave them the Excel file. They validated against the UI. Zero discrepancies.

- bench_beast