Research & Operational Analytics
Deepnote: Optional / StandbyLive operational metrics, crawl yields, exportable research datasets, and optional remote notebook automation.
Total Investigations
0
Completed web research queriesActive Research Projects
0
Collaborative workspacesSecurity Interventions
0
SSRF & egress blocksExportable Records
0
Dataset rows collectedDeepnote Data Science Notebook Snippet
Python 3.10+ / PandasIngest live BurnBox research telemetry and crawl datasets directly into Deepnote for exploratory data analysis, link graph mapping, and domain threat intelligence:
import requests
import pandas as pd
# Fetch notebook-ready research telemetry from BurnBox Control Plane
response = requests.get(
"https://burnbox.vorpent.me/api/analytics",
headers={"x-burnbox-org": "org_burnbox_secops"}
)
data = response.json().get("data", {})
# Load investigation telemetry into DataFrame
records = data.get("records", [])
df = pd.DataFrame(records)
print(f"Total Research Traces: {len(df)}")
if not df.empty and 'status' in df.columns:
print(df['status'].value_counts())
print(df.head())