The value of intelligence is heavily dictated by how easily it integrates into an engineering team's daily operations.
Data Extraction vs Event-Driven Alerting
WhoisXML API possesses a phenomenal, highly-scalable REST
API. It is optimized for massive data extraction and bulk querying. Developers
use it extensively to pull down data lakes.
CyberFurl is also built API-first, but our architecture is
designed for Event-Driven Security Operations. Instead of
forcing your developers to write scripts that constantly poll an API looking
for state changes, CyberFurl pushes data to you. We support deep, native
webhooks into Splunk, Microsoft Sentinel,
Slack, Jira, and
Microsoft Teams. The millisecond a vulnerability is detected,
a highly-contextualized alert is pushed directly into your existing SIEM and
SOAR platforms.
Remediation Workflows
Because WhoisXML API provides raw data, the burden of interpreting that data
and executing a fix falls entirely on your organization.
CyberFurl’s Remediation Platform is designed to reduce your
Mean Time to Remediate (MTTR). When an exposure is detected, the payload
pushed to your ticketing system details the exact failing control, the
affected host, the severity, and the precise technical steps required for an
engineer to fix it.
WhoisXML API structures its enterprise pricing largely around API query limits, database access levels, and bulk download bandwidth. For developers building tools, this granular pricing makes sense. However, for a security team trying to simulate continuous monitoring by constantly polling an API, they will quickly hit expensive rate limits and usage ceilings.
CyberFurl operates as a premium SaaS platform focused on continuous security consolidation. Because we fundamentally believe that continuous monitoring is a baseline requirement for EASM, we do not price based on API queries or arbitrarily limit the number of subdomains discovered. You pay for comprehensive intelligence coverage across your entire digital footprint, allowing your SOC to ingest as much alerting data as necessary without fear of unpredictable usage penalties.