OpenRisk Module
Quantify. Predict. OpenRisk.
Stop fighting thousands of irrelevant alerts. OpenRisk uses AI and global threat intelligence to predict the *real* likelihood of exploitation for every vulnerability.
The Predictive Defense Cycle
1. Data Ingestion
Collect real-time data from OpenWatch, OpenAsset, and external threat feeds (MITRE, CISA).
2. Auto Scoring
Proprietary ML model calculates a Time-to-Exploit (TTX) prediction and assigns a Contextual Risk Score (CRS).
3. Automated Flow
Prioritized issues are pushed directly to OpenSec, triggering automated remediation flows or reporting.
Features That Change the Game
From CVSS to Contextual Scoring, OpenRisk brings intelligence to prioritization.
Contextual Risk Score (CRS)
Beyond CVSS: Score threats based on asset value, exploit maturity, and exposure level.
Time-to-Exploit Prediction
Machine learning forecasts the hours/days before a vulnerability is likely to be weaponized in the wild.
Remediation Playbooks
Automated patch deployment policies via OpenFlow, triggered when CRS exceeds a predefined threshold.
Live Asset Tagging
Dynamic linking to OpenAsset inventory to assess the business criticality of the affected services.
Global Threat Feeds
Continuous integration of CISA KEV, Exploit-DB, and private intelligence sources.
Compliance Mapping
Automatic mapping of high-priority risks to regulatory frameworks (e.g., SOC 2, ISO 27001).