AI Vulnerability Management
Triage verified findings using exploitability, system context, and business impact.
OpenHack combines evidence from a finding with application context: where the affected component runs, who can reach it, what data it handles, and what business process it supports.
This context is used to rank work by demonstrated exploitability and expected impact instead of relying on a generic severity score alone. Teams get a clearer queue for remediation and a record of the evidence behind each decision.
How it works
- 01
Collect finding evidence
Bring together the affected code or target, proof of concept, prerequisites, and observed behavior for each issue.
- 02
Add system context
Map exposure, authentication, roles, sensitive data, infrastructure, and the business workflow connected to the finding.
- 03
Prioritize the queue
Rank issues using exploitability, business impact, and blast radius so teams can address the highest-risk work first.
- 04
Track remediation
Keep the finding, fix work, and later regression checks connected as the application changes.
What it covers
Exploitability
Use demonstrated behavior and required access conditions to distinguish actionable issues from unproven alerts.
Business impact
Account for customer data, financial operations, critical workflows, and other application-specific consequences.
Blast radius
Evaluate the users, services, data, and connected systems that could be affected if an issue is exploited.
Regression tracking
Retain issue context so a remediated weakness can be checked again as code and infrastructure change.
What you get
- A prioritized remediation queue
- Exploit evidence and required conditions
- Business impact and blast-radius context
- Connected remediation and regression history
Get started
Connect the managed platform to bring verified findings, application context, prioritization, and remediation work into one workflow.