Vulnerability management that doesn't end at “found it”
Most vulnerability management programs hit the same wall: the scanner finds bugs, the bugs get logged, the backlog grows, and nobody actually closes anything. The dashboard fills up. The tickets pile on. The findings outlive the engineers who first opened them.
Kolega is built around the full lifecycle — from detection to resolution — not just the detection part.
The lifecycle, end to end
A vulnerability management program needs to handle four things. Most tools do one or two well. Kolega is built for all four.
Find the vulnerabilities that actually matter
Pattern-matching scanners flag thousands of things that look like vulnerabilities. Most aren't, or are already mitigated, or don't apply to your code. The backlog problem isn't a vulnerability problem — it's a noise problem. Kolega's semantic analysis means the findings you see are real, scoring 92.4% on the RealVuln benchmark compared to 16.7% for Snyk.
Prioritise by what's actually exploitable
Severity ratings from a CVSS score don't tell you whether a vulnerability is reachable in your code. A critical-severity SQL injection in a code path that's not exposed to user input isn't actually critical. Kolega traces data flow to determine real exploitability, so the things ranked “critical” actually are.
Fix them, not just track them
Most VM tools end at the ticket. Kolega opens autofix PRs for findings where the fix is mechanical — the engineer reviews and merges, and the finding closes. Less archaeology, less context-switching, less of the backlog growing forever.
Track to resolution with evidence
Every finding has a state, a timeline, an owner, and a resolution path. Exportable for audits, integrated with your workflow tools (Slack, Jira coming), reportable to leadership.
What a finding looks like in Kolega
getOrder endpointLocation
app/routes/orders.py
Line 47 · get_order()
Real-world severity
Critical · reachable from public API
CVSS adjusted to context
State
Open · detected 2h ago
Awaiting triage
Data flow
The query filters by order_id but not by the authenticated user. Any logged-in user can read any order by changing the URL.
Suggested fix · add Order.user_id == user.id filter
Example finding · severity ranked by real-world exploitability, data flow visualised, fix one click away.
Each finding includes:
- Specific location — file, line, function
- Data flow — where the input enters, what touches it, where it ends up exploitable
- Why it matters in your code — not a generic CVE description
- Severity (real-world) — calculated from exploitability in your code path, not just the abstract bug class
- Suggested fix — apply directly, or trigger an autofix PR
- Audit trail — when detected, who triaged, when resolved, what the resolution was
That last point matters for vulnerability management specifically. The audit trail is what turns a scanner into a VM platform.
Where Kolega fits in a broader VM program
Vulnerability management spans more than just SAST. To be honest about what we cover.
We cover
- Static analysis of application code
- Hardcoded secrets in code
- Cross-file data flow vulnerabilities
- Logic and semantic flaws
We don't cover (yet)
- Dependency / SCA vulnerabilities — Use Dependabot or Snyk for SCA alongside us
- Container vulnerabilities — Trivy or your registry's scanner
- Infrastructure / IaC vulnerabilities — Checkov, Terrascan
- Runtime vulnerabilities — RASP / runtime monitoring tools
- Penetration testing — Pentest firms or bug bounty programs
If you need a full VM program covering all of the above, you'll have multiple tools. Kolega owns the application code layer of that program.
What changes when you run this properly
The teams running Kolega for VM see three patterns shift.
Findings backlog stops growing
Because the noise is low and autofix handles mechanical fixes, the rate of resolution catches up with the rate of detection. Backlogs that were measured in “thousands of open findings” drop to “dozens, all real.”
Critical findings get fixed in days, not quarters
When findings include data flow context, severity matches real exploitability, and fixes can be triggered as PRs, the time from detection to resolution collapses. Findings that would have been triaged into a 90-day SLA close in 48 hours.
Audit prep stops being painful
Because every finding has a tracked lifecycle with timestamps, audit evidence is already there. No spreadsheet exports the week before the auditor arrives.
See it in practice
Vulnerability management as a workflow is hard to understand from a page. See it on real findings in your own code.
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