Gomboc.AI, a leader in AI Code Security Assistants (ACSA), announced accelerating adoption across both its Community Edition and enterprise customers, driven by a clear market shift away from alert-driven security tools toward deterministic, code-level remediation.
Unlike traditional CSPM and IaC scanners that stop at findings, Gomboc is the only ACSA platform delivering 100% accurate, deterministic fixes, automatically converting cloud and Infrastructure-as-Code risks into verified, merge-ready code changes that integrate directly into developer workflows.
Community Adoption Shows Fixes Beat Findings
In Q4 alone, Gomboc’s Community Edition surpassed 1,180 downloads in under 30 days, signaling strong demand from platform engineers and DevOps teams looking to reduce remediation overhead without adding tickets or dashboards.
Early usage data shows sustained, repeat engagement:
- 3,400+ IaC repositories analyzed, primarily Terraform
- 18,000+ policy findings evaluated
- 8,200+ issues automatically remediated with merge-ready fixes
- ~72% of detected issues resolved automatically without manual rewrite
- Hundreds of engineers running repeat scans, indicating ongoing use beyond initial trials
Rather than triaging alerts, engineers consistently reviewed pull requests generated by Gomboc, validating fixes in code instead of managing security backlogs.
“Engineers don’t need more findings. They need fixes they can trust,” said Ian Amit, CEO and Co-Founder of Gomboc. “The Community Edition validated what we believed from day one. When remediation fits naturally into engineering workflows, teams fix more issues with the same headcount.”
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Upwork Eliminates IaC Security Debt Across Hundreds of Repositories
Enterprise adoption mirrors this pattern at scale.
At Upwork, the world’s human and AI-powered work marketplace, infrastructure teams used Gomboc to remediate misconfigurations across 250+ Terraform repositories in their first month.
Results included:
- 125–200 engineering hours reclaimed per month
- Remediation time per repository cut from 45–60 minutes to under 20 minutes
- 336 Terraform codebases standardized under consistent policy enforcement
- Security fixes delivered automatically as pull requests, aligned with internal policies
Instead of interpreting scanner alerts and writing patches manually, engineers reviewed deterministic fixes generated by Gomboc and merged them directly into production workflows.
“Gomboc eliminated weeks of manual remediation across our Terraform repositories,” said Shawn Chakravarty, Senior Director of Active Defense at Upwork and a SANS Certified Instructor. “That shift from reviewing alerts to reviewing fixes saved significant time while improving consistency and security.”
From Tickets to Deterministic Outcomes
Other enterprise customers, including C&S Wholesale Grocers, have adopted Gomboc to move away from ticket-driven cleanup toward automated, policy-enforced remediation in Git. Across deployments, a consistent theme has emerged: detection is no longer the bottleneck, manual remediation is.
By replacing alerts with deterministic fixes, Gomboc enables to enforce policies directly in code while returning measurable time back to engineering organizations.
This shift is also reflected in user feedback across G2 and community forums.
“Gomboc.AI has accurate fixes, not just alerts,” wrote one enterprise user. “It integrates cleanly into developer workflows and significantly reduces remediation backlog.”
Another platform engineer noted, “Its deterministic approach produces predictable, auditable, production-ready changes, which sets it apart from generative AI tools.”
ACSA Executed All the Way Through Code
Gomboc defines AI Code Security Assistants as more than tools that suggest changes or flag issues. Security assistance, the company argues, is only complete when risk is removed from code.
“We are ACSA, executed all the way through code,” said Amit. “If findings don’t turn into verified fixes, assessment hasn’t happened. Gomboc closes that gap by making remediation automatic, deterministic, and auditable at scale.”
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