Automated Security Remediation: 95% Less Noise. 76% More Fixes Merged.
Your scanners find the vulnerabilities. Your team cannot keep up with fixing them. Agentic security remediation closes the gap with codebase-aware triage and context-aware code fixes that developers actually merge — across every scanner in your stack, from cloud to Self-Hosted/VPC deployments.
Automated security remediation uses agentic AI systems to transform vulnerability scanner findings into production-ready code fixes without manual developer effort. Unlike detection tools that create alerts, remediation automation generates context-aware pull requests that match your codebase conventions and get merged by developers. It turns months-long backlogs into days.
The Problem Nobody Has Solved With More Scanners
Enterprise security teams are drowning. Not in a lack of detection, but in a total absence of resolution.
The average time to remediate a single vulnerability is 252 days (Veracode State of Software Security, 2025). And between 71% and 88% of those findings are false positives that never should have reached a developer's queue (Ponemon Institute, 2024).
The typical AppSec team manages 5.3 security tools (Gartner, 2024). Each tool detects. None of them fix. Every new scanner added to the stack creates more alerts, more triage work, and more developer fatigue without reducing a single line of vulnerable code.
The result is predictable. Teams that were already underwater fall further behind. Backlogs grow. MTTR stretches. Developers stop trusting security findings altogether.
"I am still manually doing triaging from GitLab... with the new platform coming and the majority of our dev teams already planning their bandwidth and their cycles for the new development."
— Head of AppSec, Jellyvision
This is not a tooling problem. It is a structural one. Organizations have invested heavily in detection and almost nothing in resolution. The find-to-fix ratio is fundamentally broken.
"The part where we have to approve the remediation, especially if it's already mitigated within the code, is manual. I continue to highlight within the team that there is just lack of process."
— CISO, Charles Schwab
Why Scanners Alone Will Never Close the Gap
Detection and resolution are fundamentally different problems. Scanners excel at finding potential vulnerabilities. Three structural gaps prevent them from finishing the job — and one capability closes every one.
Detection Is Not Resolution
A scanner that flags a SQL injection vulnerability does not produce a working fix. The finding enters a queue. A security engineer triages it. A developer writes the patch. Weeks pass. In many organizations, the majority of findings never reach resolution at all — they sit in backlogs, aging past SLA windows, accumulating compliance debt.
Suggestions Are Not Merged Fixes
Some scanners now generate fix recommendations. But a suggested code change that ignores your project's conventions, dependency constraints, or test expectations will be rejected by developers. Merge rates for generic suggestions hover in the single digits. Suggestions without codebase context create more review work, not less.
Single-Scanner Scope Misses the Picture
A SAST tool sees one set of problems. An SCA tool sees another. A DAST tool sees a third. Without a consolidated view across all scanners, teams waste hours cross-referencing duplicates and conflicting priorities. No individual scanner can deliver unified remediation.
Automated Security Remediation
Genuine automated remediation requires two co-equal capabilities: triage automation that eliminates 95% of false positives through exploitability analysis, and remediation automation that generates context-aware fixes with a 76% merge rate. Pixee delivers both across 10+ scanners, consolidating your entire detection stack into a single resolution pipeline.
Detection Only vs. Detection + Automated Remediation
| Capability | Scanners Alone | Scanners + Pixee |
|---|---|---|
| Find vulnerabilities | Yes | Yes (uses your existing scanners) |
| Triage false positives | No (manual review) | Yes (95% automated reduction) |
| Generate production-ready fixes | No (manual developer work) | Yes (76% merge rate across 100K+ PRs) |
| Match your codebase conventions | No | Yes (convention analysis before generation) |
| Detect breaking changes pre-merge | No | Yes (80-90% confidence scoring) |
| Work across multiple scanners | No (siloed per vendor) | Yes (10+ scanner integrations in one workflow) |
| Measure remediation outcomes | Partial (detection metrics only) | Complete (merge rate, MTTR, backlog burndown) |
The workflow: your scanners detect, Pixee triages to the 5% that are genuinely exploitable, and Pixee generates context-aware pull requests your developers merge. The detection layer stays. The resolution layer is new.
See Automated Remediation Running on Your Stack
Watch Pixee triage and fix vulnerabilities from your existing scanners in a live technical demo. No slides, no prototype — real findings, real fixes, real merge rates.
See a Live Technical DemoKey Capabilities
Five capabilities separate real automated remediation from bolted-on fix suggestions. Each addresses a specific failure mode in how organizations try to close the find-to-fix gap.
Scanner-Agnostic Integration
Most teams run multiple security tools. Pixee integrates with 10+ scanner platforms — Semgrep, SonarQube, Snyk, Checkmarx, CodeQL, Veracode, Fortify, and more — consolidating findings into a single prioritized queue with unified remediation. No rip-and-replace.
Convention-Matching Fixes
The difference between a fix that gets merged and one that gets rejected often comes down to style. Pixee analyzes your existing patterns — imports, error handling, logging, test structure — and generates changes that follow your team's conventions. That is why 76% of fixes get merged across 100,000+ enterprise PRs.
Breaking Change Detection
Not every fix is safe to merge without review. Pixee applies 80-90% confidence scoring to flag changes that could affect application behavior, giving developers clear signals about which PRs need careful review and which can be approved quickly. Transitive dependency updates are validated against the full dependency graph before the PR opens.
Preference Learning
Teams that consistently modify certain fix patterns train the system to adapt. Over time, the platform learns your team's preferences and adjusts its output accordingly. A team that always wraps exceptions in a custom handler will see future fixes follow that same pattern without manual correction. This is what makes Pixee compound in value — the longer you run it, the more its fixes read like code your team would have written.
Root-Level SCA Resolution
Dependency vulnerabilities require more than bumping a version number. A vulnerable transitive dependency four levels deep in your tree cannot be fixed by updating the direct dependency alone. Automated SCA remediation traces vulnerabilities through the full dependency chain and resolves them at the root level, avoiding the cascade of breaking changes that manual upgrades often trigger. The platform also validates that upgrades maintain API compatibility before generating the PR.
