From Knowing to Fixing: Why We Built Code-Q

For the last decade, security innovation has focused on finding more vulnerabilities faster. We built scanners, analyzers, and orchestrators that surface every potential issue in every line of code. And we’ve gotten very good at it. Too good.

Ask any security or engineering leader what their biggest pain point is today, and they’ll tell you:

“We’re drowning in findings. We don’t have time to fix them.”

Detection has scaled. Remediation hasn’t.

The Bottleneck Between Knowing and Doing

Every developer knows that when a vulnerability is detected, it doesn’t magically turn into a patch. They still need to step through the code, reason through the data flow, reproduce the bug, and write a fix that’s safe, compliant, and won’t break something else.

That process is slow, manual, and expensive, even for validated vulnerabilities. And that gap between “knowing” and “fixing” is where risk accumulates: security risk, financial risk, time-to-market risk.

I’ve seen this firsthand across enterprises building AI-driven software. You find thousands of open tickets, growing remediation backlogs, and developers caught between security debt and delivery deadlines.

The industry didn’t need more alerts. It needed a way to turn validated findings into verified fixes.

From Validation to Verified Remediation

That’s why we built Code-Q. It’s short for Code Quick Fix, and it’s an AI agent that closes the loop in application security: detection --> validation --> remediation.

We started by building AI Triage, which validates vulnerabilities deterministically, tracing data and control flows like a human would, and confirming whether an issue is truly exploitable. With Code-Q, we’ve extended that reasoning into the next step: generating, testing, and verifying code-level fixes automatically.

Code-Q doesn’t rely on pattern matching or generic patches. It uses deep semantic modeling to understand your application’s logic: how data moves, what’s authorized, and why the issue exists. Then it proposes a fix that aligns with that context, complete with explainable reasoning that any developer can review and trust.

The same kind of fix a skilled developer would write, but faster and still verifiable.

Code-Q is Aptori’s AI agent for automated code remediation. It understands application logic through deep semantic modeling, generates secure and context-aware fixes, and verifies them before merging. By integrating directly into developer workflows like IDEs, GitHub, and CI/CD pipelines, Code-Q bridges the gap between detection and remediation, turning validated vulnerabilities into verified fixes quickly, safely, and with full auditability.

Security That Speaks the Developer’s Language

At Aptori, we’ve always believed that real security progress happens when AI agents follow the same processes developers do. So Code-Q integrates directly into existing workflows: the same IDEs, GitHub and GitLab workflows, and CI/CD pipelines where their code lives and changes.

Developers can review, validate, and merge fixes seamlessly, supported by full transparency and auditability. For organizations under governance frameworks like SOC 2, PCI DSS, or NIST, Code-Q can even produce machine-readable evidence of each applied fix, creating an auditable trail that links every finding to its remediation.

This transforms security from something a reactive, batch process into a continuous, developer-aligned discipline.

Built for the AI-Code Era

We’re all living in a new software development era where AI is creating and securing code. To move fast safely, enterprises need systems that reason, act, and learn alongside their teams.

That’s the vision behind Code-Q and Aptori’s broader platform: a family of agentic AI systems for product security. AI Triage validates, then Code-Q remediates. Together, they form a closed loop that keeps software secure at the speed of modern development.

Security can’t be an obstacle to innovation. With Code-Q, we’re creating a way to make that happen in the workflows developers already rely on.

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