---
title: AppSecAI for vulnerability management — Stop managing tickets
description: Intake is a rate, not a queue. AppSecAI turns findings from any scanner into validated fixes your team merges, so the backlog burns down.
image: https://www.appsecai.io/hubfs/og/appsecai-og-card.png
---

<https://www.appsecai.io/vulnerability-management#body>

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# Stop managing tickets. Start eliminating vulnerabilities.

Findings from the scanners you already run arrive triaged at 97% accuracy with a validated fix already attached. MTTR stops measuring how long a negotiation takes and starts measuring how long a merge takes.

[See it with your findings](https://calendly.com/brucefram/30min) [How it works →](https://www.appsecai.io/expert-fix-automation)

The change in one line

Your program was built to manage findings because fixing didn’t scale. Fixing scales now.

Median time to a validated fix

243 days

Minutes

Cost per fix

$5,000–$20,000

Pay only for fixes you accept

Queue direction

42% of flaws age into debt

A burndown you can show

Apps left uncovered

Most of the portfolio

The exceptions you choose

## You can’t SLA your way out of a flow problem.

280 → 581

The average codebase went from 280 to 581 open source vulnerabilities in a single year.1 Intake is not a queue you are behind on. It is a rate you are losing to.

Intake is accelerating

Frontier models such as Anthropic’s Mythos are finding vulnerabilities that survived millions of automated tests. Detection just got a generational upgrade, and every one of those findings arrives in your queue.

Outflow is capped

A manual fix runs $5,000 to $20,000 and a 243-day median. That cap is set by budget and engineering hours, and no scoring model raises it. Prioritization re-orders the queue. It does not shrink it.

> “Finding, by itself, is not security. It is documentation of insecurity.”

David Kosorok, Enterprise Security Leader · Product Security · Application Security · Author

## What the quarterly review looks like after.

MTTR

### Months to minutes

For the classes you have automated. The clock stops when the fix merges, not when someone closes the ticket.

SLA compliance

### Achievable, then boring

Once compliance stops being the hard part, the interesting conversation moves to which class to promote next.

Backlog age

### A burndown that trends down

The aging report stops being a list of apologies and becomes a line with a slope you can point at.

Apps left uncovered

### All applications

Including the mediums and lows that pile up unread while the criticals get all the attention.

Every closed finding carries an evidence chain: the finding, the fix, the validation, the approval. The audit answer is generated, not assembled.

## Fits the stack you already run.

Scanners

Every major SAST or AI scanner, plus anything that exports SARIF or JSON.

Ticketing

Jira, GitHub, and GitLab stay exactly where they are. The ticket carries a fix instead of homework.

Access

Read-only access to your code. Your pipeline does the merging, under your branch protection rules.

Tool churn

Swap scanners whenever you like. The best scanner changes weekly; the fix layer is the stable investment.

## Run it against your own queue.

The benchmark

### Proof you can rerun

97% triage accuracy and 93% fix accuracy, published on the OWASP Benchmark with thousands of examples you can clone and rerun.

[See the numbers →](https://www.appsecai.io/performance-metrics)

The terms

### Cost per closed finding, as a number

You pay per accepted fix, so the cost of retiring a finding becomes a figure you can put on a slide and trend. A fix you reject costs nothing.

Getting there

Day 1

The standing backlog gets triaged. You find out how much of it was never real.

Week 1

First fixes land as pull requests, at whatever autonomy level you set per class.

Month 1

The burndown is visible, and the aging report has a direction for the first time.

## Frequently asked questions

How is this different from prioritization and ASPM tools?

They re-order the queue. This shrinks it. Prioritization tells you which of the 581 findings to work on first, which is useful right up until you notice that the constraint is remediation capacity, not sequencing. Keep the ASPM tool if you like it. Findings flow in from it the same as from a scanner.

Our scanner already has an autofix button. Why not use that?

An autofix button puts the burden back on a developer. Someone still has to open it, judge the suggestion, and take responsibility for merging it. What if there is no developer? On most of the queue there isn’t one available, which is how the backlog got its age in the first place. Autofix also covers only that scanner’s findings, in the languages that vendor supports, with suggestions that are not independently validated. This works across every tool you run, validates each fix against the finding it claims to close, and closes it under your policy rather than someone’s attention.

What happens to our SLA definitions?

They stay, and you will likely tighten them. The definitions you already wrote, critical in 7 days and high in 30, map directly onto automation policy per vulnerability class. What changes is that the clock measures how long a merge takes rather than how long a negotiation takes.

Who approves fixes at merge-on-green?

You do. Approval never goes away — it moves up, from the pull request to the policy. A class reaches merge on green only after you promote it, every merge is logged with the policy that authorized it, and you can turn the dial back down at any time. AppSecAI has read-only access; the merge happens in your pipeline, under your branch protection rules.

1 Black Duck, 2026 Open Source Security and Risk Analysis report.

## Bring us the oldest thing in your queue.

Upload your scanner results and see fixes for your own findings in 30 minutes. You pay for the fixes you keep and nothing for the ones you reject.

[See it with your findings](https://calendly.com/brucefram/30min) [Read the benchmark →](https://www.appsecai.io/performance-metrics)

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