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.
You can’t SLA your way out of a flow problem.
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.
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.
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.”
What the quarterly review looks like after.
Months to minutes
For the classes you have automated. The clock stops when the fix merges, not when someone closes the ticket.
Achievable, then boring
Once compliance stops being the hard part, the interesting conversation moves to which class to promote next.
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.
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.
Every major SAST or AI scanner, plus anything that exports SARIF or JSON.
Jira, GitHub, and GitLab stay exactly where they are. The ticket carries a fix instead of homework.
Read-only access to your code. Your pipeline does the merging, under your branch protection rules.
Swap scanners whenever you like. The best scanner changes weekly; the fix layer is the stable investment.
Run it against your own queue.
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 →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.
The standing backlog gets triaged. You find out how much of it was never real.
First fixes land as pull requests, at whatever autonomy level you set per class.
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.
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.