Every mature security programme has a triage backlog. The interesting question is not whether it exists, but whether it is shrinking, stable, or growing. A shrinking backlog means the team is closing findings faster than they arrive. A growing backlog means the tool is producing findings the organisation cannot absorb. Over a year, the direction of the backlog tells you whether the scanner is earning its subscription or quietly undermining the programme it was bought to strengthen.
Griffin AI's engine-plus-LLM architecture shrinks backlogs. Mythos-class pure-LLM tools tend to grow them. The mechanism is not a single feature, it is the compounding effect of several architectural choices, and this post walks through each one.
The arithmetic of a backlog
A triage backlog changes over time as a function of two rates: the arrival rate of new findings, and the closure rate of old findings. If arrivals exceed closures, the backlog grows. If closures exceed arrivals, it shrinks. Both rates are affected by the scanner's architecture.
The arrival rate is a function of how many findings the scanner produces per scan. A scanner that flags every CVE match regardless of reachability produces a high arrival rate. A scanner that applies reachability analysis and VEX context before emitting a finding produces a much lower arrival rate. Griffin AI sits in the second category by design, because the engine does the reachability work before anything reaches the queue. Mythos-class tools sit closer to the first category because their reachability reasoning is model-dependent and consequently less consistent.
The closure rate is a function of how quickly a triage engineer can move each finding from open to resolved. That rate depends on how clearly the finding is presented, how reliable the remediation suggestion is, and how deterministically the scanner recognises that a fix has landed. Griffin AI is strong on all three because the engine gives it the structural grounding to explain itself precisely. Mythos-class tools depend on the model to explain itself, and the explanation quality varies enough that engineers develop a habit of double-checking everything, which slows closure.
The two rates compound. Higher arrivals plus slower closures means a backlog that grows even with constant staffing. Lower arrivals plus faster closures means the same team can drive the backlog toward zero.
Why Griffin AI's arrival rate is lower
Three mechanisms in the Griffin AI engine reduce arrivals before they ever reach a triage queue. The first is reachability filtering. The engine has a call graph and a taint model for every supported language. If the vulnerable function is not reachable from any entry point, the finding is suppressed automatically and recorded with a VEX not_affected statement. The engineer never sees it in the queue.
The second is VEX state awareness. When a finding has been triaged before, either in the current project or in a peer project with the same dependency, the engine propagates the prior verdict forward. Repeated findings do not repeat through the queue. They are closed at ingestion with a reference to the prior decision.
The third is policy evaluation. The engine evaluates the organisation's policies before queueing. If the policy says a CVE below severity medium on a dev-only dependency does not need triage, the finding is closed automatically with a policy reference. Engineers see only the findings that the policy actually says they need to see.
Mythos-class tools implement versions of these mechanisms, but in each case the reasoning goes through the model. That means the mechanism works probabilistically rather than deterministically, and a few percent of findings slip through when they should have been filtered. The few percent compounds into the queue.
Why Griffin AI's closure rate is higher
Closure speed is the second lever, and it depends on three things: explanation quality, remediation quality, and fix detection.
Griffin AI's explanations are grounded in the engine's structural evidence. When a finding says the CVE is reachable from the HTTP handler at server/routes/upload.js:42 via a call chain of three hops through the library's internal parser, the engineer can verify the claim in the IDE in under a minute. Confidence in the finding translates into fast closure when the fix lands.
The remediation pull requests Griffin AI generates are validated against the dependency graph, the license constraints, and the policy gate before they are offered to the developer. When the developer opens the PR, it almost always builds cleanly and almost always resolves the finding. The developer's only decision is whether to merge, which is fast.
Fix detection is the last piece. Griffin AI detects fixes deterministically by re-scanning the post-merge state and comparing against the pre-fix intermediate representation. If the dependency version has changed to a patched release or the reachability verdict has flipped, the finding is closed on the next scan without human intervention.
Pure-LLM tools are weaker on each of the three. Explanations are sometimes model-hallucinated and have to be verified manually. Remediations sometimes fail CI and have to be redone by hand. Fix detection is probabilistic because the model has to infer whether the code change actually addresses the finding, and the inference is not always correct.
A year of backlog data from real teams
We have backlog data from several teams that migrated from a Mythos-class pure-LLM tool to Griffin AI over the last eighteen months. The pattern is consistent. At the point of migration, the backlog is several thousand findings, arrivals are slightly above closures, and the queue is growing at a few percent per month. Within the first quarter after migration, arrivals drop by roughly half because the engine's filtering removes a large block of unreachable and policy-suppressed findings. Closures hold steady or increase slightly because the remaining findings are actionable and the remediations mostly work. The backlog starts shrinking within six to eight weeks.
By the end of the first year on Griffin AI, the teams we track report backlogs in the low hundreds, a ratio of closures to arrivals comfortably above one, and AppSec engineers who have time to work on proactive projects rather than triage queues. Staffing has not increased. The tool has absorbed the repetitive work and exposed only the findings that actually require human judgement.
Backlog as a leading indicator
Executives sometimes ask for a single metric that indicates whether a security programme is healthy. The direction of the triage backlog is a strong candidate. A backlog that trends down, even modestly, signals a programme that is in control of its inputs and able to close findings in a reasonable cadence. A backlog that trends up signals a tool or staffing mismatch that will eventually manifest as breaches, audit failures, or attrition.
Griffin AI is designed to keep the backlog trending down under realistic staffing. Mythos-class tools can deliver insights but do not reliably deliver the flow of closed findings that a shrinking backlog requires. When evaluating tools, ask the vendor for real customers' backlog trajectories over twelve months. The honest vendors will share. The ones who cannot are usually hiding the shape of the curve.