You have 3,000 critical vulnerabilities in your backlog. Your team picks the ones at the top, starts working through them, and three months later the backlog is somehow bigger than when you started.
The industry’s answer to this has been remarkably consistent: scan more, find more, patch more. Better scanners. More coverage. Faster scan cycles. And it keeps not working. Not because the scanners are bad, but because we’ve been answering the wrong question.
The question most vulnerability management tools answer is “what vulnerabilities exist in your environment?” That’s a fine question. It’s also the reason your team is stuck in backlog prison. Thousands of findings across cloud and code. Everything labelled critical. Endless spreadsheets, meetings, and manual triage. Engineering time burned patching things that aren’t actually a risk, while the one truly exploitable issue sits buried somewhere in the queue.
The question that changes everything is simpler: which of these can actually be exploited here?
Is it exploitable here?
A CVE is not automatically exploitable everywhere. This sounds obvious when you say it out loud, but it’s the thing most tooling completely ignores.
Vulnerable code, written on a piece of paper, sitting on your desk, cannot be executed by an attacker to gain access to your computer. It has to be present on your system, part of a running service, exposed to the network, with a path to the internet. Every vulnerability has requirements like these. Think of them as a checklist of preconditions that an attacker needs to meet to succeed.
The operating system must be a specific type. The architecture must match. A particular feature must be turned on. A service must be running and reachable. A specific version of a library must be present and in use. Certain hardware must be attached. If the requirements aren’t met by our system and environment, the vulnerability cannot be exploited in our context.
That’s what Pleri does differently. For each CVE on each asset, she answers: can this be exploited in our environment? And she shows all the evidence behind the answer.
Critical on paper, not exploitable in practice
Let me walk through some examples of the kind of noise that drives alert fatigue daily.
CVE-2024-49138 is a critical Windows privilege escalation vulnerability. It gets flagged on a server. The exploit requirement is straightforward: the target must be running Windows. The asset is running Linux. Not exploitable. It shouldn’t be competing for attention and engineering time, but without context-aware evaluation, it sits in the backlog looking just as urgent as everything else.
CVE-2023-25775 is a high severity vulnerability in the Intel Ethernet Controller RDMA driver. The exploit requires RDMA-capable hardware with the Intel RDMA driver installed. The asset has standard NICs. No RDMA. Not exploitable.
CVE-2024-6387, also known as regreSSHion, is a critical remote code execution vulnerability in OpenSSH. The exploit requires sshd to be running and reachable by an attacker. On this asset, sshd isn’t running. Not exploitable.
These aren’t contrived examples. They’re the kind of findings that fill up backlogs everywhere. A CVE rated critical in a generic database, but the exploit requirements don’t match how the workload actually runs. Multiply this across thousands of findings and you start to see why teams feel like they’re drowning. They’re spending their days investigating things that were never a real threat.
When we started evaluating real customer environments this way, roughly 90% of findings turned out to be non-exploitable. Nine out of ten. The backlog was mostly fiction.
Not all exploitable findings are equally urgent
Once you know a vulnerability can be exploited, the next question is priority. How urgently do we need to act in our environment?
There’s a meaningful difference between base severity, which is a generic score like CVSS trying to describe an abstract worst-case, and operational severity, which is the score for our specific asset and environment based on real exploitability plus context.
Instead of treating every critical as equally urgent, Pleri splits work into clear lanes. Findings that are not exploitable get deprioritised with confidence. Findings that are likely not exploitable get deprioritised but not completely, because the evidence isn’t 100% conclusive. Findings that are likely exploitable go to the front of the queue: fix first, track to closure, align to SLAs. And findings where evidence is unclear or missing get flagged for manual investigation.
This is how teams get out of backlog prison without increasing risk. You’re not ignoring vulnerabilities. You’re making real decisions about them, backed by evidence, instead of triaging by gut feel or spreadsheet position.
When Pleri deprioritises a finding, she doesn’t just change a number. She shows the requirements, whether each requirement was met, and what evidence supports the call. When she escalates one, you see the same reasoning in the other direction.
That means you can answer the questions that always come up. Why did we deprioritise this? What would make this exploitable? What do we need to change to reduce exposure?
I think this is the most important thing we’ve built. Not the classification itself, but the transparency of it. If an AI tells you a critical vulnerability doesn’t matter, your next question is “why?” If the answer is a confidence score with no reasoning, you’re going to investigate manually anyway. You’ve added a step, not removed one.
Explainability makes it easier to align security and engineering teams. It makes it easier to defend decisions to auditors. And it’s the foundation for trusting the system to do more over time. The path to autonomous security starts with earned trust, not blind faith.
Drive remediation to done
Finding vulnerabilities is the easy part. Every tool does it. That’s why backlogs exist.
The hard part is always what happens next. Who owns this? What’s the fix? Has it been done? Is it within SLA? For most teams, this is a mess of manual coordination, context-switching, and things falling through cracks.
For the findings that actually matter, Pleri doesn’t stop at the verdict. She creates Jira issues or raises pull requests, aligned to SLAs. She provides clear remediation guidance that engineers can act on without a three-message Slack thread asking for context. She maps cross-asset impact, so when one fix resolves 158 vulnerabilities across 12 VMs, your team can see that and prioritise the work that moves the needle most. She verifies fixes and closes the loop. And she keeps updates flowing in Slack, Microsoft Teams, or email so nobody has to chase status.
Your backlog stops being a measure of how many things your scanner found. It becomes a measure of actual exploitable risk. That’s a number worth putting in front of your board.
Try it
If you want to see what your backlog looks like when it’s honest, sign in or sign up for a free trial to give it a try.
