Which of these hundreds do I actually fix?
Your security tools raise hundreds of findings. Most are not exploitable in your environment, but someone still has to prove that, one by one. Trent finds by itself the vulnerabilities that matter, and works with the security tools your organization already relies on, to judge every finding against how your system actually runs. You are shown the few vulnerabilities an attacker could use, with the evidence.
Approach
Like a security engineer, Trent works from its own findings first and uses whatever tools are available on top.
It starts from a live, security-graded map of your system, the services, agents, trust boundaries, and data flows, so every judgment is made against what is actually exposed. It reads your code, configs, and infrastructure, follows each finding through the code path, the network, and the identities involved, and sorts it into one of three groups. A high-priority vulnerability that needs fixing now, with the exploit path and the fix. A false positive, with the reason written down. Or a chain, a finding that only exists when two signals are read together, which no single tool can see. If you connect the security tools you already run, their findings go through the same judgment, and every run shows each tool's claim next to Trent's verdict, the reason, and the source line it rests on.
Why us
Nothing you already run gets replaced, and nothing you have paid for is wasted.
Where a CVSS score treats every instance of a flaw the same, Trent sees and understands the difference between a critical issue behind compensating controls and a lower-severity one open to the internet, and ranks them accordingly, drawing on a knowledge base of vulnerabilities and exploit techniques that it keeps up to date. It also learns your team's decisions: rerate a finding and say why, and Trent applies that reasoning to every similar case from then on.
What you get
Trent takes the triage off your engineers, which gives you back roughly a third of every security engineer and more value from the tools you already pay for.
Security teams spend an average of 14 hours a week chasing false positives. You get the few findings worth fixing first, a written reason for every one set aside, and counts that reconcile with your own security tools.
Customer example
An AI-native SaaS company had five security tools, about 400 open findings, and one question: which few vulnerabilities can actually be exploited?
In its first pass across their codebase, Trent rejected a flagged "hard-coded credential" in docker-compose.yml, because the values were placeholders for a local mock. It rejected a flagged "SQL injection" in reporting.py, because the value was a constant no request reaches, and kept the same rule's hit on a genuine injection elsewhere, where user input does reach the query. Then came the finding that mattered: a live API key, sitting in a ticket export someone had checked into the repository. The secrets scanner had flagged it among hundreds of alerts, but Trent made it a real finding, with its impact, and the fix to rotate and remove the key.