Healthtech · Medtech & Devices · 50–200 · Europe #b032_02

We run a large retail brokerage, and our security engineers review code faster than ever, yet scanner noise buries the findings that matter. Real issues still slip through while people triage junk. A good solution must cut false positives sharply across several languages, plug into our pipeline and ticketing, add no delay to trading paths, and keep data in our region.

We process audio that often carries health and personal details, and we keep having to prove how recordings are protected. Certifications help, but buyers want ongoing evidence. A good solution must understand audio-specific risks, work in the cloud and inside customer data centres, support EU hosting, and never retain customer recordings.

We are shipping assistant features that read payroll records, and we worry a model could leak or misuse customer data. Our threat modeling is still ad hoc, feature by feature. A good solution must enforce clear limits on what a model can see and do, log every access, and come with documented threat models for model-driven features.

We wish for a shared container platform built for mature application servers, not greenfield microservices. It should let many teams adopt it gradually while customer-hosted installs keep running. We would judge it on support for older server stacks, regional data residency, public sector references, and how little each team must rewrite to ship.

We wish for a detection tool that finds and contains threats across our cloud and container infrastructure on its own, so our engineers stop hand-writing rules and chasing every alert. We would judge it by how many response actions run automatically, how few false alarms reach on-call staff, and how cleanly it plugs into our pipeline.

Serving speech and language models at national scale makes accelerator spend our biggest line item, and we can't tell which model or team drives it. A good solution must show cost per model, keep data on-site in region, fit our private cloud Kubernetes setup, and be trialable by a small team on a modest budget.

We hold sensitive spend and vendor data across many services, and we struggle to see where personal data actually lives and flows. Our privacy engineering team is new. A good solution must map data automatically, plug into our infrastructure code and APIs, scale with heavy transaction volume, and come with clear security attestations and real enterprise support.

We ship our mobile savings app updates often, yet too much release confidence still rests on manual checks and a small team stretched thin. We need broader automated coverage that runs inside our existing pipeline, handles real devices and load, and shows clear results to engineers. Quick team trial, light onboarding and transparent pricing as we scale matter most.

We serve investment firms whose security questionnaires keep growing, and every audit cycle eats far too much of our team's time. We want evidence collection and control monitoring automated, with clear audit logs, financial services grade security, and responsive support. Our budget is modest, so pricing must fit a small team.

We're a fast-growing wealth service, and our compliance team is stuck checking operational controls by hand on a schedule. Problems slip through between checks. We want continuous monitoring that flags drift as it happens, fits our existing cloud data stack, protects member financial data, and goes live in weeks, not months. Financial services experience is essential.