We want security issues caught while code is still in a pull request, not after release. Today our product security engineers chase findings late, and outside tests surface things we could have fixed earlier. A good solution must scan changes fast, keep false positives low so developers trust it, and fit our existing pipeline without slowing merges.
DevTools · B2B SaaS Apps · 200–1000 · Europe #b024_16
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We sell a cloud phone and call-center system that plugs into business tools. Sales and support teams rely on us for every customer call.
- Industry
- DevTools & Software · B2B SaaS Apps
- Size
- 200–1000 employees
- Region
- Europe
- Tools budget
- $5K–20K per month
- Needs
- Threat detection & response. Voice fraud and account takeover attempts arrive at all hours, and our response team needs faster triage.Today: a growing detection and response group tunes alerts, with incident response engineers being added · Urgency: this yearApplication security & code scanning. We want security findings caught in pull requests rather than after release.Today: product security engineers work across the lifecycle, with outside tests and a bounty scheme · Urgency: this yearGuardrails for AI agents and LLM apps. Call transcripts go to language models, and redaction must be reliable before anything leaves our boundary.Today: call transcripts scrubbed of sensitive content before model use, voice agents rolling out · Urgency: now
- Who decides
- ciso security · security review
- Expects from suppliers
- Detection for telephony fraud patterns; Pull request scanning with low false positives; Redaction that works on noisy transcripts
- Not a fit
- Tools that cannot handle real-time voice data; Vendors without a European presence
- Stack
- cloud: AWS-class hyperscaler, LLM APIs in production, CI/CD pipeline in place
- Buying triggers
- security incident risk, AI adoption push
Needs
Cooperation
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Our managed defence analysts watch alerts from many separate client environments, and the same incident often shows up as unrelated noise across tools. Triage eats most of the shift. A good solution must correlate signals across tenants without ever mixing client data, keep data hosted locally, and explain why alerts were grouped so analysts can trust it.
We sit in front of consumer sites, and language-model agents now arrive in growing waves. Some are legitimate shopping assistants, others are impostors scraping or abusing checkout flows. We need dependable signals that tell them apart without blocking real customers. A good solution must add negligible latency, hold up under retail peaks, and explain each decision clearly.
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.
Findings from outside testers and our bounty programme arrive faster than we can sort them. Each one needs a clear owner, a severity call and a ticket in our tracker, and today that routing is mostly manual. A good solution deduplicates reports, assigns owners automatically, tracks fix deadlines, and shows evidence we can reuse in audits.
Our team works from many locations, and giving people access when they join and removing it when they leave is still manual. A handful of staff handle it through a shared directory with single sign-on. We need something simple for distributed teams, that plugs into standard single sign-on, and works without office networks or heavy seat minimums.
We are letting AI agents launch simulation jobs on our platform, and that worries us. We need hard autonomy limits, not guidelines, plus action logs our auditors will accept as evidence. A good solution must run entirely inside our authorised boundary, never move data out, and fit federal assessment expectations. Who has handled this under strict data rules?
We run our platform across two major public clouds and struggle to see misconfigurations in one place. Policies drift between environments, and fixes happen twice. A good solution must give a single unified posture view, enforce consistent policies across both clouds, cover container workloads, and flag risks that matter to a security review. Single-cloud-only tools are no use to us.
We run a small security team covering everything from governance to incident response, so we can't staff around the clock watching alerts. We need threat detection that triages and correlates automatically, surfaces only what matters, and plugs into our cloud and ticketing. Healthcare references and a security review-friendly deployment are musts. Budget is modest.
We wish for one tool that pulls alerts from endpoints, identity, email and cloud into a single view, then helps our engineers write and test detections instead of hand-building every rule. We would judge it by how much manual triage disappears, whether we keep full access to detection logic, and how it handles highly sensitive research data.
We wish we had a tool giving our infrastructure security team continuous, plain-language visibility into cloud misconfigurations across every region, ranked by real risk. We would judge it on coverage depth for our hyperscaler, enterprise access controls, low alert noise, and mapping findings to our compliance controls. Pricing must fit a modest annual budget.