Vulnerability prioritization that fixes what matters first.
Prioritize vulnerabilities using exploit evidence, asset exposure, business criticality, and compensating controls, then explain the remediation order.
Nothing is finalized until a human approves it.
Where the time and money actually go.
CVSS-only queues overwhelm teams, asset context is incomplete, exploited issues can hide in noise, and delayed public enrichment weakens generic scanners.
Vulnerability analysts, asset owners, engineers, risk teams
Trigger to act: Backlogs are growing, KEV/SLA misses occur, scanner severity does not match risk, or an MSSP wants client-specific prioritization.
The result you can model before you sign.
Illustrative only: 50,000 findings × 80% reduction in low-value analyst review × 45 seconds × $75/hour ÷ 3,600 = $37,500 review capacity per cycle. Safety is judged by critical recall.
The outcome, plainly: Prioritize vulnerabilities using exploit evidence, asset exposure, business criticality, and compensating controls, then explain the remediation order.
Inputs in. A cited, review-ready result out. Your expert decides.
A Read-only security investigation agent. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra, selected on the client’s code/security golden set; Deterministic scanners, policy rules and tests remain authoritative; use a smaller model for labeling only. Read-only tool adapters; Sandboxed execution; Static analysis / test framework.
- Finding
- CVE/CWE
- exploit/KEV status
- asset owner/criticality
- exposure path
- software reachability
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Security engineers approve priority, exception, compensating control, and risk acceptance; the system never auto-deprioritizes a potentially critical true positive without review.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
25,000–1,000,000 security events/month; SIEM/export charges can dominate.
Planning assumptions, not vendor quotations. Your Vulnerability scanners and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Security engineers approve priority, exception, compensating control, and risk acceptance; the system never auto-deprioritizes a potentially critical true positive without review. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: No autonomous risk acceptance, patch, suppression, or closure; no offensive exploit execution; no claim that any single score equals business risk..
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $1,550–18,200/month (25,000–1,000,000 security events/month; SIEM/export charges can dominate), plus a $3,800/month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. The client needs an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Tenable?
Tools like Tenable, Qualys, Rapid7 are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it’s live, and how do we prove it works?
This is a enterprise / regulated launch. We baseline “KEV remediation SLA” first, then measure against that baseline. You see the scorecard before expanding scope, the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work