Secrets exposure detection and response that finds the real leak, fast.
Find exposed credentials across approved sources, validate safely, identify blast radius, and orchestrate owner-approved rotation and cleanup.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Secrets appear in repositories, logs, tickets, images, and collaboration tools; scanners generate duplicates while teams struggle to determine validity and ownership.
Developers, AppSec, SOC and platform teams
Trigger to act: A credential leak occurred, developer velocity increases secret sprawl, audit findings cite weak controls, or multiple scanners create unmanageable noise.
The result you can model before you sign.
Illustrative only: 1,500 secret alerts/month × 5 minutes removed × $90 loaded hourly cost ÷ 60 = $11,250 monthly capacity. Avoided breach loss is not guaranteed ROI.
The outcome, plainly: Find exposed credentials across approved sources, validate safely, identify blast radius, and orchestrate owner-approved rotation and cleanup.
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.
- Potential secret and location
- Type/provider
- Commit/history
- Owner
- Resource access
- Usage telemetry
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 and resource owners approve validation method, revocation/rotation, incident severity, history rewrite, and customer notification.
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 to 1,000,000 security events per month; SIEM/export charges can dominate.
Planning assumptions, not vendor quotations. Your source repositories 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 and resource owners approve validation method, revocation/rotation, incident severity, history rewrite, and customer notification. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no use of exposed credentials; no intrusive validation; no autonomous mass revocation that can cause outage; no secret content sent to unapproved model endpoints.
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 to $18,200 per month (25,000 to 1,000,000 security events per month; SIEM/export charges can dominate), plus a $3,800 per 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. You need an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from GitHub Secret Scanning?
Tools like GitHub Secret Scanning, GitGuardian, and TruffleHog 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 is live, and how do we prove it works?
We baseline true-secret precision 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