Service 03
Forward Deployed AI Security Engineers
Your product works in the demo. We make it work in every customer’s environment.
On day one, an AI SOC or AI security tool knows nothing about your customer — their VPN ranges, service accounts, admin habits, change windows or critical systems. Our engineers work inside the customer’s environment, learn how it really runs, and teach your product the context it needs to give the right answers. Because we’ve already used and reviewed your product in depth, we start from day one, not week six.
- Your customer buys your product
- Our Forward Deployed AI Security Engineer learns their environment and tunes your product
- Verdicts the customer can trust — and fewer cancellations for you
Learn the environment · review verdicts daily · turn patterns into approved context · tune detections and playbooks · report product issues back to you.
For you
Successful deployments and happier renewals, without sending your own engineers to every customer site.
For your customer
A product that understands their environment — not a flood of alerts and a manual to decode.
Typically a 60–90 day onboarding as part of your deployment, then optional ongoing tuning as the customer’s environment changes.
The role
What a Forward Deployed AI Security Engineer does
The same idea AI companies use with forward deployed engineers — an expert from the vendor’s side who works inside the customer’s environment until the product genuinely works there. Ours are SOC analysts, detection engineers and security engineers.
- Learns the environment Network layout, identity setup, normal working hours, known noisy systems.
- Reviews verdicts every day The AI’s verdicts, and the ones the customer’s analysts confirmed or overturned.
- Turns patterns into context “This range is the corporate VPN exit” or “svc-backup reads file shares at 01:00 nightly.”
- Tunes detections and playbooks So the product does useful work, not just “less noise”.
- Reports back to you Issues that affect every customer become test cases for your team; customer-specific facts stay with that customer.
Why it needs an expert
Context with limits, not permanent whitelists.
Context an AI trusts forever is a security risk. VPN ranges change, service accounts get compromised, and an attacker who knows a range is trusted will hide behind it. So every piece of context we add is recorded, approved, scoped and reviewed.
Example context record
- Fact 203.0.113.0/24 is the corporate VPN egress
- Evidence Confirmed by the network team, ticket NET-2231
- Approved by The customer’s network lead
- Scope Suppresses impossible-travel alerts only — not every alert from this range
- Review date Every 90 days
- Still alert if The device is new, or MFA fails
Anyone can whitelist an IP range. A security engineer limits what that trust allows.
That judgement — what to trust, how far, and for how long — is what we bring to every deployment.
How an engagement works
Start with onboarding. Continue as the environment changes.
Phase 1
Onboarding & tuning · 60–90 days
Part of your deployment. Our engineer learns the customer’s environment, builds the approved context and tunes the product until verdicts hold up.
Phase 2
Ongoing tuning & verdict review
A monthly engagement. Environments, attackers and models keep changing — we keep the context current and catch drift early.
Also available
Expert operation
For tools like agentic pentest, our pentesters can run the engagement with your product on your behalf and help the customer act on the results.
Every engagement runs under a clear agreement between you, your customer and us — covering data access, confidentiality and who owns the learned context (your customer does).
FAQ
Questions about forward deployment
Will you compete with our customer success or engineering team?
No. We work for you, as an extension of your team. We take the hands-on tuning work off their plate so they can stay on the product and the relationship.
Who owns the context you build?
Your customer. It describes their environment, so it stays theirs — including if they ever change vendor. Product-wide issues come back to you as test cases.
How do you handle customer data?
Only the access agreed in writing, for the purpose agreed, under a three-way agreement between you, your customer and us.
Which products is this for?
AI SOC and SecOps platforms first, then AI insider risk, detection, CNAPP and cloud security, identity and data security tools — anything that needs to learn a customer’s environment to give the right answer.
Who pays?
Usually you, the vendor, for the onboarding period as part of deployment. Ongoing tuning can be paid by you or by the customer directly — whichever fits your commercial model.
Explore SecureFLIT
Make your AI security product good enough to stand behind.
If you’re building an AI security product and want real cybersecurity experts reviewing its output — on your side of the table — let’s talk about where to start.
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