Find AI launch risks before buyers or attackers do.
Shipping an AI feature, copilot, RAG system, agent, or workflow soon — or stuck in enterprise security review? We find abuse paths, data-leak risks, control gaps, and graph-backed evidence fast. Our practitioners pair hands-on red-team testing with a purpose-built SecEng Workbench, so findings turn into fixes, retest evidence, and sign-off your buyers and reviewers actually trust.
Built for founders, CTOs, product security, AppSec, AI platform, sales engineering, and governance teams.
What you're facing
AI Launch Needs Urgent Review
Launch is moving faster than security testing, evidence, and release gates.
Most Scanners Don't See AI
Your tools catch code issues, but miss AI prompts, RAG, agents, and attack chains.
Dev AI Use Outpaces Controls
Dev teams are moving faster than the AI security process around them.
AI Questions Block Sales
Customers need safe answers on AI data, controls, and proof before deals move.
AI Skills Haven't Caught Up
People are being asked to secure AI faster than they are being trained for it.
Hiring AI Unicorns Is Hard
Hiring is ad hoc because the role, rubric, criteria, and outcomes are not calibrated.
Experience across
Splunk, Forescout, Devo, Cornerstone, Unum, Disney, Defence & more
Methodology
M.A.D.E.
Map · Attack · Defend · Evidence
Our work starts by mapping the AI system: models, prompts, tools, RAG paths, data boundaries, agents, logs, and workflows. Then we attack realistic paths, identify defense breakpoints, and package validated findings into engineering-ready remediation and evidence that security, product, sales, legal, and executives can actually use.
Supported outputs
Findings should not die in a PDF.
Turn AI security review work into the artifacts your teams already use: engineering tickets, GitHub issues, CI/CD evidence, Slack or Teams updates, buyer-ready summaries, remediation checklists, and retest evidence.
From finding → fix → retest → evidence, the work is packaged so security, product, engineering, sales, and governance teams can act without translating another generic report.
Jira
GitHub
GitHub Actions
Azure DevOps
SlackMicrosoft TeamsSEServiceNow
Google Docs
Notion
Salesforce
HubSpotEBEvidence bundleBUBurp SuiteOWOWASP ZAP
MoodleSCSCORMCLCLI / headlessWHWebhooksJSJSONSRSARIFMDMarkdownPDPDFRTRetest checklistBEBuyer evidenceShipping AI in the next 30–60 days?
Before launch, know whether your copilot, RAG system, agent, or AI workflow can leak data, follow hostile instructions, misuse tools, bypass approvals, or create evidence gaps your buyers will find first.
Offer
AI Launch Security Review
Timeline
First findings in 5 business days. Launch-ready review in 5–10 business days.
Outputs
- Launch Risk Memo
- Abuse-Path Findings
- Release Gate Checklist
- Sprint-Ready Fix Backlog
- Buyer-Ready Evidence Summary
Start here
Start with the thing blocking progress.
Launching an AI feature? Stuck in enterprise security review? Unsure whether your agent, RAG system, scanner output, or team readiness will hold up? Pick the problem. We scope the work, test the risk, and turn the result into fixes, defense breakpoints, and graph-backed evidence your team can use.
AI Launch Security Review
We are launching an AI feature, copilot, RAG system, agent, or workflow soon and need launch-risk clarity fast.
- For:
- Founder, CTO, VP Product, Head of Engineering, Product Security, AppSec owner
- Result:
- First findings in 5 business days. Launch-ready review in 5–10 business days.
- You'll get:
- You'll get a Launch Risk Memo and a go/no-go release gate.
AI Security Sales Enablement
Enterprise buyers are asking AI security questions we cannot answer cleanly, and the deal/security review is slowing down.
- For:
- Founder, CEO, Sales Engineer, Head of Sales, Customer Trust, Security Assurance, GRC
- Result:
- First evidence-gap readout in 5 business days. Buyer-ready pack in 5–10 business days where scope allows.
- You'll get:
- You'll get a buyer-ready evidence summary and an answer bank.
Agentic Workflow Security & Hardening
Agents, tools, credentials, workflows, approvals, and actions have unclear blast radius.
- For:
- AI platform lead, engineering manager, security engineer, automation owner, product owner
- Result:
- First authority map and abuse-path readout in 5 business days. Hardened review plan in 5–10 business days.
- You'll get:
- You'll get a tool permission matrix and an agent authority graph.
No-Cost Scoping Retainer
We may want to move, but vendor onboarding, NDA, finance, SOW, procurement, security review, and internal justification can stall everything.
- For:
- Champion who needs legal, finance, procurement, security, and product aligned before paid work can start.
- Result:
- No-cost scoping packet immediately. Draft review plan after intake. Paid SOW/private offer after scope is clear.
- You'll get:
- You'll get an NDA, a procurement packet, and an internal approval memo.
SecEng Code Scanner OEM Pilot
Your scanner covers web, APIs, and infrastructure. It doesn't cover AI-generated code, LLM apps, or agentic workflows — and customers are starting to ask.
- For:
- Scanner vendor founder, product owner, CTO, head of AppSec product, commercial/partnerships lead
- Result:
- Feasibility Sprint: 2 weeks. 30-Day OEM Pilot: 30 days. White-Label Productization: 8–12 weeks.
- You'll get:
- You'll get a working invocation plan, JSON/SARIF/Markdown output examples, AppCheck-style report mapping, and annual license terms.
Role Readiness / Platform Partner Add-On
Training platforms prove skills but can't answer 'which AI security role is this person ready for' or 'how should our enterprise customers hire for AI security' — leaving practitioners without career direction and corporate buyers without workforce evidence.
- For:
- VP Product, BD lead, or partnerships director at a cybersecurity training platform, cyber range, certification provider, or enterprise L&D company
- Result:
- Pilot validation: 2 weeks. Platform Integration: 4–8 weeks. Strategic License: by negotiation.
- You'll get:
- You'll get a role taxonomy sample, Q&A bank preview, integration architecture spec, and commercial terms.
Pen Test & Red Team Readiness Packet
We want to commission a pen test or red team but don't have the scope definition, authorization documents, ROE, evidence handling plan, or vendor criteria in place yet.
- For:
- Offensive security lead, red team coordinator, AppSec manager, CISO office scoping an external pen test or red team engagement
- Result:
- Readiness packet delivery: 5–10 business days. Engagement-ready authorization: after your legal and technical owners sign off.
- You'll get:
- You'll get a scoped ROE, authorization pack, evidence handling policy, and vendor selection criteria.
AI Security Academy
We need structured AI security training for our teams but have no budget for a custom curriculum build, and off-the-shelf compliance training doesn't cover LLMs, agents, RAG, or AI product security.
- For:
- L&D lead, CISO, security training program manager, HR/enablement director, or team lead at an organization with 50+ security, engineering, product, or governance staff
- Result:
- Team access live within 1–3 business days. LMS package delivery: 2–4 weeks. Private cohort: scheduled by agreement.
- You'll get:
- You'll get course access, a team training plan, manager reports, and an LMS package (by scope).
Why AI product security is different
AI security is not just model risk, AppSec, governance, or compliance with new labels. Real AI products cross prompts, files, users, tools, APIs, retrieval layers, model providers, logs, and business workflows. Every boundary becomes a security question: who can instruct it, what can it access, what can it change, what can it leak, and what evidence proves the controls work?
Data Boundaries
RAG boundaries, tenant isolation, sensitive data exposure, context leakage, identity propagation, and policy enforcement at retrieval time.
Hostile Instructions & Inputs
Prompt injection, jailbreaks, malicious documents, poisoned retrieval, indirect instructions, and user-controlled context at every layer.
Guardrails, Gates & Controls
Release gates, guardrails, eval suites, approval boundaries, tool permissions, policy enforcement, and rollback paths that keep AI operating within intended scope.
Evidence, Audit & Review Readiness
Security questionnaires, trust reviews, release gates, and audit requests need evidence your team can stand behind.
Services
Focused AI security engagements for real product risk.
Start with a launch review, product assessment, red team, agent hardening review, governance buildout, or buyer-evidence package. Each engagement is scoped around the system, the risk, the decision, and the evidence your team needs next.
Research & labs
Research behind the practice
Practical research, field guides, control mappings, and local-first assessment tools — research-backed, tool-backed, and field-tested across real AI product security programs.
SecEng Workbench
SecEng Workbench turns testing into fixes and evidence.
Workbench-supported delivery connects system mapping, code-derived attack paths, adversarial testing, defense breakpoints, validation records, and buyer-ready exports. The point is not another report. The point is a usable path from risk to remediation.
Where can AI code become an attack path?
SecEng Code Scanner
Graph-backed AI SAST for MCP, RAG, browser-agent, and tool-calling code. Groups source/sink signals into attack paths, validation plans, SARIF, remediation evidence, and buyer-ready proof.
Where are the trust boundaries?
SecEng Threat Canvas
DFD-style AI threat modeling with Jira export and Confluence evidence.
Public Work
Work across product security, AI systems, research, and enterprise engineering
The same methodology applies across product security programs, AI systems, governance work, detection engineering, and applied security tooling. These public-safe examples show the range behind the current AI security practice.
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Start here
Bring the AI system. We'll find the next step.
Bring an AI product, agent workflow, RAG system, model gateway, security review, launch risk, or governance gap. We'll scope the first useful step, test what matters, and package the results into engineering-ready fixes and buyer-ready evidence.
- Map the AI system: models, tools, RAG paths, agents, data flows
- Test abuse paths and harden the controls that matter
- Produce engineering-ready fixes and release-ready evidence
- Scoped first step before more work