Embed graph-backed AI security analysis inside your product.
Invoke one bounded Workbench capability or an approved module set through the OEM Engine. Return versioned findings, connected context, path state, remediation, retest, and evidence through your own interface, orchestration, schema, and lifecycle. You keep the product surface, brand, workflow, customer, and commercial relationship.
Start with the security question the product needs to answer.
Start with one bounded security question. Add another module only when it creates measurable product value.
Connected context
What application, workflow, authority, or control relationships matter? Map approved entities, code findings, code-risk paths, authority paths, and boundary context using Threat Canvas, Code Scanner, Tool Analyzer, Authority Graph, or Runtime Trace context and partner findings.
Normalized entities, supported relationships, code findings, code-risk paths, authority paths, boundary context, and evidence references.
Flow and validation
What happened, and can the failure be reproduced or constrained? Exercise scenarios and capture observed control state using Adversarial Range, RAG Security Testing, prompt-injection test packs, output-safety analysis, and Runtime Trace.
Scenario outcomes, observed trace references, control state, reproduction state, regression fixtures, and retest requirements.
Path and evidence analysis
Which supported conditions form a consequential path, and what control interrupts it? Attack Path Analysis, the evidence model, remediation-chokepoint analysis, and lifecycle and retest tracking determine what is defensible.
Candidate, supported, validated, reproduced, rejected, or residual paths, related candidate paths, evidence grounding and inference labels, remediation chokepoints, and lifecycle and retest state.
CAPABILITY LAYER
Partner need → selected capability group → partner-native output.
Choose the capability layer.
Partners can embed one bounded Workbench capability or combine a controlled module set without adopting the entire OEM Engine interface.
Stacked diagram showing partner-need capabilities on the left, the corresponding OEM Engine modules in the center, and the resulting partner-native capability outputs on the right.
A partner can invoke one bounded analysis capability or an approved module set without adopting the AI Security LLC interface or exposing unrelated Workbench internals.
HEADLESS INTEGRATION
One bounded request. One versioned return. Your product remains the system of engagement.
The partner sends the minimum supported input required by the selected capability. The OEM Engine returns a structured result that the partner can correlate, render, remediate, update, and retest through its own product.
From headless engine to partner-native product.
A bounded input invokes a headless Workbench capability and returns a structured object that fits the partner data model, lifecycle, and interface.
A bounded input invokes a headless Workbench capability and returns a structured object that fits the partner data model, lifecycle, and interface.
Representative finding, repository selection, trace, workflow object, configuration, artifact, or approved context.
One named module or an approved bounded capability set.
Stable identity, findings, relationships, evidence, claim state, remediation, and lifecycle state.
The partner controls presentation, correlation, prioritization, reporting, remediation, support, and customer experience.
Internal prompts, detection logic, scoring implementation, analysis internals, and unrelated Workbench capabilities remain encapsulated unless separately licensed or explicitly included in the evaluation.
Inspect the contract shape before sharing a native schema.
The public example shows the questions a product team must resolve before implementation: identity, version, selected module, input reference, result state, evidence, errors, and data handling. Exact fields remain module- and partner-specific.
{
"contract_version": "seceng.oem.request.v1",
"request_id": "req_demo_001",
"organization_id": "partner_customer_042",
"module": "code_scanner",
"input": {
"kind": "repository_selection",
"repository_ref": "repo_demo",
"revision": "commit_or_archive_ref",
"paths": ["services/agent/**"]
},
"requested_outputs": [
"findings",
"code_risk_paths",
"evidence_refs"
],
"data_policy": {
"retain_source": false,
"return_evidence_refs": true
}
}Illustrative public contract shape. Final fields, module support, transport, retention, and lifecycle behavior are agreed during pilot scoping.
Deployment, compatibility, and support must be explicit.
Deployment
Not every module supports every deployment mode.
Compatibility and updates
- engine, contract, partner profile, adapter, schema, and output versions remain independently identifiable
- supported version combinations remain explicit
- backward-compatible additions and incompatible changes remain distinguishable
- deprecation, migration, update, and rollback behavior belongs in the production support schedule
Do not imply a fixed compatibility window unless one has been commercially committed.
Support and ownership
- product UI and workflow
- customer account and billing
- first-line support
- customer-facing triage and communication
- product-specific prioritization
- native lifecycle rendering
- licensed capability defects
- canonical contract and schema defects
- selected module updates
- Workbench result integrity within the agreed scope
- integration diagnosis
- partner-profile mapping
- release coordination
- evidence-review questions
- production deployment planning
- incident and vulnerability coordination where applicable
AI Security LLC does not contact, market to, or contract around the partner’s end customers unless explicitly authorized.
- selected capability entitlement
- deployment rights
- customer or usage scope
- redistribution rights
- white-label rights where agreed
- offline rights where agreed
- update entitlement
- support tier
Prove one request and one native return before productization.
Start with one representative partner input, one selected Workbench capability, one agreed output contract, and one acceptance decision. The objective is to determine whether the capability creates material product value inside the partner workflow.
Partner supplies
- one technical owner
- one representative input
- expected native result
- expected lifecycle behavior
- approved data and processing boundary
- acceptance review
AI Security LLC supplies
- selected capability configuration
- contract and partner-profile mapping
- execution and evidence review
- structured result
- pilot readout
- production recommendation
The pilot proves
- the input can invoke the selected capability
- stable identity survives the round trip
- evidence and claim state remain explicit
- the result can be represented in the partner product
- lifecycle behavior supports remediation and retest where in scope
- deployment, ownership, support, and data boundaries are workable
- a proceed, revise, or stop decision is documented
A successful pilot is evidence for a production decision. It is not automatically a production license, compatibility guarantee, redistribution right, exclusivity grant, or customer-ready deployment.
AFTER THE PILOT
Productize only what creates measurable value.
From evaluation package to production OEM.
The path to production proves integration, evidence quality, deployment fit, entitlement, support, and a credible commercial operating model.
The path to production proves integration, evidence quality, deployment fit, entitlement, support, and a credible commercial operating model.
Expansion follows demonstrated product value, not the size of the Workbench catalog.
Embed one capability. Prove the return path. Expand only when the product value is clear.
Start with one representative object and one bounded return path. Continue only when the result improves the partner product and supports a maintainable commercial and operating boundary.