AI SECURITY ENGINEERING

AI security engineering for real AI products.

We map LLM, RAG, and agent systems, test realistic abuse paths, harden what matters, and produce evidence your team can use for launch and enterprise review.

Scope an AI security review View services

Research-backed AI securityRed + blue team executionThreat modeling to evidence

For product, engineering, security, and trust teams shipping AI features.

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.

Agent Blast Radius Is Unknown

Agents can read, write, browse, call tools, and trigger workflows with no clear boundary.

MAPATTACKDEFENDEVIDENCE

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.

Agent Blast Radius Is Unknown

Agents can read, write, browse, call tools, and trigger workflows with no clear boundary.

M.A.D.E.

Map · Attack · Defend · Evidence

AI security is MADE, not assumed.

Recommended path

Launch Security Review

5–10 day pre-release launch gate. First findings in 5 business days.

SecEng Code Scanner

Scan AI code for source-to-sink attack paths teams can fix.

AI Security Program Scorecard

Baseline controls, ownership, roadmap, and evidence plan.

Sales Enablement Package

Package safe AI security answers before reviews stall.

Agentic Workflow Hardening

Bound tool permissions, approvals, rollback, and logging for agents.

AI Red Teaming

Test prompt injection, RAG abuse, and tool misuse paths.

Business days to first findings

MADE pillars covered every engagement

Core AI security engagements

Delivery

Senior-led delivery, Workbench-backed testing, and engineering- and buyer-ready evidence.

What blocks real AI products

Most teams do not need another generic AI risk list. They need to know what can fail in their system, what to fix, and what evidence will survive review.

Launch with fewer unknowns

Find abuse paths, data exposure, trust-boundary gaps, and release blockers before customers or attackers do.

Fix what attackers can chain

Reproduce prompt injection, retrieval abuse, tenant leakage, tool misuse, and unsafe agent actions—then turn findings into prioritized engineering work.

Answer enterprise buyers with evidence

Replace ad hoc AI security answers with reviewed claims, clear system boundaries, and evidence your sales, security, product, and legal teams can reuse.

From system risk to usable evidence

M.A.D.E.

Map · Attack · Defend · Evidence

Our work follows one engineering loop: map the system, attack realistic paths, defend the weak points, and package the result as evidence.

MAPMap the systemUnderstand models, prompts, data flows, retrieval layers, tools, identities, permissions, and trust boundaries.

ATTACKTest realistic abuse pathsTest prompt injection, retrieval abuse, tenant leakage, tool misuse, control bypass, and unsafe autonomy.

DEFENDHarden what mattersTurn findings into scoped controls, permission changes, release checks, remediation work, and retests.

EVIDENCEMake the result usableDeliver reproducible findings, remediation priorities, control proof, and evidence for launch, customer review, and governance.

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

Slack

Microsoft TeamsSEServiceNow

Google Docs

Notion

Salesforce

HubSpotEBEvidence bundleBUBurp SuiteOWOWASP ZAP

MoodleSCSCORMCLCLI / headlessWHWebhooksJSJSONSRSARIFMDMarkdownPDPDFRTRetest checklistBEBuyer evidence

See supported outputs Scope your review

Expert-led services. Workbench-backed engineering.

AI Security LLC leads the work. Our assessments, red teams, hardening engagements, and evidence programs are delivered by security practitioners and powered by the SecEng Workbench—our technical platform for mapping systems, testing attack paths, verifying controls, and generating structured evidence.

Selected Workbench capabilities are also available through separate software licensing and OEM paths. Buying an expert-led engagement and licensing the tooling are distinct commercial options.

See the SecEng Workbench Explore licensing and OEM options

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.

Explore Code Scanner

Where are the trust boundaries?

SecEng Threat Canvas

DFD-style AI threat modeling with Jira export and Confluence evidence.

Live demo →Product page ›

Built for the systems teams are shipping now

AI product risk spans code, prompts, retrieval, identities, tools, permissions, model behavior, and the paths between them. Our work looks across those boundaries instead of treating the model as an isolated endpoint.

Agent and tool boundaries

MCP servers, OpenAPI specifications, callable tools, workflow actions, permissions, side effects, and excessive agency.

RAG and data boundaries

Retrieval flows, tenant isolation, context leakage, poisoning paths, authorization gaps, and data exposure.

AI application code

Prompt construction, unsafe execution chains, unguarded tool calls, boundary-crossing flows, and AI-specific code risk.

Structured evidence

Reproducible findings, graph-backed attack paths, engineering remediation, SARIF-compatible outputs, and evidence bundles.

Start here

Tell us what is shipping and what is blocked.

Share the system, the decision you need to make, and the timeline. We will recommend the smallest useful first step.

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

Scope an AI security review View services

Scope the first step, or browse the full engagement catalog.