ConsultingWorkbench-backed AI security engagements — map, attack, defend, and prove your AI systems.
Scope a Review

SecEng Workbench

SecEng Map

Map.

Find every AI surface, agent, workflow, tool, retrieval path, and data exposure.

Before you can attack, defend, or prove a system, you need to know what exists. SecEng Map discovers AI model providers, SDKs, agent frameworks, tool schemas, data flows, and trust boundaries — and models them as a structured security canvas.

Capabilities

What Map instruments do.

AI vendor & runtime fingerprinting

Detect model providers, SDKs, agent frameworks, vector stores, guardrails, eval harnesses, and shadow AI across your product estate from browser snapshots and API signals.

Security data flow canvas

Draw the system as a DFD-style security canvas with external entities, processes, data stores, trust boundaries, data flows, and identified risks in a single view.

AI threat modeling

Use the canvas to model trust boundaries, identify STRIDE threats, surface AI-specific risks (prompt injection, retrieval leakage, excessive agency), and link findings to Jira and Confluence.

RAG pipeline mapping

Map the full retrieval path: query, embedding, vector store, policy check, context window, and response. Identify cross-tenant leakage and poisoning exposure before testing.

Agent authority surface

Import tool schemas, MCP server definitions, and agent workflow code. Build an authority register showing what agents can read, write, send, execute, and administer.

Attack surface register

Export a structured AI asset inventory with vendor, family, confidence, trust boundary, owner, and risk tags — for product security, governance, and audit reporting.