SecEng Workbench · Map
SecEng Threat Canvas
AI threat modeling, trust-boundary mapping & abuse-path planning.
Model the AI system as a structured security canvas before you attack, defend, or prove it. SecEng Threat Canvas maps external entities, agents, tools, retrieval paths, trust boundaries, and data flows — then turns that structure into enumerated attack paths, mapped controls, and Jira-ready findings.
Capabilities
What Threat Canvas does.
DFD-style security canvas
Draw the AI system as a data-flow diagram with external entities, processes, data stores, trust boundaries, agents, tools, and retrieval paths in one structured view.
Trust-boundary mapping
Define and annotate trust zones — user-facing surfaces, internal services, external APIs, model providers, vector stores — and show where authorization, data handling, and logging requirements change.
AI threat modeling
Apply STRIDE and AI-specific threat patterns: prompt injection paths, retrieval leakage exposure, excessive agency, model inversion risk, and supply-chain poisoning entry points.
Abuse-path planning
Enumerate plausible attack scenarios from the canvas. Each abuse path names the actor, the entry point, the data flow, the trust-boundary crossed, and the potential impact.
Control mapping
Attach controls and mitigations directly to canvas elements. Link findings to OWASP LLM, NIST AI RMF, MITRE ATLAS, and ISO 42001 at the point of discovery.
Jira & Confluence export
Push threat-model findings to Jira as structured security tasks and generate Confluence design records with risk register, control matrix, and reviewer sign-off fields.