AI Threat Modeling & Trust-Boundary Mapping
Map AI trust boundaries into a security decision canvas.
DFD-style AI threat modeling with trust-boundary mapping, abuse-path planning, and Jira/Confluence export. Model the system before you attack, defend, or evidence it.
DFD-style security canvas
Model AI systems as data-flow diagrams with trust boundaries, components, and data flows.
Trust-boundary mapping
Identify and annotate trust boundaries across AI pipelines, agents, and retrieval systems.
AI threat modeling
Enumerate AI-specific threats — prompt injection, data leakage, model manipulation, and supply chain risk.
Abuse-path planning
Trace attacker-controlled paths from entry through the system to impact.
Control mapping
Map threats to OWASP LLM, NIST AI RMF, and MITRE ATLAS controls.
Jira & Confluence export
Export threat models and remediation tasks directly to Jira tickets and Confluence pages.
Core capabilities
What SecEng 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.
SECENG WORKBENCH
Ready to put SecEng Threat Canvas to work?
Scope a Workbench-backed review — we'll map the AI surfaces, identify the highest-priority gaps, and give you clear findings before any larger commitment.
Also in the Workbench
WHAT AI DO WE HAVE?
SecEng Surface Scanner
Browser, repo & IDE discovery for AI assets, vendors, and risky patterns.
WHERE CAN AI CODE BECOME AN ATTACK PATH?
SecEng Code Scanner
AI-native SAST and marketplace readiness for AI-enabled apps, agents, integrations, and managed packages.
WHAT DID IT ACTUALLY DO?
SecEng Runtime Proxy
MITM capture, replay & runtime evidence reconstruction.
HOW CAN IT FAIL UNDER ATTACK?
SecEng Adversarial Range
Scenario-driven AI red-team testing for prompts, agents, tools, RAG, and multimodal systems.
WHAT CAN AGENTS ACTUALLY DO?
SecEng Authority Graph
Agent authority, tool permissions, approval paths & delegated-action risk.
WAS RETRIEVAL AUTHORIZED?
SecEng RAG Test Harness
Test retrieval security & context authorization.
WHAT DO OUR PUBLIC AI CLAIMS REVEAL?
SecEng Trust Scanner
Public trust surface scoring across six AI governance dimensions.
WHERE DO TRUST BOUNDARIES LIVE IN JIRA?
Atlassian Threat Canvas
AI threat models that ship to Jira and Confluence.
DO YOUR AGENTS HAVE TOO MUCH PERMISSION?
SecEng Agent Permission Analyzer
Deterministic permission security analysis for AI agent tool configs.
WHAT'S INSIDE YOUR AI ARTIFACTS?
SecEng Artifact Analyzer
Static artifact intelligence for AI security and evidence packaging.
HOW RESILIENT IS YOUR SYSTEM TO INJECTION?
SecEng Injection Harness
Structured prompt injection probes with evidence session export.
ARE YOUR PROMPTS SECURE?
SecEng Prompt Reviewer
Deterministic rule-based scanner for system prompts and RAG corpus documents.
WHO CONTROLS WHAT MODELS CAN DO?
SecEng Model Gateway
Governed AI routing, policy enforcement, and spend control.
WHAT DOES YOUR AI SECURITY PROGRAM LOOK LIKE?
SecEng Program Blueprint Kit
Complete AI security program structure for Jira, Confluence, and Linear.
IS YOUR MODEL OUTPUT SAFE TO RENDER?
SecEng Output Safety Tester
Deterministic AI output safety analysis across 8 sink types.
WHERE DOES YOUR PROGRAM STAND?
AI Security Program Scorecard
14-domain AI product security baseline with evidence pack generation.
WHAT CAN YOUR AI TOOLS REALLY DO?
SecEng Tool Capsule Analyzer
Analyze MCP servers, OpenAPI specifications, and AI tool definitions to understand capabilities, permissions, and attack surface.
WHERE ARE YOUR PRODUCTION PROMPTS?
SecEng Prompt Asset Scanner
Inventory and review system prompts, developer prompts, agent instructions, and prompt templates for security risks.
WHAT CAN YOUR AGENTS ACTUALLY DO?
SecEng Agent Authority Diff
Compare declared permissions with observed capabilities to identify excessive agent privileges and unsafe tool access.
WHICH AI DEPENDENCIES CHANGE RELEASE RISK?
SecEng Supply Chain Scanner
Identify AI-specific dependency, model loader, framework, and supply-chain security risks.
CAN YOU PROVE WHAT YOUR EVALS COVER?
SecEng Eval Coverage Auditor
Measure whether AI security evaluations adequately cover prompt injection, tool abuse, RAG, memory, and other critical attack classes.
ARE YOUR AI CONFIGS SAFE TO DEPLOY?
SecEng AI Config Linter
Identify AI-specific dependency, model loader, framework, and supply-chain security risks.
CAN YOU PROVE WHAT YOU'VE DONE?
SecEng Evidence Packs
Buyer-ready evidence artifacts from AI security assessment and testing.