Security Data Flow Canvas for Jira + Confluence
Turn AI architecture into Jira-ready threat models and Confluence evidence.
Turn AI and product architecture into structured threat models, Jira-ready remediation, and Confluence-ready security design evidence. Security Data Flow Canvas built for product security teams.
AI product launch review
Structure security review of AI feature launches with evidence that meets product security gates.
RAG application threat model
Model retrieval pipelines, knowledge bases, and context boundaries with abuse paths and controls.
SaaS architecture review
Threat model SaaS AI integrations, vendor connections, and data flows across trust boundaries.
Secure design review before release
Design-level security reviews that ship as Jira tasks and Confluence evidence.
SOC 2 / ISO 27001 / ISO 42001 evidence
Export threat models and control mappings as structured evidence for compliance audits.
Security Data Flow Canvas
AI System Threat Model
User Browser
External Entity
LLM Gateway
AI Process
RAG Pipeline
AI Process
Vector Store
Data Store
Tool Executor
Process
Audit Log
Data Store
Trust boundary: AI inference layer
Identified risks
Prompt injection via user input
HIGHRetrieval context leakage
MEDExcessive agent permissions
HIGHAudit log tampering
LOW4 risks · 2 controls mapped · Jira-ready
Core capabilities
What Atlassian Threat Canvas does.
Security Data Flow Canvas
DFD-style notation with external entities, processes, data stores, trust zones, flows, and risks on a single canvas.
STRIDE-compatible risk discovery
Structured threat identification across spoofing, tampering, repudiation, disclosure, DoS, and elevation of privilege.
AI/RAG/agent risk templates
Pre-built templates for LLM gateways, RAG pipelines, tool calls, agent actions, memory, and model provider boundaries.
Jira issue generation
Create Jira issues from canvas risks with component context, severity, ownership links, and remediation instructions.
Confluence evidence reports
Auto-generate structured security design records: threats, controls, evidence, and approvals in readable format.
Trust boundary mapping
Visualize system perimeters, data classification boundaries, privilege zones, and external integration edges.
The problem
Threat models should not die in workshop notes.
Threat modeling is too often trapped in workshops, whiteboards, and stale diagrams. Engineering teams need clear Jira work. Security teams need review evidence. Leaders need a readable risk picture.
Diagrams go stale
Architecture diagrams do not keep pace with real system changes. Threat models built from them become unreliable within weeks.
Tickets lose context
Security action items divorced from the model lose the architectural context that makes remediation decisions tractable.
Evidence gaps at audit
Compliance and audit reviews need documented design rationale. Workshop notes and meeting decks do not fill that gap.
AI systems make it harder
Modern AI products add flows and failure modes that traditional architecture diagrams do not capture: prompt construction, retrieved context, model provider boundaries, tool calls, agent actions, memory reads, and side-effect outputs.
- Prompt injection via user and retrieved content
- Retrieval context leakage across tenants
- Excessive agency from tool-enabled agents
- Unsafe output handling and data exfiltration
- Model provider trust boundary violations
- Audit log gaps and side-effect traceability
Outcomes
From architecture to action.
Model the system
Sketch your architecture as a security data flow canvas: external entities, processes, data stores, trust boundaries, and flows.
Identify risks
STRIDE-compatible threat discovery across every flow and boundary. AI systems get first-class templates for prompt injection, retrieval leakage, and excessive agency.
Create Jira work
Convert risks into structured Jira issues with context, severity, ownership, and remediation guidance — not vague risk language.
Publish evidence
Export a Confluence-ready security design record with controls, evidence, risk summary, and reviewer sign-off.
Atlassian-native
Built for the tools your teams already use.
Threat models that live inside Jira and Confluence stay connected to engineering work. Risks become tickets. Decisions become design records. Evidence is always traceable to the model that produced it.
Confluence
- Security design record page
- Threat model snapshot and summary
- Control and evidence tables
- Architecture reviewer sign-off
- Living document — updates with the model
Jira
- Risk-to-ticket with full context
- Severity, component, and owner fields
- Linked back to the canvas model
- Remediation guidance in description
- Tracks to sprint and release
Who benefits
Security teams
Auditable evidence of design review, control coverage, and risk disposition.
Engineers
Clear Jira tickets with architectural context and remediation guidance — not abstract risk language.
Engineering managers
A prioritized security backlog that connects to sprints and release gates.
Leaders and auditors
An executive risk summary and a structured record of threat modeling decisions.
Compliance teams
Design-time evidence for SOC 2, ISO 27001, and ISO 42001 controls without a heavyweight GRC system.
AI systems
Threat modeling for RAG, agents, and AI-enabled products.
AI-specific risk surfaces
AI systems introduce new flows and failure modes that normal architecture diagrams do not capture. The canvas has first-class support for every layer of the modern AI stack.
Prompt construction layer
Prompt injection, template injection, context manipulation
Retrieval / RAG pipeline
Context leakage, cross-tenant retrieval, poisoning
LLM gateway and model providers
Trust boundary violations, model substitution
Tool calls and agent actions
Excessive agency, unsafe tool permissions
Memory and session state
State poisoning, session fixation, replay attacks
Output handling and side effects
Data exfiltration, unsafe code execution, SSRF
Audit and observability layer
Log tampering, traceability gaps, evidence loss
The canvas treats AI systems as first-class threat model subjects. Pre-built templates surface the risk taxonomy for LLM applications, RAG pipelines, and agent-enabled products — so teams do not start from a blank STRIDE spreadsheet.
AI template
LLM gateway review
Map prompt flows, system prompt exposure, user trust levels, and output routing. Identify prompt injection and model substitution risks.
AI template
RAG pipeline model
Canvas the retrieval path: query, embedding, vector store, context window, and response. Surface leakage and poisoning exposure.
AI template
Agent threat model
Model agent tool permissions, action chains, memory reads, and side effects. Identify excessive agency and unsafe tool use.
AI template
AI product launch review
Full pre-launch threat model with Jira remediation backlog and Confluence evidence record. Evidence of security review before ship.
Framework alignment
AI risk templates align with OWASP LLM Top 10, MITRE ATLAS AI adversarial techniques, and NIST AI RMF governance controls — so evidence produced connects to the frameworks your buyers and auditors already reference.
Features
Everything the canvas needs to produce evidence.
Security Data Flow Canvas
DFD-style notation with external entities, processes, data stores, trust zones, data flows, and risks on a single canvas.
STRIDE-compatible risk discovery
Structured threat identification across spoofing, tampering, repudiation, information disclosure, DoS, and elevation of privilege.
Trust boundary mapping
Visualize and reason about system perimeters, data classification boundaries, privilege zones, and external integration edges.
AI/RAG/agent risk templates
Pre-built threat templates for LLM gateways, RAG pipelines, tool calls, agent actions, memory, and model provider boundaries.
Jira issue generation
Create Jira issues directly from the canvas with component context, risk severity, ownership links, and remediation instructions.
Confluence evidence reports
Auto-generate structured security design records for Confluence: threats, controls, evidence, and approvals in readable format.
Controls and evidence tracking
Link mitigations and controls to specific risks and flows. Track evidence status as design decisions and testing results land.
Executive risk summaries
Produce a concise risk posture summary for leadership: threat count, severity breakdown, control coverage, and open items.
Workflow
Threat modeling in one flow.
Import or sketch architecture
Start from an existing Confluence diagram, an architecture description, or draw the system from scratch on the canvas.
Mark trust boundaries and data classes
Define perimeters, privilege zones, and data sensitivity across every flow and storage layer.
Generate or review threats
Use STRIDE templates and AI-assisted threat generation to identify risks, or walk through the model manually.
Map controls and evidence
Attach existing controls, record gaps, and link to test results or audit evidence directly on the canvas.
Create Jira issues
Convert open risks into Jira tickets with full context: component, flow, severity, owner, and recommended remediation.
Publish the Confluence design record
Export a structured security design document to Confluence. Living models update as the architecture evolves.
Differentiation
Not another diagram. Not another spreadsheet.
Most threat modeling tools produce artifacts that stop at the edge of the security team. This product creates a living model that drives engineering work, captures evidence, and connects to the systems teams already use.
vs. Diagramming tools
Diagrams do not drive remediation. There is no risk registry, no Jira integration, and no evidence output. They produce pictures, not work.
vs. GRC platforms
GRC systems are too heavy for design-time product security. They track compliance programs, not architecture-level threat models.
vs. AI chatbots
A chatbot generates threat ideas without durable context. There is no canvas, no trust boundary model, no Jira output, and no evidence record.
vs. Spreadsheets
Spreadsheets do not connect risks to architecture. They go stale immediately and produce no Jira work or Confluence evidence.
What makes it different
Structured security data-flow canvas — not a whiteboard, not a chat window.
Risks are linked to specific components, flows, and trust boundaries in the model.
Jira issues carry full canvas context: component, flow, severity, and remediation.
Confluence records are generated from the model, not written from scratch.
Evidence is always traceable back to the threat model that produced it.
Living models update as architecture evolves — not a one-time snapshot.
Use cases
Where security teams deploy it.
Use case
AI product launch review
Threat-model a new AI-enabled feature before launch. Identify prompt injection, data exposure, and model provider risks early.
Use case
RAG application threat model
Map retrieval pipelines, vector stores, embedding models, and output handlers. Surface retrieval leakage and context injection risks.
Use case
SaaS architecture review
Review multi-tenant SaaS systems for trust boundary violations, privilege escalation paths, and data flow risks.
Use case
Secure design review before release
Run a structured pre-release design review. Produce evidence that risks were identified and mitigated before ship.
Use case
SOC 2 / ISO 27001 / ISO 42001 evidence
Generate security design records and control evidence that support audit requirements without a heavyweight GRC system.
Use case
Product security backlog generation
Build a prioritized Jira backlog of security remediation work directly from the threat model — not from a spreadsheet.
Early access
Bring threat modeling into the engineering workflow.
What early access includes
Threat Models That Ship.
Join the early access list to be first to run a structured AI threat model for your system — with Jira-ready remediation work and a Confluence security design record.
Platform delivery
Web application
Hosted canvas available now for standalone use — no Atlassian account required.
Open canvas demoConfluence macro
Planned Confluence packaging would place risks and evidence next to architecture docs. Current access is direct scoping, not a live marketplace listing.
View marketplace ecosystemJira integration
Create and track Jira issues directly from the canvas model. Risks become sprint-ready security work.
Start No-Cost ScopingSECENG WORKBENCH
Ready to put Atlassian 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.
WHERE ARE THE TRUST BOUNDARIES?
SecEng Threat Canvas
Structured AI threat modeling, trust-boundary mapping, and abuse-path planning.
WHAT DO OUR PUBLIC AI CLAIMS REVEAL?
SecEng Trust Scanner
Public trust surface scoring across six AI governance dimensions.
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.