Map · Discover · Baseline
SecEng Map
Map the AI system before you test the model.
Identify the architecture, data paths, identities, providers, retrieval boundaries, tools, credentials, and delegated authority that determine how the AI product can actually fail.
SecEng Map creates the system context every later security decision depends on. We trace models, applications, APIs, retrieval paths, agents, tools, identities, permissions, external providers, sensitive data, ownership, and evidence gaps, then turn that context into a reviewable security baseline for Attack, Defend, and Evidence.
Connected context
Map once. Use the context everywhere.
Mapping is not documentation for its own sake. The same system context drives Code Scanner prioritization, adversarial scenarios, Authority Graph analysis, APC grounding, defensive control design, retest conditions, and final evidence.
MAP
System + trust-boundary context
ATTACK
What can actually fail?
DEFEND
What control changes the outcome?
EVIDENCE
What can we prove afterward?
Capabilities
What Map makes visible.
System architecture
Models, applications, APIs, SDKs, gateways, vector stores, retrieval layers, MCP servers, agents, tools, browser surfaces, and external providers.
Trust boundaries
Where identities, tenants, sensitive data, model providers, retrieval contexts, tools, and external systems cross security boundaries.
RAG and data paths
Trace query, authorization, retrieval, provenance, policy checks, context assembly, model invocation, and response flow before testing for leakage or poisoning.
Agent authority
Map what agents and tools can read, write, send, execute, administer, approve, and change, including credentials, approval gates, and external effects.
Ownership and release gates
Identify who owns each system, control, exception, and release decision before a finding becomes an organizational orphan.
Evidence gaps
Expose missing diagrams, logging, ownership records, control mappings, architecture decisions, review artifacts, and buyer evidence before they become blockers.
Workbench capabilities that support Map
The context layer that feeds Attack, Defend, and Evidence.
SecEng Threat Canvas
Model the AI system as a security canvas: applications, external entities, models, RAG paths, agents, tools, data stores, trust boundaries, and data flows in one reviewable architecture.
SecEng Authority Graph
Model delegated authority across agents, identities, credentials, tools, MCP servers, approvals, and downstream systems, including dangerous permission compositions and blast radius.
SecEng Code Scanner
Find code-derived AI security paths across LLM applications, RAG, agents, MCP, tool calling, model integrations, and AI-specific trust boundaries, then carry relevant paths into Attack.
AI Security Program Baseline
Baseline ownership, control, evidence, and program-level gaps before launch or roadmap decisions.
AI Product Security Assessment
For deeper whole-product architecture and trust-boundary review.
SecEng Surface Scanner
Discover AI-native surfaces from browser, repo, and IDE signals when you need discovery data feeding Map.
SecEng Trust Scanner
Review public trust, legal, governance, security, and SDLC artifacts for coherence and evidence gaps.
Stage flow
MAP
Inventory and trust-boundary context.
ATTACK
Reproduce what actually fails.
DEFEND
Change the controls that break the path.
EVIDENCE
Preserve what was tested and proven.
MAP
Inventory & Trace
Understand architecture, data paths, trust boundaries, authority, ownership, and evidence gaps.
Open routeATTACK
Test & Validate
Reproduce AI abuse paths and determine which findings form meaningful attack chains.
Open routeDEFEND
Harden & Verify
Change architecture and controls, constrain authority, and verify the original paths no longer succeed.
Open routeEVIDENCE
Prove & Reuse
Preserve findings, attack paths, fixes, retests, control proof, and buyer-ready artifacts.
Open route