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TRUSTELIS AI Control Architecture

From use case assessment to runtime enforcement, every AI action is governed before it acts.

AI Governance Lifecycle
▣
Use Case
☷
Assessment
◇
Risk & Readiness
♙
Approval Journey
▤
Policy Rules
⬡
Runtime Gateway
✦
AI Execution
✓
Controlled Outcome
2. TRUSTELIS Control Plane
Control Plane
⌕ Assessment Engine Evaluates intent, data, context, and capability.
◇ Risk Classification Scores risk across security, compliance, and impact.
☉ Governance Approval Journey Routes use cases through policy-defined workflows.
▤ Policy Rules Engine Policy-as-code defining allow, deny, restrict, require, and guardrails.
Runtime Gateway Runtime Control Single control point enforcing policy, approvals, and risk decisions before any AI action proceeds.
✓ Human Approval Gates Requires human sign-off for high-risk or sensitive actions.
🔒 Runtime Enforcement Blocks, allows, masks, delays, or routes actions.
▤ Trust Pack & Evidence Packages decisions, context, evidence, and outcomes for audit.
3. AI Execution Layer
◉
RAG / Knowledge Base Enterprise knowledge, vector DBs, documents, and data sources.
☁
LLM Providers OpenAI, Claude, Gemini, Microsoft, Azure OpenAI, and on-premise models.
✦
AI Agent Reasoning, planning, tool use, and orchestration.
⚙
MCP Tools & Automation MCP servers, functions, scripts, and automations.
▥
Business System Actions CRM, ERP, ITSM, HRIS, finance, production systems, APIs.
4. Evidence & Governance Feedback
▤
Runtime Logs Every request, decision, and enforcement event.
↻
Approval History Who approved what, when, and under what conditions.
♙
Policy Decisions Record of policy evaluations and rule outcomes.
◇
Risk Records Risk scores, factors, and reassessments over time.
▤
Trust Pack Evidence bundles for each AI action.
⌕
Audit Trail End-to-end traceability for compliance and audit.
Primary Flow
Governance Feedback
Execution Flow
◇Policy Enforcement
🔒Access Control
♙Human Oversight
◉Data & Knowledge
⚙System Actions
TRUSTELIS AI
Control Architecture

From use case assessment to runtime enforcement, every AI action is governed before it acts.

01Use Case IntakeBusiness need
02Control PlaneDecision control
03AI ExecutionAction layer

AI Governance Lifecycle

01
Use CaseBusiness need and intended AI action.
02
AssessmentIntent, data, context, capability.
03
Risk & ReadinessSecurity, compliance, business impact.
04
Approval JourneyRouting and human sign-off.
05
Policy RulesAllow, deny, restrict, require.
06
Runtime GatewayControl point before actions.
07
AI ExecutionRAG, LLM, Agent, MCP, systems.
✓
Controlled OutcomeApproved, blocked, logged, escalated.

1. Use Case Intake

☉
Business Use CasesHR, finance, triage, operations.
👥
Users & TeamsRoles, permissions, context.
▦
Business AppsCRM, ERP, HRIS, ITSM.
◉
Data & KnowledgeDocuments, DBs, files, RAG.
⚙
APIs & ActionsInternal and external services.
▥
OT SystemsPLCs, SCADA, DCS, Historians.
⌁
IoT Devices / SensorsEdge devices, telemetry, gateways connected into controlled AI flows.

2. TRUSTELIS Control Plane

⌕
Assessment EngineIntent, data, context, capability.
◇
Risk ClassificationSecurity, compliance, impact.
Runtime Gateway Runtime Control Single control point enforcing policy, approvals, and risk decisions before any AI action proceeds.
☉
Approval JourneyPolicy-defined workflows.
✓
Human Approval GatesSign-off for sensitive actions.
▤
Policy Rules EngineAllow, deny, restrict, require.
🔒
Runtime EnforcementBlocks, masks, delays, routes.
▤
Trust Pack & EvidencePackages decisions, context, evidence, approvals, and outcomes for audit.

3. AI Execution Layer

◉
RAG / Knowledge BaseEnterprise knowledge and vector DBs.
☁
LLM ProvidersOpenAI, Claude, Gemini, Azure.
✦
AI AgentReasoning, planning, orchestration.
⚙
MCP ToolsFunctions, tools, automations.
▥
Business System ActionsCRM, ERP, ITSM, HRIS, finance, production systems, and APIs.

4. Evidence & Governance Feedback