Customer data and source repositories
Security & Governance
Make every AI outcome accountable.
T-Flux brings identity, source permissions, model discipline and evidence-led oversight together in one operating model. Security is not an afterthought; it is part of how the platform is designed to operate.
From data and models to people, records and controls, each part of the AI lifecycle can be governed, reviewed and aligned with enterprise requirements.
Security posture
What the platform protects
Model endpoints, registries, and orchestration policies
User identities, roles, and administrative privileges
Prompt, response, and workflow execution records
Audit evidence, exported reports, and governance artefacts
Tenant separation across hosted or customer-controlled environments
Architecture objective
Security by design
T-Flux applies a private-by-default architecture. The platform is intended to run either inside the customer environment or in a tightly controlled Solveworx-managed hosted deployment, with clear logical separation between tenants, governed access to data sources, and policy controls around model interaction.
Security architecture layers
The security model should be understood as a set of coordinated control layers rather than a single perimeter control.
Deployment layer
Supports Solveworx-hosted, private-cloud, or on-premises deployment. The design favours customer control over infrastructure placement, network boundaries, and data locality.
Identity layer
Users authenticate through controlled enterprise identity paths with MFA and optional SSO. Privileged actions are restricted to authorised administrative roles.
Data governance layer
Connector access, source ingestion, document visibility, and retrieval are governed by role and attribute-based rules, trust labels, and data lifecycle settings.
Response control layer
Model orchestration, guardrails, policy checks, citation requirements, and evidence trails help ensure outputs remain explainable and controllable.
Core security controls
These controls should be described on the page as the minimum architecture expected for enterprise deployment.
Private deployment and tenant isolation
- Customer private cloud, on-prem, or governed hosted deployment
- Logical segregation of enterprise tenants and workspaces
- Controlled model registry and isolated enterprise configuration
- Separation of customer data, customer SLMs, and workflow artefacts
Identity, access, and administrative control
- MFA for administrator and privileged user access
- Support for SSO integration with enterprise identity systems
- RBAC and ABAC controls for users, teams, and business functions
- Restricted access to model management, connector setup, and audit views
Governed data access
- ACL-aware retrieval and source visibility inheritance
- Connector-specific permissions and sync boundaries
- Trust labels, retention rules, and jurisdiction-aware tagging
- Optional masking or redaction for sensitive content classes
Model and inference controls
- Controlled registration of approved model families and endpoints
- Ability to disable or constrain specific models by policy
- Guardrail thresholds and refusal behaviour for risky requests
- Structured orchestration rules for model selection and synthesis
Auditability and evidence
- Prompt, response, workflow, and policy-event logging
- Citation and source-evidence traceability where configured
- Drill-down from dashboard metrics to request and document evidence
- Exportable governance, activity, and evidence packages
Operational resilience
- Monitoring of compute, model health, queues, and connector status
- Visibility over failed jobs, failed files, and ingestion exceptions
- Backup, retention, and environment configuration controls
- Hosted or customer-managed recovery approach aligned to deployment mode
Security across the AI lifecycle
Security across the AI lifecycle
The architecture should make clear that control is applied from initial source onboarding through to final response generation.
Ingestion
Source systems are connected through governed connectors. Access boundaries, sync rules, and permitted repositories are defined before content enters the platform.
Preparation
Documents are parsed, classified, indexed, and tagged with metadata, trust labels, and visibility constraints so retrieval does not bypass enterprise policy.
Inference
Prompts are routed through approved models and orchestration logic. Guardrails, response constraints, and evidence requirements shape output behaviour.
Oversight
All critical events can be surfaced through governance and operational views, allowing drill-down into policy decisions, citations, model activity, and workflow records.
Governance model
Security architecture and governance architecture are intentionally linked within T-Flux.
Policy controls
The platform is intended to support policy-driven operation rather than ad hoc access. This includes who can connect data, who can publish models, which models can be used in production, which sources are trusted, and when a response must provide evidence or refuse to answer.
Evidence controls
Governance should not stop at access control. T-Flux is structured to provide operational and compliance evidence through audit logs, source traceability, model activity reporting, and exportable evidence packs for internal review or supervisory assurance.
Summary
T-Flux uses a layered enterprise security architecture that combines private deployment, tenant isolation, strong identity and access control, ACL-aware data governance, controlled model registration, response guardrails, and full auditability. The objective is to allow organisations to deploy and scale AI within a controlled environment where data, models, permissions, and evidence remain subject to enterprise policy.