Your industrial data stays controlled, protected, and auditable
Industrial AI adoption requires trust. This page explains access controls, encryption, privacy, data ownership, retention, incident response, auditability, and no-lock-in principles in language that works for executives, IT/OT, and plant teams.
- Data ownershipcustomer-owned
- EncryptionTLS 1.3 · AES-256 at rest
- Tenant isolationper-workspace
- Access controlrole-based, per tag
- Model trainingnever on customer data
- Audit logevery change replayable
Trust principles
Private
Customers decide who has access to their data and workflows.
Trusted
Data and predictions are protected against unauthorized manipulation.
Safe
Data is protected against loss with appropriate backup, resilience, and continuity controls.
Agile
Security controls and processes respond to the evolving threat landscape.
How it fits into the full Intelecy workflow
- 01
Connect relevant data through secure industrial integration.
- 02
Use Data Explorer to understand the historical and live process context.
- 03
Build, validate, deploy, and monitor the relevant model or workflow.
- 04
Route outputs to dashboards, alerts, recommendations, or controlled automation.
- 05
Measure the impact with verified operational KPIs.
Proof
Proof requirementAdd one relevant customer quote, screenshot, or mini story that proves this capability in a real industrial context. If proof is from an adjacent industry, label it clearly.
See this capability on a real process workflow
In a demo, Intelecy can show how this capability fits into your data, your team, and your operational targets.