> For the complete documentation index, see [llms.txt](https://help.tillit.cloud/tillit/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.tillit.cloud/tillit/tools/aura/security.md).

# Security

> ### Aura was built with isolation, read-only access, and sandboxing as foundational design principles.

## Data Isolation

### Read-only guarantee

There is no write path from the AI agents back to the source data.

### Constraints on what Aura can do

* **No production database connection.** Aura holds no connection string or credentials for the live database, and has no network path to it. It only processes pre-exported, static snapshots.
* **No data modification.** The read-only database configuration and immutable format mean there is no write path to any data source.
* **No persistence between runs.** Each analysis cycle uses a fresh snapshot. The container environment is disposable, and analysis artefacts are discarded after reports are generated.
* **No cross-tenant access.** Each tenant deployment is scoped to its own data partition, with separate storage paths and access credentials. There is no shared data layer between tenants.
* **No arbitrary code execution on customer infrastructure.** AI agents run inside an isolated container within the Aura environment, with no access to the customer's network, servers, or systems.

## Network Isolation

The Aura container enforces a network allowlist at the firewall level. This is a deny-by-default configuration: any destination not explicitly listed is blocked.

## Containerised Sandboxing

Aura's execution environment uses layered isolation with private subnet, no public IP, runs as a non-root user with limited capabilities in its Docker Container and the filesteme restricted to specific AI agent processes.

## AI Model Interaction

#### Data sent to the model

When an analysis cycle runs, the following categories of information may be sent to the LLM:

* **Aggregated metrics** — statistical summaries such as OEE percentages, downtime totals, and production counts, rather than raw row-level records.
* **Query results** — SQL result sets, limited to the columns and rows relevant to a specific hypothesis being tested.
* **Analysis prompts** — instructions describing what to investigate, referencing the customer's facility context and KPI definitions.
* **Operator comments** — free-text observations from the MES event log, used to corroborate statistical findings.

#### Safeguards

* Encryption in transit.
* No model training on customer data.
* Regional data residency.
* No cross-session persistence.

For technical questions about this architecture, contact your Roima account team. See [here](https://www.roimaint.com/en/product/offering-by-product-aura-ai/aura-ai--aura).