Compliance · EU AI Act
SweetHive was built AI-native, not AI-retrofitted. Its architecture aligns with the core principles of the EU AI Act — Regulation (EU) 2024/1689 — because data minimization, human oversight, least privilege and auditability are properties of the data model, not policies bolted on afterwards.
The AI Act asks organizations to minimize unnecessary access to data and reduce the impact of AI systems. SweetHive implements that at its core: every agent operates inside a strictly defined scope, and on every request its effective permissions are the intersection of the user's current permissions and the token's configured scope.
An agent reaches only the hives, contexts and sub-contexts explicitly granted to its token — and only information already visible to the requesting user.
Permissions are computed at answer time as user ∩ token. An assistant can never access anything beyond what its owner can currently see.
Lose access to a context or group, and every connected agent loses it at the same instant — no token regeneration required.
Every connection follows the Principle of Least Privilege recommended across European cybersecurity and AI governance frameworks. Agents receive only the capability their task requires — and organizations decide which capabilities a hive allows, or disable external agents entirely.
The default. The agent can read items and search — strictly within its scope.
It can also prepare drafts for a person to review and send. Nothing is published.
It can publish — but only after a human confirms the exact text, and always attributed via the user.
A key AI Act requirement is that people remain in control of AI-assisted decisions and actions. An agent can draft messages, summaries or reports — but publishing always remains a human act.
Privacy and security are embedded throughout — from how tokens are scoped and revoked to where inference can run.
Configurable expiration, one-click revocation, continuous permission verification. A leaked token never exposes an entire account — its reach stays limited to the configured scope and the owner's current permissions.
Connectors are read-only unless a capability is explicitly granted. Secrets are encrypted; credentials are managed server-side.
With SweetHive Agents Node, models can run on the user's own machine — prompts and data stay local during inference, reducing data transfers.
Organizations retain full visibility and control over connected AI systems. For every agent you can see its scope, its capabilities, when it was last used and whether it is online — and revoke it in one click.
Which agents are connected, which contexts they can access, and which capabilities they hold — never hidden.
Last token usage, expiration dates and a live online indicator support accountable, auditable AI workflows.
Every AI-generated post is attributed and obeys the same group targeting as any message — accountability by construction.
The AI Act regulates the deployment and use of AI systems rather than collaboration platforms themselves. SweetHive provides the technical foundation that makes governance practical.
| AI Act principle | SweetHive implementation |
|---|---|
| Data minimization | Scoped contexts and group-based visibility |
| Human oversight | User confirmation before AI publishing |
| Least privilege | Capability-based scoped tokens |
| Transparency | Visible connected agents and permissions |
| Accountability | Token tracking and AI attribution |
| Privacy by design | Local inference, scoped access, secure connectors |
| Security by design | Dynamic permission enforcement and immediate revocation |
Secure, private, human-controlled and ready for enterprise AI adoption under European regulatory frameworks. See it on your own structure.
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