AI droids introduce attack surfaces traditional SaaS doesn't. These are the architectural choices that keep that surface small — implemented in code, not in policy.
01
Credentials live in a vault, not in context
API keys and OAuth tokens are stored encrypted and injected at execution time by a backend tool gateway. The model itself never sees them — not when planning, not when calling tools, not in logs. This isn't a policy, it's how the runtime is built.
02
Sensitive actions gated by default
Outbound emails, code pushes, ad-spend changes, charges, app deploys — anything with a side effect waits for explicit approval in the channel of your choice before it runs. Admins decide which categories require it. Defaults are conservative.
03
Per-workspace sandboxes
Each workspace runs in an isolated execution environment with no cross-tenant access at the infrastructure or application layer. Memory, skills, and integrations are scoped to your workspace. What happens in your tenant stays in your tenant.
04
Your data never trains a model
Conversations, files, business data, and outputs stay in your workspace. We don't train on customer data — contractually, not just as a policy. Anthropic, OpenAI, and Google operate under no-training, zero-retention agreements for Droid traffic.
05
Untrusted content stays as data
Emails, web pages, attachments — anything inbound — is rendered as data inside the model context, not as instructions. Combined with the approval layer, an injection that hijacks the model still can't move money, push code, or send mail without you.
06
Encryption everywhere it matters
TLS 1.3 in transit, AES-256 at rest, secrets in a KMS-backed vault with automatic rotation. SSO via OAuth/OIDC on every plan; SAML 2.0 (Okta, Entra ID, Google Workspace) on Enterprise. MFA enforced at the IdP.