We build the security layer around AI and agent systems: what they can touch, when they must ask, how credentials stay bounded, and how every consequential action is reviewed and logged.
AI systems fail differently than ordinary software. The risk is not only a vulnerable endpoint; it is an agent using the wrong tool, carrying stale authority, skipping a human approval, or turning a prompt into policy.
Agent9 treats those risks as architecture. We define the permissions, identity checks, approval gates, logging, and fallback behavior before autonomy reaches production.
A model should never be the security boundary. The enforceable boundary lives in the harness, the identity layer, the credential store, and the human approval path.
Rally documents a Google Cloud security model built around hostile email input, untrusted model output, repeated webhook delivery, and bounded autonomous loops.
The public security evidence names IAM-restricted Cloud Run invocation, Secret Manager-backed application tokens, Firestore identity and session records, KMS-wrapped per-connection keys, OAuth replay protection, short-lived magic links, and metadata-only telemetry.
For healthcare network email defense, Barracuda provided the security layer that materially reduced spam and phishing while surfacing employee-impersonation attempts before losses could occur.
Let us design the permission layer, review path, and security architecture before your agents touch production systems.
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