Deep dives into AI agent security, zero-trust architecture, and the engineering decisions behind Ruakiel.
Secrets and adversarial content arrive through user prompts, tool arguments, tool results, and model responses — four distinct boundaries each requiring independent filtering. Here is why single-point defenses fail and what all-boundary enforcement looks like in practice.
AI agents fail differently from deterministic software. A malicious tool response, a poisoned retrieval result, or an adversarial prompt can cascade silently. Here is how Ruakiel bounds the blast radius before the failure occurs.
Standard SaaS isolation relies on query filters. AI agents introduce conversation history, context windows, and cross-objective artifacts — none of which a database row filter can protect. Here is what structural isolation actually requires.
Every AI platform claims enterprise-grade security. The distinction between marketing and engineering is evidence: traceable rules, specific tests, and documented findings. Here is what that looks like in practice.
Traditional security models assume a trusted perimeter. AI agents that call external tools, access user data, and make autonomous decisions break every assumption those models rely on.
Prompt injection is the SQL injection of the AI era. A single-layer defense will fail. Here is how Ruakiel applies defense-in-depth — from input validation to output filtering — to keep agents under control.
Your AI agent has access to tools that can read databases, send emails, and mutate state. Without role-based access control, every agent is a superuser. Here is how to fix that.