Engineering
Trust3 Labs is the research arm of Trust3 AI. Our team studies how autonomous AI agents behave in real enterprise environments, where access control, data governance and runtime enforcement break down, and what it takes to secure them. We publish engineering deep dives, threat research, open tools and conference talks so security and data leaders can understand emerging risks such as agent-to-agent communication, MCP access, credential exposure and policy drift, and put practical controls in place before those risks reach production. This section collects our engineering content, so you can follow one thread of our work in depth. Every article is written by the practitioners who build and test the Trust3 AI platform.
Every post, tool release, and field note from the Trust3 research team.

Engineering Agent Authorization, Part 2: Where to Enforce AI Agent Permissions in Gateways, Tools, and Destination Systems
The tradeoffs between enforcing agent permissions in a gateway, a trusted tool, or the destination system, and what each one still cannot…

Engineering Agent Authorization, Part 1: Designing Capability Tokens That Bound Scope, Custody, Delegation, and Lifetime
AI agents turn model-generated choices into tool calls that can read data and change external systems. For engineers building those integrations, authorization…

Securing A2A’s Two Attack Surfaces: Ingress vs. Egress
Key Takeaways A2A calls are one more entry point for prompt injection: a receiving agent has no way to tell trusted instruction…

The 3 Layers of Agent Enforcement: Where Your SSH Key Actually Leaks
TL;DR Scope is pro-code agents (Claude Code, Codex, Cursor, Cline) on a developer’s machine, not no-code platforms. Infrastructure controls don’t apply to…

Policy the Agent Reads Is Not Policy the Agent Enforces: Build-Time Configuration ≠ Runtime Enforcement
TL;DR Build-time configuration is the necessary starting point for agent security, but visibility into an agent’s config is not control over its…