Adopt AI with confidence
Fear, uncertainty, and doubt shouldn't stand in the way of adopting AI. We help you build the platform and manage the risks to set you up for success.
From manual coding to autonomous agents
Each level up is more autonomy, and a bigger investment in controls.
Autonomous agent
Claude Code, Antigravity, Codex.
- Runs as
- A developer's own context
- Can reach
- Anything accessible on the system, including sessions, data
- Stopped by
- Code review and zero trust, runtime protection
We work alongside your team to identify your current maturity, where you're headed, and what controls to put in place first.
- Defined autonomy tiers for the use case
- Design and implementation with defined guardrails
- Acceptable gates preventing destructive actions
Trust in AI starts with the platform
Building a secure, reliable platform lets you adopt AI faster without taking on too much risk. Achieving greater autonomy requires a rigorous platform — confined blast radius, inventory, and modern policy management — in a space that's moving fast.
- Shadow AIpersonal plans
- Agent identitiesno central inventory
- Model APIsseveral with no governance
Where to start
AI security can be built on existing platform investments, but it introduces risk wherever those investments are lacking or implementations are incomplete.
Read the full field guide →Anatomy of an attack
Malicious instruction
A markdown from an untrusted source that the agent reads contains a malicious instruction.
Backdoor
The agent adds a malicous dependency that is interpreted as ordinary housekeeping.
Pipeline runs the malware
The malicious package gets built. Nothing in the pipeline treats agent-authored changes as untrusted input.
Exfiltration
A malware exfiltrates environment variables. The build completes as expected.
We write it all down
We write up what we've shipped, in more detail than is comfortable. All of it is free, none of it gated.