Raqibix is the workflow governance layer for AI-assisted development. We keep context, evidence, approvals, and human judgment inside the environment where AI agents are writing code, so your security policies are followed, your approval chains are intact, and your audit trail exists by default.
If you lead security at a company where AI agents write code, these are the questions your CISO, your auditor, or your incident response team will eventually ask. Most teams cannot answer them today.
→ Your AI agents are operating under security rules right now. How many different places do those rules live, and when did any of them last agree with each other?
→ Someone approved the rules your AI coding agents are following. Can you name them, and produce a signed, timestamped record that proves it?
→ When a developer asks an AI agent to write authentication logic, is it working from your specific security policies, or making its best guess from generic training data?
→ An AI agent writes code today that causes an incident in six months. Can you reconstruct which policy version was active that day, and who had signed off on it?
→ How many hours a week does your security team spend catching AI-generated code that violated policies it never knew about in the first place?
→ Your next auditor will ask how AI-assisted development is governed at your company. Not whether you use AI. How it is governed. What will you hand them?
A malicious prompt was injected into the official release of Amazon Q v1.84.0, distributed to nearly a million developers. It instructed the AI to delete home directories, wipe S3 buckets, and remove IAM users.
Indirect prompt injection in PR comments caused GitHub Copilot Chat to exfiltrate AWS keys from private repositories. The attack affected every Copilot user until Microsoft patched it.
A hidden instruction in a GitHub Issue convinced Claude Code to leak credentials to a public repository. EDR saw nothing. Roblox rolled back AI coding access for 300+ engineers for four weeks.
Three dimensions need to be operationalised together.
What your AI coding agents are permitted to do, defined as structured policy your team controls, not generic defaults from a model provider.
Who approved those rules, when, and under what role, captured as a named, versioned, durable record rather than implicit consent from a code commit.
Continuous evidence that your AI agents are actually following the approved policy, generated automatically, ready when your auditor asks.
Security teams write structured policies with named approvers and version control. No more maintaining the same rule across docs, IDE configs, scanner rules, and audit binders.
Approved policies flow into AI agent context before code is written, and catch violations before code reaches a reviewer. Developers see consistent guidance. Security teams stop reviewing the same kinds of issues over and over.
A simple CLI (rx scan, rx pull) integrates into your existing pre-commit hooks and CI without restructuring your workflow.
Every policy version, every approver, every enforcement event, captured as it happens. When auditors ask how AI-assisted development is governed at your company, the answer already exists. Not screenshots, not theater. Actual evidence.
Raqibix is built around two surfaces. Security teams author and approve policy in the web workspace, structured forms, approval workflows, signed records. Developers see those policies enforced through a lightweight CLI (rx) that integrates with pre-commit hooks and CI pipelines. The two are connected through an API: when a CISO approves a policy, every developer's next rx scan immediately reflects it. Same product, two surfaces, designed for the people who do each part of the work.
AI governance has two categories: workflow-level and substrate-level. We want to be clear which one we're in.
Substrate-level governance would mathematically constrain what AI models can produce. That's an unsolved research problem. We don't claim to solve it.
Workflow-level governance is what we do, and what enterprises actually need today. We give security teams a structured way to author policy with named approval, ensure it reaches AI agents at coding time, catch violations before code is committed, and produce audit evidence for SOC 2, ISO 27001, and the EU AI Act. It's the governance layer your compliance framework requires today.
Vulnerability scanners find weaknesses after code is written. Runtime governance engines enforce policy when AI agents take actions in production. Code review tools comment on pull requests. GRC platforms orchestrate the broader compliance program.
Raqibix operates at a different layer: workflow governance of what AI agents are permitted to write at coding time, with the approval chain, version history, and audit trail your compliance program requires.
We're complementary to the tools you already use. We close the workflow governance gap that none of them address: who writes the rules, who approves them, what version was active when, and what your AI agents are actually following.
Building Rx, the policy and audit layer for AI-assisted development.