Resources
Practical guidance for AI-assisted engineering
Build policies developers can follow, measure AI-assisted work with honest evidence, protect sensitive inputs, and keep tool costs accountable.
Templates
Templates
Copy-ready policy material for engineering and security teams.
Explainers
Explainers
Clear descriptions of the evidence, controls, and limits behind AI governance.
explainer
What Is Shadow AI in Software Engineering?
Learn how unapproved AI coding tools, personal accounts, MCP servers, and extensions appear, what they risk, and how engineering teams detect them.
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How to Measure AI-Assisted Pull Requests
Understand session, branch, commit, file, and hunk evidence for AI-assisted pull requests, plus a transparent contribution estimate and confidence model.
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DLP for AI Prompts on the Developer Machine
See how on-device DLP finds secrets and personal data before AI requests leave the machine, with honest warn, redact, block, and audit outcomes.
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Guides
Operating practices and shared vocabulary for an AI engineering program.
guide
AI Coding Tools: Cost and Seat Hygiene
Build a monthly review for AI coding tool seats and token costs, find inactive licenses, measure pricing coverage, and make renewal decisions by team.
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AI Engineering Control Plane Glossary
Use this plain-language glossary for AI engineering control planes, covering sessions, adapters, MCP, DLP, attribution, seats, true-up, and audit terms.
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