Case Study — Security Leadership
Responsible AI Coding: Security-First AI Adoption
"AI is not a coding tool. It is a thinking partner for every stage of building."
Challenge
Engineers were already using AI coding tools, unevenly and without guardrails — the risk wasn't AI itself, it was adoption outrunning any security framework for it.
Evaluation
A structured evaluation of the tools already in use against the org's actual threat model, not a generic vendor checklist.
Security framework
Guidelines built around how engineers actually work — prompt hygiene, dependency review, and a code-review process that treats AI-generated code as a first draft, not a finished one.
Phase 1
Security-embedded AI coding adoption
Tool evaluation against a real threat model, prompt hygiene as a trained habit rather than a wiki page, AI-aware code review, and internal guidelines the team actually reads.
Phase 2
AI across the full product lifecycle
The same rigour extended past the editor — discovery, PRDs, QA, and release communication, everywhere AI touches how the product gets built and shipped.
Lessons learned
The rollout that stuck wasn't the strictest one — it was the one engineers could follow without slowing down. Security that gets bypassed under deadline pressure isn't security.
Rolling out AI tools without a security framework? Let's talk.
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