Only about a third of enterprises see meaningful ROI from generative AI — and most have no reliable picture of where AI-written code lives in their portfolios. Software Improvement Group is now trying to close that gap with a new governance layer in its Sigrid platform.
Software Improvement Group (SIG) has launched AI Code Governance, a new capability in its Sigrid enterprise software intelligence platform that lets organisations detect AI-generated code and track where AI technologies are deployed across their application landscape.
The product addresses a concrete and growing problem. AI coding tools are now standard in professional software development, but most enterprise teams have no systematic way to see which parts of their codebase were written by an AI assistant rather than a human engineer. SIG's own data shows AI-generated code carries higher maintainability and security risks than human-written code, which means the speed gains at the drafting stage can be offset by technical debt that accumulates later.
AI amplifies what's already there. Organizations with strong software foundations will go faster and build better with AI. Organizations with technical debt, poor architecture, and ungoverned portfolios will accumulate more problems, more quickly. That was already true with AI coding assistants. With agentic AI — systems that write, test, and deploy code autonomously — the stakes are becoming even higher. The productivity gained in initial code production could be lost in future maintenance, unless you can see what's happening across your portfolio and act on it
Sigrid can now detect AI-generated code with 95 to 99 per cent accuracy across Java, Python, and C#, using stylometric analysis trained on 25 years of pre-AI enterprise code and on output from current frontier models. It identifies the delta between the two.
Jasper Geurts, CTO of SIG, said: "I'm proud of what our R&D teams have built. AI Code Governance detects AI-generated code across the portfolio with 95–99% accuracy. It works through stylometric analysis, trained on 25 years of pre-AI enterprise code and on what frontier models produce today. It learns the delta between them. Now, teams can see whether AI is introducing risk and fix it before it ships."
The launch comes as concerns grow about agentic AI systems that can write, test, and deploy code without direct human oversight — a scenario where the governance problem compounds. Luc Brandts, CEO of SIG, put it plainly: "AI amplifies what's already there. Organizations with strong software foundations will go faster and build better with AI. Organizations with technical debt, poor architecture, and ungoverned portfolios will accumulate more problems, more quickly."
Future Sigrid releases are planned to add AI-specific security risk flagging, productivity measurement for AI coding tools, and AI-assisted modernisation guidance.
