GitHub Expands Secret Scanning with General Availability of MCP Server Integration - InfoQ
PushButton AI Team ·
GitHub Expands Secret Scanning with General Availability of MCP Server Integration - InfoQ
You bought the AI tool. Nobody used it. $200/month gone, and now you're more skeptical than ever.
That's not a you problem. That's an implementation problem — and it's more common than anyone admits.
You bought the AI tool. Nobody used it. $200/month gone, and now you're more skeptical than ever.
That's not a you problem. That's an implementation problem — and it's more common than anyone admits.
Here's what caught my attention this week. GitHub just made it standard practice to automatically scan AI-generated code for security leaks — passwords, API keys, sensitive credentials — before they cause damage. Not because developers are careless. Because AI coding assistants move fast and miss things humans would normally catch.
This matters to you even if you're not writing code. It tells you something important about where AI actually delivers value: not in replacing judgment, but in handling the repetitive, high-stakes checking that humans skip when they're busy.
That's the lens missing from most AI conversations. The wins aren't in flashy tools. They're in automating the boring, error-prone tasks your team already does — just inconsistently.
One practical move this week: list three tasks your team repeats daily that follow a predictable pattern. That list is your AI shortlist. Start there. No ripping apart systems. No retraining anyone. Just one process, one tool, one measurable result.
What's the one task in your business you'd automate first if you knew it would actually work?
The release reflects a growing industry focus on securing AI-enhanced software delivery pipelines, where autonomous agents and AI coding assistants ...
