How To Govern Employee-Built AI And Low-Code Tools

As AI, low-code tools and automation become part of everyday technology development, more employees can build applications, workflows and other digital tools. That can speed up experimentation and problem-solving, but it can also leave organizations with a growing layer of technology that’s difficult to track, secure and maintain.

As companies broaden who can build technology, they also need to make sure greater access doesn’t come at the expense of effective oversight. Here, members of Forbes Technology Council share guardrails organizations can consider as they open development to more employees while keeping the technology they create manageable.

Build Dynamic Trust Into Citizen Development

Registries, owners and kill switches are necessary, but they’re still mechanisms of control, and control won’t scale with citizen-built AI. Standardize the tool layer with MCP or similar standards, then make trust an architectural primitive: measurable, contextual and dynamic, so autonomy can be earned, expanded and revoked based on evidence and risk. - Casey Kindiger, Droma (formerly Grokstream)

Standardize AI-Assisted Code Design

Establish a code design template. Create a package (versus stand-alone functions) for functionality that is shared between different groups. Handle authentication mechanisms involving usernames and passwords through defined security procedures or packages—for example, use keyring rather than storing credentials in code files. Employ config files for static values. There should be a check to ensure any new AI-based code adheres to this design before deployment. - Aritra Pal, First Citizens Bank

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