Stopping design IP leaking into AI tools across 12 manufacturing sites
Engineers were pasting specifications and code into AI assistants to move faster. For a manufacturer whose value is its designs, that was an existential blind spot.
reduction in design IP reaching unapproved AI tools
of flagged events involved design files or source code
to full visibility across 3,000 endpoints
The challenge
Design specifications, CAD exports and firmware source were the crown jewels, and they were exactly what engineers were sharing with AI assistants to debug faster and document less.
With sites across three continents, off-network laptops and a long tail of engineering SaaS, a network chokepoint was never going to see the risk, and re-architecting global traffic through a proxy was a non-starter.
The approach
Discover, control, detect, prove
Discover
Lightweight sensors deployed across 3,000 endpoints in two days, on and off the corporate network, with no re-architecture.
Control
Guardrails block design files and source code from leaving to unapproved AI destinations, and steer engineers to the sanctioned enterprise assistant.
Detect
Pre-trained models recognised source code and technical drawings in prompts and uploads, the events keyword DLP had missed for years.
Prove
Site-level reporting showed leadership exactly where IP risk concentrated, and tracked it falling quarter on quarter.
The results
- Design IP reaching unapproved AI tools fell 94% in the first quarter, while engineers kept an approved assistant for the use cases that were driving adoption.
- Two-thirds of flagged events involved design files or source code, evidence that finally moved AI risk from a compliance topic to a board priority.
- No network changes, no latency added to global traffic, and nothing for site IT teams to maintain.
“Our DLP had been "protecting" designs for years and never flagged a single AI event. The first week of CultureAI data ended that conversation.”
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