Why AI Cybersecurity Projects Often Stall After a Successful Demo

Colleagues, a note: AI demos in cybersecurity are impressive, but often fail to transition to production.

I encountered common issues:
— Data quality: demos use clean datasets; production data is fragmented and noisy.
— Integration and latency: the model may be fast alone but loses value in multi‑step workflows.
— Edge cases: exceptions and unpredictable user behavior break scenarios.
— Governance and compliance: without early policies projects get stuck in approvals.

Why it matters: success depends less on the model and more on fit with real workflows and governance.

What do you consider the first step when moving from demo to production?

#AI #Cybersecurity #DataGovernance #DevOps