Generative AI introduces risks that differ meaningfully from traditional software: outputs are probabilistic rather than deterministic, training and grounding data can leak into responses, and a model can be manipulated through carefully crafted inputs in ways conventional applications cannot. Enterprises that apply only traditional application security review miss these categories entirely.
What belongs on the checklist
A practical governance checklist should require data classification review before any content is used to ground or fine-tune a model, testing for prompt injection and jailbreak resistance proportional to the sensitivity of the use case, and human review gates for any output that informs a consequential business decision. Usage policies should clearly state which use cases are approved, which require additional review, and which are prohibited outright, so employees are not left guessing.
Vendor and model selection deserves equal scrutiny: understanding what happens to submitted data, whether it is used for further training, and what contractual guarantees exist around confidentiality and data residency.
JIG helps enterprises build governance checklists and technical controls that let generative AI initiatives move quickly without moving recklessly.