llm app security

The model did not write malicious HTML on its own; a web page it summarised did. Then the conversation was shared, and the renderer trusted it.

The provider secures the model service. Everything you put into it, connect to it and build on top of it is still yours.

The regulator does not care which classifier you used. The design that scores puts authorisation and commitments in code, uses classifiers where they are good, and decides in advance what happens when a streamed answer goes wrong halfway.

Build an authorized chatbot assessment around real data and action boundaries, then turn observed effects into findings, useful-work checks and retest criteria.

Trace the data from its owner to each actual reader. Repair the access or reuse boundary, including caches, revoked permissions and retained artifacts.

A startup cannot afford a governance department. It can afford two weeks that close the risks that would end the company.