Data Handling
AI tool integration
Trace a task from source to approved result, then assess browser, workflow, connector and API routes, including permissions and failures.
Section
Data Handling
Data Handling
AI tool privacy and data handling
Check what enters an AI tool, who can access it, how long it may remain and whether it can be used for training before approving business use.
Data Handling
Checking API and export options before adoption
Check how an AI tool exchanges task data and how you would retrieve records, workflows and run history before adopting it.
Data Handling
Check whether an AI vendor uses submitted data for training
Find the training rule for the precise AI product, account and feature, including prompts, files, feedback and connected services.
Data Handling
Browser tools vs business-system integrations by task
Compare a person-led browser handover with a connected AI workflow by tracing the input, review, final action and failure path.
Data Handling
Keeping confidential data out of a public tool trial
Set an input boundary, prepare fictional examples and check every upload route before trialling a public AI tool.
Data Handling
Reviewing conversation logs without exposing customer data
Use aggregate patterns, limited access and carefully prepared excerpts to review AI assistant conversations while reducing customer-data exposure.
Data Handling
Reviewing recording permissions before using a meeting bot
Check participant notice, host approval, auto-join, sharing and stopping controls before a meeting bot captures a call.
Data Handling
Reviewing retention settings before uploading business files
Trace chats, uploaded files, projects and compliance copies before deciding whether a business file may enter an AI tool.
Data Handling
Separating attractive images from usable production files
Inspect exported AI artwork for dimensions, transparency, editability and handover quality before approving it for web or print.