Your AI changed.
We check.

AI governance sets the standard: the policies, risk appetite and controls a firm commits to. AI assurance produces the proof: it re-tests, after a material change, that the deployed system still meets that standard. Governance without assurance is a policy document; assurance without governance has nothing to test against.

When something material changes, not on a fixed calendar. A material change is a model version, prompt, data, permission or integration change in the workflows you rely on. The vendors behind your tools ship updates on their schedule, so the trigger is the change itself.

AI model drift is when an AI tool's answers or behaviour change over time without anyone changing its settings. It usually follows a model update, a shift in the data the tool reads, or a vendor changing a default. Assurance catches it by re-running the same checks after each material change, so a drop in quality is found by a test rather than by a client.

Token costs rise with how much text an AI tool reads and writes, so the cost check looks at usage by workflow, not only the monthly bill. It compares consumption against what each use case is for, flags runs that cost more than expected, and shows where a prompt or feature could be trimmed. You get a cost view your team can review.

A report lists each check that ran, what it tested, what passed, what failed, and the evidence behind each result. Your team can review it without us in the room. Anything that failed comes with the step needed to fix it, and the checks run again after the next material change.

Yes. Agentforce behaviour depends on the underlying models, your data and your permissions, and those change between releases. A single-platform stack narrows the inventory, not the need: the security, performance and cost checks still apply, and the consolidation check can show where the platform now covers other paid tools.