Conversational AI Trust and Transparency Audit (QA)

Conversational AI Trust and Transparency Audit (QA)

1. 01

   Inventory every conversational AI touchpoint in the client's stack

   List each chatbot, voice agent, or avatar deployment across web, messaging, and voice channels. Include the vendor, channel, and the specific customer journey stage it serves.

2. 02

   Review disclosure and consent language for each AI interaction

   Check that clients clearly inform users when they are speaking with an AI, not a human. For regulated industries like finance or healthcare, verify compliance with sector-specific transparency rules.

3. 03

   Test escalation paths to human agents under real conditions

   Run scripted scenarios where the AI fails or the user requests a human. Measure time-to-handoff, whether context transfers correctly, and whether the user feels the handoff was smooth.

4. 04

   Assess data handling and privacy controls for each vendor

   Confirm where conversation logs are stored, who has access, and whether data is used for model training. Flag any vendor that cannot provide a clear data retention policy.

5. 05

   Evaluate error rates and failure modes with a sample of live interactions

   Pull a representative sample of recent conversations and categorize errors: misunderstood intent, wrong information, or inappropriate tone. Compare against the client's service-level targets.

6. 06

   Document findings in a risk register with severity ratings

   Assign each issue a severity level and a recommended remediation owner. Share the register with the client's compliance or CX lead, and schedule a follow-up review within 30 days.