Plan a defensible audit
Define scope, criteria, evidence, stakeholders, and the limits of the assessment.
AILI Certification 06 / 08
Learn a structured, evidence-based approach to AI assurance. Examine governance, data, models, security, operations, and impact—then translate what you find into action.
What you will be ready to do
Assess AI systems through evidence, controls, findings, and remediation.
This program combines live learning with applied practice so you leave with a repeatable approach and evidence of what you can do.
Define scope, criteria, evidence, stakeholders, and the limits of the assessment.
Distinguish control design gaps from failures in how controls operate.
Connect likelihood, impact, evidence, ownership, and urgency in clear language.
Turn findings into prioritized actions, validation steps, and executive reporting.
Inside the intensive
Set objectives, map the system lifecycle, choose criteria, and build an evidence request.
Test policies, accountability, data practices, evaluation, documentation, and monitoring.
Assess access, misuse, incidents, vendor exposure, user harm, and ongoing control performance.
Write defensible findings, prioritize remediation, and brief decision-makers without losing nuance.
The two-week capstone
Conduct a scoped AI risk audit and produce a professional assurance package with evidence, control testing, rated findings, and a remediation roadmap.
Who this is for
Tell us what you want to be able to do, and we’ll help you choose the right certification and next cohort.