Step 01
Gap Assessment & AIMS scoping
Assess current AI usage, define scope, identify applicable requirements, and evaluate governance maturity across the AI lifecycle.
Certification
Establish a trusted AI Management System aligned with ISO 42001.
Design and implement an AI Management System (AIMS) that addresses risk, transparency, and accountability across your AI lifecycle—meeting growing regulatory and customer expectations.
We align policies, controls, and evidence with how your teams build and deploy AI - so governance becomes operational, scalable, and sustainable.
Defined checkpoints, ownership, and timelines so leadership can track AI governance maturity and risk posture in real time.
Documented model lifecycle controls, risk assessments, and decision logs that stand up to audits and customer scrutiny.
Align data science, engineering, legal, and GRC teams under a unified governance framework and shared accountability.
Translate AI risks, controls, and investments into clear insights for leadership, regulators, and enterprise buyers.
Step 01
Assess current AI usage, define scope, identify applicable requirements, and evaluate governance maturity across the AI lifecycle.
Step 02
Develop policies, standards, and controls covering model development, validation, deployment, monitoring, and ethical use.
Step 03
Operationalize controls across teams, including model documentation, risk assessments, human oversight, and lifecycle tracking.
Step 04
Conduct internal reviews, impact assessments, and remediation cycles to ensure readiness for external audits or stakeholder review.
Step 05
Coordinate audit activities, manage evidence requests, and support responses to findings through closure.
Step 06
Establish KPIs, model monitoring practices, review cycles, and governance updates to keep pace with evolving AI risks and regulations.
Not sure where to start? Book a short call—we will map gaps, priorities, and a practical next step.
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