eBook
Strategies for Effective AI Governance
The growing integration of artificial intelligence (AI) within organizations brings significant challenges, particularly concerning governance. As AI becomes more ingrained in decision-making processes, the potential for unintended repercussions increases, posing risks to customers, employees, communities, and society at large.
The task of AI governance is to navigate this landscape by establishing a robust framework that addresses ethical, legal, and societal concerns. Without structured governance, the potential to breach legal norms and ethical standards escalates, threatening trust and safety.
Why do organizations need a formal AI governance framework?
As AI adoption accelerates — with 44% of organizations now using AI in some form — the risks of unintended harm, bias, and regulatory non-compliance grow alongside it, making a structured governance framework essential rather than optional.
What does governing AI responsibly actually look like in practice?
Effective AI governance follows four key steps: inventorying and documenting all AI systems, conducting risk assessments specific to AI's unique impacts, implementing controls across the full AI lifecycle, and continuously monitoring for emerging risks and incidents.
How is AI governance different from traditional IT risk management?
Unlike standard IT risk assessments that focus on data classification and system functionality, AI governance must also account for ethical considerations, potential societal impacts, and compliance with AI-specific regulations like the EU AI Act and the NIST AI Risk Management Framework.
About Archer
For 25 years, Archer has been a trusted leader in GRC innovation, providing global organizations with enterprise compliance and risk management solutions that combine artificial intelligence, risk intelligence, and integrated approaches to improve decision-making, strengthen regulatory compliance, and build resilience at scale. Learn more at www.archerirm.com