AI adoption without trust: A call for compliance professionals
By 
Tom Fox2025-09-16T18:39:00
Employees are adopting AI faster than companies can build policies, governance, and training. That gap creates compliance exposure in areas from data privacy to shadow IT to workplace equity.
What strategies can compliance professionals adopt to handle this growing problem?
THIS IS MEMBERS-ONLY CONTENT
You are not logged in and do not have access to members-only content.
If you are already a registered user or a member, SIGN IN now.
Related articles
-
OpinionThe rise of the AI compliance officer
As AI reshapes business operations and regulators move quickly, companies increasingly need a dedicated AI compliance officer to ensure ethical, transparent, and accountable deployment.
-
OpinionThe CFO, AI, and the New Compliance Frontier
As CFOs use AI to streamline operations, they face new compliance risks tied to accountability and algorithmic governance. CCOs must work with them to ensure transparency and oversight throughout adoption.
-
OpinionWhat compliance can learn from a 95 percent AI pilot failure rate
Compliance professionals have long known that systems fail when governance does. An MIT study’s finding that 95 percent of enterprise artificial intelligence (AI) pilots fail underscores how essential compliance-grade discipline is to the success of emerging technologies.
More from Opinion
-
OpinionWhen AI Is Forced on Compliance: The ECCP as your Guide
When a company rapidly adopts AI, compliance officers can be blindsided, tasked with governance almost immediately. Luckily, there is a guide from the U.S. Department of Justice to help.
-
OpinionEthics as strategic value: When compliance becomes a board-level decision tool
For many Boards of Directors, compliance reporting feels familiar and reassuring. Dashboards are green. Policies are updated. Training is complete. Incidents are investigated and closed. On paper, the system works.
-
OpinionSafely leveraging generative AI: A practical guide for compliance leaders
Generative AI (GenAI) has moved rapidly from experimentation into day-to-day use across many organizations. Over the past year, teams have shifted from exploratory pilots to relying on these tools for core activities such as contract analysis, research, and software development.