Across all industries, AI governance is now an urgent challenge for executives and senior leaders. Some of the most common questions I hear right now all come back to a similar problem: How do you govern the AI that’s already in use in your organization?
Don’t ask. Let’s assume that AI is already in use, with or without your permission. The question is not whether AI is used, but whether it is used correctly and safely.
The biggest mistake leaders make is viewing AI governance as a future problem when it is already here. Without protocols in place, there is no visibility into how AI is used or where it may create risk to your brand, your privacy, or the quality of your work.
Your job is to understand how it is used, what tools are used, and where that use creates risk for your organization.
To get a clear picture of your team’s use of AI:
- Conduct a survey to see which LLMs they use most often in their daily work (ChatGPT, Gemini, Claude, etc.) and their preferences.
- Identify whether specialized AI tools, such as AI agents, are used.
- Gauge how comfortable people are with AI. Are people embracing its use, resisting it, or somewhere in between?
- Ask them if they currently have enough guidance to use AI confidently or if they are figuring it out on their own.
What you learn here will help you determine your next steps. The more information you have about how your teams actually use these tools, the better you’ll be able to create a governance framework that catches problems before they escalate.
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You may already have a compliance and privacy issue
Large organizations, especially in regulated industries, may unknowingly expose themselves to significant risks when there is no clear oversight of AI use.
Without an AI governance policy, teams may pass private or sensitive information to LLMs whose chat logs could be used for model training, putting your organization at risk of liability for:
- Privacy concerns related to entering proprietary or customer information into third-party models that train on the data.
- Security risks related to AI tools that have not been assessed or verified by security teams or IT.
- Legal exposure by agreeing to third-party terms that give AI platforms rights over any data capture.
- Risks related to AI tools that maintain conversation history that could be accessed or subpoenaed in the event of a breach.
If you work in a regulated industry and lack visibility into what is used or what data is shared, implement a governance policy that puts your organization in control.
Although the use of generative AI has seen rapid growth in recent years, not all AI tools carry the same risk. An LLM chatbot that uses your data for training models carries a very different risk than an enterprise-level AI tool with guaranteed privacy protections.
With a clear list of approved tools, your team can reduce exposure to risks with serious consequences. Address:
- Which tools meet compliance, legal or security standards.
- Which platforms are allowed for daily use.
- Which tools can be used in limited or specific use cases.
- Which tools and platforms are not allowed under any circumstances.
- Whether subscription plans or free tiers are allowed.
- How tools are approved and which teams are responsible for them.
This is particularly important if your organization is in a regulated industry, where compliance standards for data processing, privacy and security are stricter.
Create clear guardrails around data and privacy
Without explicit guidelines, people will make their own judgments about what is safe to share with AI tools, and those judgments will not always be correct. This lack of awareness creates human risk and exposes your organization to unnecessary data privacy breaches and security vulnerabilities.
Your data and privacy safeguards should cover:
- Which tools can be used with internal documents and sensitive data, and which cannot.
- What categories of information are not allowed in any prompt, such as personal information, internal documents, customer data, or financial information.
- How to manage confidential information from suppliers or partners.
- Requirements for anonymizing data before using AI to analyze it.
- Compliance regulations specific to your industry, such as GDPR.
Your AI governance policies should clearly document these guidelines in a way that is easy to understand and practical to apply. For example, a one-page infographic is easier to remember than a 50-page policy that is too dense to read.
Create a quality assurance process before moving to production
Another often overlooked risk is quality deterioration, which arises from the assumption that AI can produce content at scale with little human oversight. When AI is used to produce content in large volumes without a quality assurance process in place, quality can decline as production exceeds the ability to maintain brand standards.
Before scaling anything, set:
- The process of reviewing all AI-generated content.
- What types of content require heavier editorial oversight rather than lighter editing.
- Which looks pretty good.
- Who has final signing authority.
- Brand voice, tone and messaging guidelines for generated content.
- How ownership of quality issues is managed.
AI can be a powerful tool, but without a quality assurance protocol in place, the quality of results can quickly deteriorate and erode stakeholder trust.
Create an AI governance policy that grows with your organization
Establishing an AI governance policy should not be a one-off process. The space is evolving too quickly for rigid protocols. As tool capabilities and usage evolve, use cases may expand or contract. As long as AI tools are used, your governance policy will need to be reviewed. Leaders who write policy will need to remain flexible and keep pace with change.
To help governance policies evolve over time:
- Start a feedback process where employees can ask questions, share new tools, and discuss the use of AI.
- Schedule regular reviews to audit approved tools, update guardrails, and evaluate what’s working.
- Reinforce the right use of AI and work to mitigate misuse.
Don’t wait to build guardrails
An AI governance policy doesn’t need to be complicated or dense, but it needs to exist. Take advantage of how AI is already used and understand how it is applied. Define which tools are allowed and not allowed, what use cases should look like, and how to maintain quality standards when AI is part of content production.
Review your policy on a quarterly, semi-annual or annual basis to ensure teams have up-to-date guidance on using these tools safely and effectively.
