Your AI governance gap is bigger than you think

Across every industry, AI governance is now a pressing challenge for C-suite executives and senior leaders. Some of the most common questions I’m hearing right now all circle back to a similar issue: How do you govern AI that’s already being used across your organization?

Don’t ask. Assume AI is already being used, with or without your permission. The question isn’t whether AI is being used, but whether it’s being used well and safely.

The biggest mistake leaders make is treating AI governance as a future problem when it’s already a present one. Without protocols in place, there’s no visibility into how AI is being used or where it may be creating risk for your brand, privacy or quality of work.

Your job is to understand how it’s being used, which tools are in play and where that usage creates risk for your organization.

To get a clear picture of your team’s AI usage:

• Conduct a survey to see which LLMs they use most often in their day-to-day work (ChatGPT, Gemini, Claude, etc.) and 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 usage, resisting it or somewhere in between?
• Ask whether they have enough guidance to use AI confidently right now or if they’re largely figuring it out on their own.

What you learn here will help you determine the next steps. The more insight you have into how your teams are actually using these tools, the better positioned you are to create a governance framework that catches issues before they escalate.

You may already have a compliance and privacy problem

Large organizations, especially in regulated industries, can unknowingly expose themselves to significant risks when there’s no clear oversight of AI use.

Without an AI governance policy, teams may be feeding private or sensitive information into LLMs whose chat logs could be used for model training, putting your organization at risk of liability for:

• Privacy issues from proprietary or client information being entered into third-party models that train on the data.
• Security risks from AI tools that haven’t been evaluated or vetted by security teams or IT.
• Legal exposure from agreeing to third-party terms that give AI platforms rights over any data input.
• Risks from AI tools that retain conversation history that could be accessed or subpoenaed in the event of a breach.

If you’re in a regulated industry and lack visibility into what’s being used or what data is being shared, implement a governance policy that gives your organization control.

Define which tools are approved and which are not

Although generative AI usage has grown rapidly over the past few years, not all AI tools carry the same risk. An LLM chatbot that uses your data for model training 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 cleared for day-to-day use.
• Which tools can be used in limited or specific use cases.
• Which tools and platforms are not permitted under any circumstances.
• Whether subscription plans or free tiers are allowed.
• How tools are approved and which teams are responsible.

This is especially important if your organization is in a regulated industry, where compliance standards around data handling, privacy and security are more stringent.

Create clear guardrails around data and privacy

Without explicit guidelines, people will make their own judgment calls about what’s safe to share with AI tools and those calls may not always be correct. This lack of awareness creates human risk and exposes your organization to unnecessary data privacy violations and security vulnerabilities.

Your data and privacy guardrails should cover:

• Which tools can be used with internal documents and sensitive data, and which can’t.
• What categories of information aren’t permitted in any prompt, such as PII, internal documents, client data or financial information.
• How to handle confidential vendor or partner information.
• 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’s easy to understand and practical to apply. For example, a one-page infographic is easier to remember than a 50-page policy that’s too dense to read.

Build a QA process before you scale up in production

Another risk that’s often overlooked is quality deterioration, stemming 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 QA process in place, quality can slip as production outpaces the ability to maintain brand standards.

Before scaling anything, define:

• The review process for all AI-generated content.
• Which content types require heavier editorial oversight versus lighter review.
• What good enough looks like.
• Who has final sign-off authority.
• Brand voice, tone and messaging guidelines for generated content.
• How ownership of quality issues is handled.

AI can be a powerful tool, but without a QA protocol in place, output quality can quickly deteriorate and erode trust with stakeholders.

Create an AI governance policy that evolves with your organization

Establishing an AI governance policy shouldn’t be a one-time process. The space is evolving too quickly for rigid protocols. As tool capabilities and usage evolve, use cases can expand and shrink. As long as AI tools are in use, your governance policy will need to be revisited. Leaders writing the policy will need to remain flexible and keep up with the pace of change.

To help governance policies evolve over time:

• Start a feedback process where employees can ask questions, share new tools and discuss AI usage.
• Schedule regular reviews to audit approved tools, update guardrails and assess what’s working.
• Reinforce good AI usage and work to mitigate poor usage.

Don’t wait to build guardrails

An AI governance policy doesn’t need to be complicated or dense, but it does need to exist. Build on how AI is already being used and understand how it’s being applied. Define what tools are permitted and not permitted, what use cases should look like and how to maintain quality standards when AI is part of content production.

Revisit your policy on a quarterly, semi-annual or annual basis to ensure teams have up-to-date guidance to use these tools safely and effectively.

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