AI Governance: Why it Belongs in Your Privacy Program and Steps to Deploy

If your organization has a privacy program that’s finally starting to feel “real” (e.g., you have a website notice you trust, a DSR process that mostly works, and even a basic incident response plan) the next question often sounds like:

“Do we have to do anything special for AI… or can we just treat it like any other tool?”

In most organizations, the honest answer is "you can treat AI like any other tool," that is until the first moment it quietly rewrites your data flows, your risk profile, and your expectations from regulators, customers, and employees. Oops!

That’s why it’s worth thinking of AI governance as a natural extension of your privacy program, not a completely separate universe. The same disciplines you’ve built for personal data, such as data mapping, DPIAs, vendor review, incident response, and training curricula, are exactly the tools you need to manage AI well.

This article walks through three things:

1. Why a mature privacy program should deliberately add AI governance to its privacy ecosystem
2. The most common AI use cases you’ll want to get your arms around
3. What to include in an employee AI Acceptable Use Policy (AUP) so your people know what’s allowed, what’s not, and when to ask for help

Remember, this article is for general information and education only. This is not legal advice. You should talk to your own counsel about how the law and rules apply to your specific facts.

1. Why AI Governance Belongs Inside Your Privacy Program

You don’t need a separate “AI tower” that sits off to the side of your privacy program. In fact, trying to govern AI completely separately often creates more confusion and inconsistency. AI governance makes sense as a layer inside your existing privacy and data protection framework for a few important reasons.

AI eats personal data for breakfast

Most AI use cases are powered by some combination of:

• Personal data used to train models
• Personal data used as inputs (prompts, uploaded files, live integrations)
• Personal data embedded in outputs (personalized responses, scores, recommendations, profiles)

The common denominator in all of those... personal data! Yout existing privacy obligations (e.g., transparency, lawful basis, minimization, retention, access rights, security, cross‑border transfers, and vendor management) all already apply, even if the technology label is new.

If you already have data mapping, DPIAs, retention schedules, incident response procedures, and vendor risk management in place, you are more than halfway to AI governance. You just need to apply those disciplines to a new class of systems where the risks are a little less intuitive.

AI risk isn't just that it will output “hallucinations”

The popular conversation about AI often revolves around hallucinations and copyright. Those are real and represent an actual risk, but the privacy and governance risks of AI usually show up in quieter ways:

• An employee pastes sensitive customer data into a free‑tier tool, which retains it.
• A marketing team uses generative text to personalize emails in a way that feels invasive or discriminatory.
• A recruiting model drifts over time and starts screening candidates in a way that’s indirectly biased.
• A product team quietly ships a feature that autocompletes user queries using logs that were never meant for that purpose.
• A company chatbot starts collecting information from users that wasn't contemplated when it was deployed.

These are a very small set of examples of the kind of issues your privacy program is built to handle. Data misuse, unfair or unexpected processing, invisible data sharing, and loss of control over information are all privacy risks, not just AI risks.

Regulators are already connecting the dots

Around the world, regulators are making it clear that AI and privacy are linked. Data protection authorities have opened investigations into AI tools and models, questions about training data and legal bases, and concerns about profiling, automated decision‑making, and fairness. Sectoral regulators (financial, health, employment) are starting to issue guidance on how AI fits inside existing rules.

To have an effective program that covers AI, you don’t need to chase every headline. However, you do want to be in a position, if asked, to say:

• We know where AI is used in our business.
• We have a process for evaluating higher‑risk use cases.
• We have policies and controls that limit what staff can do with AI tools.
• We can explain, at a high level, how we mitigate the main risks.

Those are privacy program answers, not one‑off, case‑by‑case improvisations.

2. Common AI Use Cases You Will Want to Address with Your Privacy Program

To govern AI, you need to know where it actually lives in your organization. That usually starts with an AI use-case inventory, which can be a simple register of which teams use AI, for what purpose, and with which data. It can be an off-shoot of your existing data map, but it should be expanded to include fields relevant for AI.

Here are some of the most common patterns that show up when organizations start looking for data AI touches inside their company.

Productivity and “copilot” tools

These are tools that help employees draft, summarize, translate, or brainstorm during their normal working day. They might include document assistants, email summarizers, coding copilots, and chat‑based interfaces that sit alongside office suites or IDEs.

The main risks are data leakage, over-collection, and over-reliance. People paste whatever is in front of them into prompts, such as contracts, customer lists, financial data, incident details, etc. without thinking about where it goes or whether the tool will retain it. They may also start to treat AI‑generated content as authoritative without human review.

Customer‑facing agentic assistants and chatbots

Many organizations are rolling out AI‑powered support agents, FAQ bots, or “ask us anything” widgets that sit on top of knowledge bases and user data.

Here, risks include inaccurate or discriminatory answers, unexpected profiling, and opaque decisions. If the agent has access to account or behavioral data, the privacy implications are even stronger. You have to think about what are you using that data for, what are you promising customers, and how could misuse of that data harm them?

Personalization, scoring, and recommendations

Recommendation engines, churn prediction models, upsell suggestions, “next best action” systems, and risk scoring tools often incorporate AI or advanced analytics. These uses raise questions about fairness, transparency, and data minimization. Some questions to consider are as follows: Are you using sensitive attributes (or proxies) in ways that could produce biased outcomes? Can you explain the logic of automated decisions where the law requires it? Are the data sources and retention periods appropriate to the purpose?

HR and recruiting tools

AI‑assisted resume screening, candidate scoring, internal mobility recommendations, and performance analytics introduce significant risks around discrimination, employee monitoring, and consent. 

In some jurisdictions, like California, employment decisions and worker surveillance are heavily regulated. That means these AI tools often need higher‑touch governance, including explicit review by legal and HR, more careful documentation, and sometimes prior consultation with worker representatives.

Code generation and security tooling

Engineering teams may use AI to generate code, write tests, or analyze logs and threats.

The privacy angle here includes data exposure in logs, incorporation of sensitive snippets into training data, and too much trust in AI‑generated security assessments. You’ll want to set expectations around what can data be shared for training and how code and log outputs must be reviewed.

The tip of the iceberg

These specific use cases are not the only categories where AI may be used inside your company. Marketing content generation, AI‑assisted research, and back‑office automation are everywhere. However, the above categories are a good starting point as you think through how AI is used inside your company. The key is to recognize that each use case has a different risk profile and may need a different level of review.

3. What AI Governance Looks Like Inside a Privacy Program

Once you’ve identified your main use cases, the question becomes: what do we actually do with this information?

You probably don't need an AI mega project unless AI is the entire foundation of your business or product, but you will need a few deliberate building blocks that sit comfortably alongside your existing privacy controls.

Inventory and classification

Start with an AI use-case register. For each use, capture who owns it, what systems and vendors are involved, what categories of data it touches, and what decisions or outputs it affects. Then, assign a rough risk level based on impact. Only you can fully determine your own risk, but we've provided a simple, sample risk tiering as follow: low (internal drafting assistance with no sensitive data), medium (customer communications, mild personalization), and high (employment decisions, credit/risk scoring, safety‑critical applications).

The risk classification will determine how much governance each use case should get. Not every experiment needs a full assessment, but high‑impact tools should not go live on the basis of “it seemed cool.”

Risk assessments and privacy‑by‑design

For higher‑risk use cases, adapt your existing privacy impact assessment (PIA/DPIA) process to include AI‑specific questions. Some questions to consider:

• What is the purpose of using AI here, and is it necessary?
• What data will be used for training, inputs, and outputs? Are any special categories involved?
• Can we minimize or anonymize data before it touches the tool or model?
• What are the possible impacts on individuals if the system is wrong, biased, or misused?
• Is a human meaningfully involved in decisions that significantly affect people?
• How will we monitor performance, drift, and complaints?

You don’t have to invent this from scratch. You can often bolt AI‑specific questions onto your existing DPIA templates and workflows. The aim is to ensure that AI is considered before deployment, not just after problems arise.

Vendor and contract management

Many AI use cases rely on third‑party vendors. Here, your vendor risk management and contracting standards become critical.

When reviewing AI vendors, focus on:

• How they handle training data and logs (Can you disable training on your data? What is retained, and for how long?)
• Their security posture of the vendor (Do they have access controls, a mature incident response plan and procedures, and any certifications?)
• Their willingness to support your regulatory obligations (How does their company handle DSR support, audit cooperation, and subprocessor transparency?)
• Clear prohibitions on using your data to train or improve models for other customers, if that’s your policy.

Contracts should reflect expectations in alignment with your values and policies. This means no training on customer data without explicit agreement, documented sub processors, incident notification SLAs, and assistance with rights requests where applicable.

Monitoring and continuous improvement

AI systems are not “set and forget.” They can drift, pick up bias, or be misused over time because the are "learning" and changing with additional use. Your governance framework should include, proportionate to risk:

• Periodic reviews of AI use cases. Are the AI tools still necessary? Are they still using the right data?
• Monitoring of key performance and fairness indicators where appropriate.
• A way for employees and customers to raise concerns or complaints about AI‑mediated decisions or interactions, with a clear path for review.

All of this mirrors what a good privacy program already does, which is inventory, assess, control, monitor, and improve. AI governance simply gives you a lens to apply those cycles to a new family of tools.

4. What to Put in an AI Acceptable Use Policy for Employees

Even with good governance at the system level, a lot of risk lives in everyday behavior. Employees experimenting with generative tools, forwarding outputs without review, or pasting sensitive information into prompts can undo carefully designed controls in seconds.

That’s where an AI Acceptable Use Policy (AUP) comes in. You have developed a set of systems and protocols, so make sure everyone understands them and agrees to follow them. 

Here are the building blocks to consider as you develop and roll out an AUP.

Scope and definitions

Start by explaining, in plain language, what you mean by “AI” in this context. Employees don’t need a textbook definition. They need to know which tools this policy covers, such as chatbots, content generators, code copilots, AI‑powered search and summarization, and embedded AI features inside the tools they already use. 

Clarify that the policy applies to both company-provided tools and personal use of AI in the ordinary course of their work, including free online services.

Approved and prohibited tools

Employees are more likely to comply when the rules are concrete because they have clarity on what they need to do. Identify the following in your AUP:

• Which AI tools are approved for work use (for example, a specific enterprise license of a model, or a governed integration inside your productivity suite).
• Which categories are prohibited without prior approval, such as free‑tier tools with unclear data use, browser plugins that scrape internal systems, or tools that route traffic through unknown infrastructure.

You don’t need an exhaustive list, but you should give people a default: “If it’s not on the approved list and you’re using it with company or customer data, ask first.”

Data classification and sharing rules

This is the heart of a privacy‑aware AUP. Spell out which kinds of information may never be pasted or uploaded into AI tools, and which may only be used under specific conditions.

Typically, you will forbid or tightly restrict sharing:

• Regulated or sensitive personal data (health, financial, children’s data, government IDs).
• Confidential customer information and company non-public information or trade secrets.
• Security‑sensitive information (credentials, internal architecture diagrams, vulnerabilities, incident details) or privileged information.
• Any data covered by strict contractual or regulatory restrictions.

You can allow lower‑risk data (e.g., public information, anonymized samples, generic descriptions) while insisting that anything sensitive either goes through an approved, enterprise‑governed platform or is not shared at all.

The more concrete the examples are, the better: “Do not paste full customer records into prompts” lands more clearly than “Avoid sensitive data.”

Human review and accountability

A good AUP makes it clear that AI is a drafting and thinking assistant, not a substitute for human judgment. Employees should understand that:

• They must critically review AI‑generated content for accuracy, bias, and appropriateness.
• They remain responsible for the work product they submit, even if an AI tool helped create it.
• In areas like legal, medical, financial, or HR decisions, AI cannot replace professional judgment and must not be presented as such.

You can also set expectations around transparency that AI was used. In certain contexts, colleagues or external audiences should know when AI has been used to generate content, especially if there is a risk of misunderstanding.

Intellectual property, confidentiality, and records

Your AUP should also touch on IP and confidentiality. Employees should know not to upload third‑party copyrighted materials in ways that violate license terms, and not to treat AI outputs as if they are inherently free from IP risk. The law remains unsettled on this last point, so careful review doesn't just mean checking for accuracy. It also means ensuring that the employee's own words are being used in contexts where the company is positioning the output as its content.

From a records perspective, consider whether AI‑mediated interactions that form part of business decisions need to be documented and retained for audit purposes. For example, final drafts, queries that led to key analytical outputs, or prompts that handle customer data may want to be saved for future review as part of a periodic AI audit.

Monitoring, support, and reporting

Finally, an AUP should explain how you will monitor compliance (which you should), where people can go with questions, and how to report concerns or potential misuse. Fear‑based policies tend to drive AI use underground, so a better approach is to make it easy for people to ask, “Is this OK?” and to admit mistakes early. You want employees to see the AUP as a safety rail, not a trap.

Bringing It All Together

Adding AI governance to your privacy program does not mean starting from scratch. It means you to take the structures you already have in place, like governance, data mapping, DPIAs, vendor risk, incident response, and training, and extend them thoughtfully to a new class of tools and use cases.

Practically, that translates to:

• Building a simple inventory of AI use cases and owners.
• Applying proportionate risk assessments to the higher‑impact ones.
• Baking reasonable requirements into your vendor and contracting processes through standard, approved language.
• Standing up an employee AI acceptable use policy that sets clear, realistic guardrails and a useful level of specificity.

Done well, AI governance doesn’t just keep you out of trouble. It helps your teams use AI confidently and responsibly, rather than in the shadows. It aligns experimentation with your values and obligations. Finally, it reinforces a message that should already be at the heart of your privacy program, which is that you take the stewardship of data seriously enough to plan hard, respond fast, and keep learning, no matter what new tools arrive.