AI Policies: A Seatbelt for Your AI Adoption

Cars are an essential part of our daily lives—and some cars are incredibly fun to drive. However, we can all agree that safety features such as seatbelts, airbags, and tire pressure monitors keep us safe as we use and enjoy cars. 

The exact same is true for AI. While we’re used to cars and have a long history with them, AI is still new and shiny. As our society navigates the intoxication phase of AI, we see new uses, tools, and innovations occurring every day while millions of people—from scientists and technologists to consumers just playing around with ChatGPT or Copilot—are relentlessly experimenting. 

In all the fun, it’s easy to overlook the AI version of seatbelts: Policies. 

It’s likely you’re embracing AI in some way at your organization, and it’s easy to just start using tools without thinking about wider repercussions. That’s where some baseline AI policies can give you a seatbelt and an airbag before you go too far and introduce unnecessary risk into your organization. 

In this article, we discuss four essential AI policies that your organization needs to mitigate the risk of AI misuse. 

1. AI Use Policy 

An AI Use Policy is a set of guidelines that explains how your employees are allowed to use AI tools inside your organization. It’s the equivalent of an IT acceptable use policy but tailored to AI’s unique risks and opportunities. 

You need this policy because: 

• Employees might accidentally share confidential information in a public AI tool.

• You may violate a regulation or get sued because you published incorrect information generated by AI.

• An employee might copy, paste, and publicly share AI-generated content that’s off‑brand, misleading, or inconsistent. 

Your AI Use Policy will serve as a playbook that covers: 

• Purpose and scope: Explain why the policy exists, to whom it applies, and what AI tools are covered.

• Approved AI tools: Clarify which tools are sanctioned, allowed with restrictions, or prohibited.

• Approved AI use cases: Clarify where AI can be used (such as drafting emails or analyzing documents) and where AI cannot be used (such as making disciplinary decisions or legal judgments).

• Data protection and privacy: Explain security requirements and data sanitization steps—while making it crystal clear what data can never be entered into AI tools.

• Accuracy, bias, and quality controls: Remind employees to always fact-check AI outputs, look out for bias, and avoid completely relying on AI to make a legal, medical, or financial decision.

• Ethical and responsible use: Include standard reminders to avoid causing harm, using AI for deception, and respecting intellectual property. 
  

2. AI Training Policy 

An AI Training Policy provides guidelines on how employees are trained to use AI tools responsibly, effectively, and safely while learning about the mechanics, risks, limitations, data use restrictions, and expectations around these tools. It’s a way to standardize AI knowledge across your organization so employees aren’t winging it. 

Your AI Training Policy should cover: 

• Purpose and scope: Describe what you want the training to achieve and who must complete the training.

• Approved training resources: Specify how the training will be delivered—whether it’s through workshops, certification programs, vendor-led instruction, internal training modules, etc.

• Required topics: Delineate the core content employees must learn before using AI.

• Performance expectations: Describe how you will measure performance such as employees passing training tests, receiving tool-specific certification, or demonstrating specific skillsets in their job.

• Frequency of training: Outline if training is one-off, periodic, and/or situation dependent. AI evolves quickly, so you may want to require periodic retraining and updates when changes occur. 

 3. AI Incident Response Policy 

An AI Incident Response Policy outlines how your organization identifies, reports, investigates, and resolves incidents involving the use of AI. You need this policy in case: 

• AI produces harmful, discriminatory, or unsafe outputs

• You leak sensitive data through AI tools

• There is an unauthorized use of AI systems

• AI system failures or unexpected AI behavior disrupts operations 

It’s just like a cybersecurity incident response plan, but focused on the unique risks and potential failures of AI tools. 

Your AI Incident Response Policy should cover: 

• Purpose and scope: Explain why this policy exists, what qualifies as an “AI incident,” and which tools, systems, and teams are covered.

• Incident classification levels: Create tiers such as critical (data breach), high (safety, security, or legal risk), medium (customer experience risk), and low (minor output error).

• Reporting procedures: Explain how employees should report suspected AI incidents, where to report them, and what information must be included.

• Roles and responsibilities: Make sure there is an AI governance team and points of contact for IT/cybersecurity, legal/compliance, and communications.

• Communication guidelines: To prevent misinformation or accidental admissions, clarify who can communicate about the incident internally, who can communicate externally, and how communications are handled with customers or partners.

• Post‑incident review: Ensure that you document what happened, why it happened, what was done, and what improvements will be implemented. 

4. AI Governance Handbook 

An AI Governance Handbook is a comprehensive guide that defines how AI tools are selected, used, managed, monitored, secured, and evaluated across your entire organization. It should act as the single source of truth that ensures AI aligns with your organization’s values, legal requirements, and organizational goals. 

Make sure your handbook includes the following items: 

• Introduction and purpose: Explain why the handbook exists, how AI supports your organizational goals, and who needs to read the handbook.

• AI strategy and vision: Outline your organization’s AI vision, strategic priorities and opportunities, and principles guiding your AI decisions.

• AI governance council: You should have a formal AI governance council that decides who evaluates, approves, and onboards new AI tools.

• Governance framework overview: Explain how your AI governance is structured, your decision-making processes, and any committees, councils, or working groups.

• AI policies and standards: You can include the AI policies above along with any other policies, guidelines, and standards in your handbook.

• Tool and technology standards: You can mention any approved AI tools and platforms, requirements for selecting new tools, and processes for third-party vendor due diligence.

• Data governance for AI: Define any data access rules and data quality standards.

• Human oversight expectations: Clarify where AI is meant to assist versus where humans make final decisions, high-risk use cases requiring stronger oversight, and expectations for reviewing AI outputs. 

With these four policies, you’ll have a strong foundation upon which you can build your AI strategy moving forward. To get started, we recommend using the NIST AI Risk Management Framework as an excellent model that follows industry best practices. 

Common Questions About AI Policies

Do small organizations really need AI policies?

Yes. Even limited AI use—such as drafting emails or summarizing documents—can introduce data, compliance, and reputational risks without clear policies.

Are AI policies required for compliance?

Many regulations and frameworks now expect organizations to demonstrate governance, risk management, and oversight of AI tools, especially when sensitive data is involved.

Can we just block AI tools instead of creating policies?

Blocking tools alone is rarely effective. Employees often find workarounds. Policies combined with training and technical controls are more effective.

How often should AI policies be updated?

AI policies should be reviewed at least annually and updated whenever new tools, regulations, or use cases are introduced.

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It’s important to note that policies alone aren’t enough. While they will set expectations and reduce ambiguity, they’re most effective when supported by technical controls and enforcement mechanisms to help prevent unauthorized or risky AI usage. 

That might include limiting AI use to approved tools, putting technical restrictions in place to prevent sensitive information from being shared, and having oversight processes that help catch and correct misuse early. In practice, policy defines the rules of the road while technical controls act as guardrails to ensure those rules are followed. 

TL;DR

AI policies help organizations use artificial intelligence safely, responsibly, and consistently. This article explains four foundational AI policies every organization should have:

• AI Use Policy: Defines which AI tools are approved, how employees can use them, and what data should never be shared.
• AI Training Policy: Ensures employees are properly trained on AI risks, limitations, and responsible use before using AI tools.
• AI Incident Response Policy: Establishes how to identify, report, and respond to AI‑related incidents such as data leaks or harmful outputs.
• AI Governance Handbook: Serves as a central guide for how AI is selected, managed, monitored, and aligned with organizational goals.
  

Together, these policies reduce risk, support compliance, and create a strong foundation for long‑term AI adoption.

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We can help you be safe, compliant, organized, and ready to adopt AI by giving you a complete, standards‑based blueprint for governance, risk, training, and responsible AI use. Reach out to us today.