Establishing an Acceptable Use Policy for AI
A Step-by-Step Guide to Crafting an Effective AI Usage Policy
Creating an AI usage policy can feel like herding cats in a siloed business environment. While an executive might declare, “We don’t use AI,” other teams could be experimenting with AI-powered solutions without formal approval. This can create a dangerous situation where AI is used without proper oversight, potentially leading to data breaches, compliance violations, or ethical issues.
Blocking AI entirely is like trying to stop the tide. It’s a reactive approach that often leads to unintended consequences. When employees are denied access to legitimate AI tools, they may turn to unofficial or unsafe solutions, increasing the risk of security breaches and data loss.
Instead of a blanket ban, provide a framework for responsible and ethical AI adoption. By establishing clear guidelines for data privacy, security, and ethical considerations, organizations can mitigate risks and harness the benefits of AI while maintaining control over its usage.
Don’t let perfection keep you from AI protection. A thoughtfully designed policy can help your organization navigate the complexities of AI usage today. Regardless of the status of your technical controls and licenses you can make progress.
Before we delve into the specifics of creating an AI usage policy, let’s establish a common understanding of key AI terms. These definitions will serve as one of the first inputs for your policy and ensure that everyone involved is on the same page.
- Artificial Intelligence (AI): The broad concept of using machines to perform tasks that would typically require human intelligence, such as learning, reasoning, problem-solving, and perception.
- Machine Learning (ML): A subset of AI that involves training algorithms to learn from data and improve their performance over time without being explicitly programmed.
- Large Language Model (LLM): A type of AI model trained on massive amounts of text data to understand and generate human language. LLMs are capable of tasks like translation, summarization, and creative writing.
- Generative AI: A subset of AI that focuses on creating new content, such as text, images, or audio, based on patterns learned from existing data. LLMs are a common example of generative AI.
- Hallucinations: Inaccurate or nonsensical output generated by AI tools, often due to limitations in the training data or underlying algorithms.
- Bias: The tendency of AI systems to exhibit unfair or discriminatory behavior, often reflecting biases present in the data used to train the models.
Establishing your policy definitions will help readers better grasp the implications of AI usage within your organization and let you tailor your policy to your particular organization and industry.
General AI Principles: The Foundation of Your Policy
Think of these principles as the bedrock upon which you’ll build your policy. They’re general guidelines to ensure your AI usage is ethical, responsible, and aligned with your organization’s values. Here are some key principles to consider:
- Limit Agency: AI should augment human capabilities, not replace them.
- Critical Review: Regularly evaluate AI outputs for accuracy, bias, and fairness.
- Human Oversight: Humans should always be in the loop to make decisions and intervene when necessary.
- Data Privacy: Protect user data and comply with relevant privacy regulations.
- Data Ownership: Clearly define who owns the data generated by AI systems.
- Transparency: Be open about the use of AI and its limitations.
- Accountability: Establish mechanisms to hold individuals and organizations accountable for AI-related issues.
Remember: These principles can be tailored to your specific industry and line of business. For example, a healthcare organization might prioritize patient privacy and safety, while a financial institution might focus on data security and compliance.
Approval Considerations: Who Calls the Shots?
You will need to establish a clear approval process. This process should align with your existing data governance and software request procedures. Here are some key factors to consider:
- Data Classification: Determine the sensitivity level of the data that will be used or generated by AI systems.
- Data Types: Consider the types of data that will be involved, such as prompts, completions, training data, validation data, results, and input data.
- Existing Policies: Assess how your AI usage policy will interact with other relevant policies, such as your data governance, data classification, privacy, software development, and software request procedures.
Next you will want to clearly outline approved language models, associated AI tools, and how users can interact and leverage their outputs. Your policy should consider whether deployments are local or remote, programmatic or user interactive, and the amount of agency AI output has.
This output should also be user friendly and concise for people to make informed and guided decisions. Here is an example table for your tooling standards and guidelines:
Reporting and Violations: Keeping Things in Check
A well-crafted AI usage policy should include clear guidelines for reporting and addressing violations. This will help ensure that AI is used safely and ethically within your organization. Consider the following:
- Reporting Procedures: Establish a process for reporting incidents related to AI usage, such as data breaches, biases, or unethical behavior.
- Violations: Define the consequences of violating the AI usage policy, including disciplinary actions or corrective measures.
Now, let’s get to work on drafting that policy!
Here is a prompt optimized for Creating an AI Usage Policy tailed for your organization. Simply paste this into your favorite AI website and you’re off to the races!
And finally I would like to cite the organizations and resources that I found helpful in the creation of this AI Prompt.
OWASP Generative AI Top 10 List
Cloud Security Alliance — Generative AI: Proposed Shared Responsibility Model
