Tools like ChatGPT and other generative AI platforms are now part of everyday life. Employees use them to draft emails, summarize documents, brainstorm marketing ideas and even write code. That productivity boost is real, but so are the risks. Without a clear AI policy playbook, you are relying on everyone’s individual judgment about what is safe, appropriate and compliant.
For most organizations, that is not good enough. Unmanaged AI use can lead to accidental data leaks, regulatory problems, misleading content and decisions made on top of output that was never properly reviewed. A practical AI policy playbook gives you a way to harness the benefits of generative AI while keeping your data, your customers and your reputation protected.
This AI policy playbook is built around five critical rules. Together, they help you decide where AI is allowed, what data can and cannot be used, who is accountable for oversight and how to keep your AI practices aligned with your overall security and compliance strategy.
Rule 1: Define Where AI Is Allowed (and Where It Is Not)
The first rule of any AI policy playbook is simple: be explicit about where AI is allowed and where it is off limits. “Use AI responsibly” is not guidance. People need concrete boundaries.
Map Your Business Use Cases
Start by listing how people are already using ChatGPT and other tools, even informally. Common examples include:
- Drafting emails, proposals and marketing copy
- Summarizing long documents or meeting notes
- Brainstorming ideas for campaigns or product names
- Creating outlines for internal documentation or training
- Helping with code snippets or simple scripts
Your AI policy playbook should identify which use cases are:
- Approved – safe, low-risk and encouraged
- Allowed with conditions – permitted, but only with certain safeguards
- Prohibited – not allowed due to data sensitivity, regulatory issues or business risk
Approved uses might include drafting or editing non-sensitive communications. Allowed-with-conditions uses might include summarizing documents that have had sensitive information removed. Prohibited uses should cover anything involving regulated data, confidential customer information or internal trade secrets.
High-Risk Areas That Need Extra Care
Some parts of your business are simply too sensitive for generative AI without very careful controls. Your AI policy playbook should explicitly call out areas such as:
- Legal and HR decisions about specific individuals
- Handling of regulated financial, health or student data
- Security configurations, penetration testing details or incident response plans
- Proprietary algorithms, source code or trade secrets
For these areas, the default stance should be “do not use public AI tools,” unless you have a tightly controlled, private deployment with appropriate contracts and technical safeguards in place.
Rule 2: Protect Sensitive Data and Privacy
The second rule in your AI policy playbook is to prevent sensitive data from being exposed through prompts. When people are under time pressure, it is easy to paste entire emails, contracts or screenshots into ChatGPT without thinking about what is inside.
Data Categories Employees Must Never Put into AI Tools
Your policy should clearly describe which types of data must never be entered into public AI tools. That list typically includes:
- Customer names, contact details and account numbers
- Financial records, payment card information and bank details
- Protected health information or other regulated personal data
- Employee performance reviews or sensitive HR information
- Non-public pricing, contracts or vendor terms
- Network diagrams, security settings and internal IP addresses
Instead of just saying “do not share confidential data,” spell out concrete examples. People follow policies more reliably when they can recognize the situations they are actually in.
Redacting and Anonymizing Before Prompting
Your AI policy playbook should also teach people how to safely prepare information before they prompt an AI tool. That may include:
- Removing names, IDs and contact details
- Replacing company names with generic labels (for example, “Client A”)
- Summarizing only the relevant sections instead of copying entire documents
- Using internal, approved AI tools for sensitive workloads instead of public ones
This is also where your overall security and compliance strategy comes into play. For many organizations, partnering with a security-first IT provider to define data categories and train staff is a smart move. If you do not have that support today, consider working with a team like ParJenn Technologies to align your AI usage with your existing policies.
Rule 3: Set Clear Ownership, Review and Accountability
The third rule of your AI policy playbook is to decide who owns AI governance and how AI-generated output will be reviewed. Without clear accountability, AI content can slip into customer-facing communication, contracts or reports without anyone taking responsibility for its accuracy.
Who Owns AI Policy at Your Organization
Every organization should have a named owner for AI governance. Depending on your size and structure, that might be:
- A CIO, CISO or IT director
- A compliance or risk officer
- A cross-functional committee with representation from IT, HR, legal and operations
The owner is responsible for keeping the AI policy playbook up to date, coordinating training and ensuring the policy reflects current regulations and risk management practices.
Approval Flows for AI-Generated Content
Your AI policy should explain when AI-generated output can be used directly and when it must be reviewed or edited. For example:
- Internal drafts: AI may draft emails, documentation or internal memos, but the sender is responsible for reviewing and editing before sending.
- Customer-facing materials: AI can assist with brainstorming and first drafts, but final versions must be reviewed and approved by an appropriate human owner.
- Any legal, financial or compliance content: AI may not be used as the final authority. Human experts must make the final judgment.
This keeps people from treating output from ChatGPT or other tools as “automatically correct” and reinforces that AI is a helper, not a decision-maker.
Rule 4: Control Access, Logging and Vendor Risk
The fourth rule in your AI policy playbook is about controlling who can use AI tools, which tools are approved and how usage is tracked over time. This is where IT, security and vendor management all come together.
Which AI Tools Are Approved
Your policy should include an approved-tools list that clearly identifies:
- Which AI platforms staff are allowed to use (for example, specific versions of ChatGPT or other generative AI tools)
- Which accounts to use (personal vs. corporate logins)
- Any enterprise agreements that change how data is stored or processed
Unapproved tools should be clearly flagged. Shadow IT, where employees bring in their own tools without review, is a major source of risk. Your AI policy playbook should give IT the authority to block or restrict tools that have not been evaluated.
When you are designing these controls, it helps to anchor them to a recognized standard. Many organizations look to the National Institute of Standards and Technology’s AI Risk Management Framework and its supporting resources as a reference point for risk-based AI governance.
Logging, Monitoring and Offboarding Access
Generative AI usage should be treated like any other critical business system. That means:
- Ensuring corporate accounts are tied to central identity and access management
- Logging access and, where appropriate, high-level usage patterns
- Removing access quickly when employees leave or change roles
Your vendor risk process should also extend to AI platforms. That includes reviewing terms of service, data handling practices, security certifications and incident response procedures. Public-sector examples, such as the Department of Homeland Security’s principles for ensuring AI is used responsibly, can be a useful model for developing your own internal expectations around safety, accountability and privacy.
Rule 5: Train, Test and Update Your AI Policy Regularly
The fifth rule of a durable AI policy playbook is recognizing that the technology, the risks and the regulations will continue to evolve. A one-time policy document that sits on a shelf is not enough.
Onboarding and Ongoing Training
Employees need more than a link to a policy PDF. Build AI usage education into your onboarding and ongoing training:
- Short training modules that explain what AI is and how your organization uses it
- Concrete examples of good prompts and bad prompts
- Real-world scenarios where AI went wrong and what should have happened instead
- Clear instructions on how to ask for help or report concerns
When the AI policy playbook is part of everyday conversations, not just an annual checkbox, people are more likely to follow it.
Incident Lessons → Policy Updates
Even with good rules, you should expect occasional missteps or near misses. Those incidents are valuable input for improving your AI policy playbook. When something goes wrong or almost goes wrong:
- Document what happened and why
- Update the policy with clearer language or new examples
- Share lessons learned (without blame) to help others avoid the same issue
Make AI governance part of your overall risk management cycle. If you already have regular reviews of security policies, vendor risks and compliance requirements, add AI usage to that agenda.
How ParJenn Technologies Can Help You Build an AI Policy Playbook
Generative AI is not going away. The organizations that benefit most will be the ones that treat it like any other powerful tool: with clear rules, thoughtful safeguards and ongoing oversight. An AI policy playbook gives you a framework for using AI to move faster without creating hidden risk.
If you do not have the time or internal resources to build this on your own, you do not have to start from a blank page. As a security-first IT partner, ParJenn Technologies can help you:
- Assess how employees are already using ChatGPT and other tools
- Develop a tailored AI policy playbook aligned with your industry and risk profile
- Integrate AI usage into your existing security, compliance and acceptable use policies
- Train your team on safe, practical ways to use generative AI in daily work
To explore what an AI policy playbook could look like for your organization, connect with ParJenn Technologies. Our security-focused services team can help you put guardrails around AI that support innovation instead of shutting it down.
