You built a customer support chatbot for a financial services client. Six months later, you discover that users have been asking it for investment advice—and it has been giving it. The chatbot was never designed for financial advice, never tested for accuracy on investment questions, and never cleared by compliance. But without a clear acceptable use policy, nobody knew where the boundaries were.
Acceptable use policies define what an AI system should and should not be used for. They protect the client from misuse liability, protect users from harmful outcomes, and protect your agency from building a system that is used in ways you never intended or tested.
Why Acceptable Use Policies Matter
Preventing Scope Creep in Usage
AI systems are flexible by nature. A chatbot designed for customer support can technically answer questions about anything. Without explicit boundaries, users will push the system beyond its intended scope—into areas where it has not been tested, is not accurate, and may create liability.
Regulatory Protection
Regulators hold organizations accountable for how AI systems are used, not just how they are built. An acceptable use policy demonstrates that the organization has defined appropriate usage boundaries and communicated them to users.
Liability Management
If an AI system causes harm because it was used outside its intended scope, the acceptable use policy is evidence that the organization defined appropriate limits. Without one, the organization has no defense against claims that they failed to manage usage risks.
User Protection
Users benefit from knowing what the AI system can and cannot help with. Clear boundaries prevent users from relying on the system for tasks it is not designed to handle.
Policy Components
Section 1: Purpose and Scope
Define what the AI system is designed to do:
- What specific tasks or workflows the system supports
- Who the intended users are
- What types of inputs the system is designed to handle
- What types of outputs the system produces
Be specific. "This system is designed to help customer support agents respond to billing inquiries by providing relevant policy information and suggested responses" is much more useful than "This system provides AI-powered customer support."
Section 2: Permitted Uses
List the specific ways the system should be used:
- Answering customer questions about billing, account status, and service features
- Generating draft responses for agent review before sending
- Classifying incoming support requests for routing
- Summarizing customer conversation history for agent context
Section 3: Prohibited Uses
List specific uses that are not permitted:
General prohibitions:
- Making binding commitments to customers without human approval
- Providing advice in regulated domains (legal, medical, financial, tax) unless specifically designed and approved for that purpose
- Processing data types the system was not designed to handle (for example, sensitive health information in a general support bot)
- Using the system to make automated decisions that significantly affect individuals without human oversight
- Attempting to circumvent the system's safety controls or output filters
Domain-specific prohibitions (examples for a support chatbot):
- Providing investment or financial planning advice
- Making promises about service levels or guarantees not in official policy
- Sharing information about other customers
- Processing payment information directly
- Diagnosing technical issues that require on-site assessment
Section 4: User Responsibilities
Define what users are expected to do:
- Review AI outputs before acting on them or sharing with customers
- Report outputs that seem incorrect, inappropriate, or unexpected
- Follow escalation procedures when the AI cannot adequately address a request
- Not share login credentials or allow unauthorized access to the system
- Complete required training before using the system
- Comply with all applicable policies and regulations when using AI-generated outputs
Section 5: Data Handling Requirements
Define how data should be handled when using the system:
- What types of data can be input to the system
- What data should never be input (sensitive categories, personal information of third parties)
- How outputs containing personal data should be handled
- Data retention expectations for inputs and outputs
- Reporting requirements for data incidents
Section 6: Oversight and Monitoring
Describe the monitoring and oversight mechanisms:
- How system usage is monitored
- What metrics are tracked and reviewed
- Who reviews system performance and usage patterns
- How policy violations are detected and addressed
- Audit schedule and procedures
Section 7: Incident Reporting
Define the process for reporting issues:
- What constitutes a reportable incident (incorrect output, inappropriate response, potential bias, data concern)
- How to report incidents (channel, format, urgency classification)
- Expected response times for different incident types
- Who investigates reported incidents
- How findings are communicated back to the reporter
Section 8: Enforcement
Define consequences for policy violations:
- Warning for first unintentional violations
- Additional training for repeated unintentional violations
- Access revocation for intentional violations
- Escalation procedures for serious violations
- Documentation requirements for all enforcement actions
Implementation Strategies
Technical Enforcement
Where possible, enforce acceptable use policies technically:
Input filtering: Detect and block input that falls outside permitted use. If the chatbot should not handle investment questions, implement topic detection that redirects those queries.
Output filtering: Detect and block output that violates policy. If the system should not make binding commitments, filter for language that sounds like a commitment.
Access controls: Restrict system access to authorized users. Implement role-based access that limits what each user type can do.
Usage monitoring: Automatically track usage patterns and flag anomalies. Alert when usage suggests the system is being used outside its intended scope.
Training and Communication
Technical enforcement is necessary but not sufficient. Users need to understand the policy:
Training program: Include acceptable use policy review in user training. Make it practical—use examples of permitted and prohibited uses that are relevant to the user's role.
In-system reminders: Display acceptable use reminders in the system interface. Brief, contextual reminders are more effective than long policy documents.
Regular communication: Periodic reminders about acceptable use policies, especially when policies change or when monitoring detects increasing boundary-pushing.
Policy Maintenance
Acceptable use policies are not static. Update them when:
- The system's capabilities change (new features or expanded scope)
- New use patterns emerge that were not anticipated
- Regulations change, requiring new restrictions
- Incidents reveal gaps in the existing policy
- Client feedback suggests the policy is too restrictive or not restrictive enough
Review the policy at least quarterly and update as needed.
Building Policies Into Your Delivery
Discovery Phase
During discovery, work with the client to define intended use:
- What specific problems will the AI system solve?
- Who will use it and in what context?
- What should the system absolutely not be used for?
- What regulatory constraints apply to usage?
- What existing policies (IT, security, compliance) need to align?
Development Phase
Build policy enforcement into the system:
- Implement input and output filtering for prohibited uses
- Build monitoring for policy compliance
- Create admin interfaces for policy management
- Include policy-related logging for audit trails
Deployment Phase
Deliver the policy alongside the system:
- Final acceptable use policy document approved by client stakeholders
- Training materials that cover the policy
- Technical enforcement mechanisms active
- Monitoring dashboards operational
- Incident reporting channel established
Maintenance Phase
Support ongoing policy management:
- Monitor for policy violations and report findings
- Recommend policy updates based on usage patterns
- Implement technical changes when policies are updated
- Support client in communicating policy changes to users
Drafting Tips
Be specific: "Do not use for financial advice" is more useful than "use responsibly."
Use examples: For each prohibited use, provide an example of what it looks like in practice.
Write for the user: The policy should be understandable by the people who use the system, not just the lawyers who review it.
Keep it concise: A 20-page acceptable use policy will not be read. Keep the core policy to 2-3 pages with supplementary detail available for reference.
Make it accessible: The policy should be easy to find—linked from the system interface, included in training materials, and available in the knowledge base.
Acceptable use policies are the governance layer that makes AI systems safe to deploy. Without them, AI systems are open-ended liabilities. With them, usage boundaries are clear, violations are detectable, and the organization can demonstrate responsible AI deployment to regulators, customers, and the public.