Policy layer
Enterprise principles
Define the organization’s expectations around accountability, safety, data responsibility, human oversight and acceptable use.
An enterprise AI policy framework should tell teams what AI can be used for, what data can be accessed, which actions require approval, how model and vendor choices are governed, and what happens when a production system moves outside its approved operating boundary.
Peak Demand designs AI policy frameworks to support real implementation. The policy should be specific enough to guide behaviour, but flexible enough to accommodate different enterprise workflows and risk levels.
When policy is vague, employees either avoid AI unnecessarily or use it in ways the organization never intended. A strong framework gives people clear boundaries while creating a path for higher-risk use cases to be reviewed and approved without slowing every low-risk workflow.
Define the organization’s expectations around accountability, safety, data responsibility, human oversight and acceptable use.
Clarify approved, restricted and prohibited activities for employees, contractors, agents and automated systems.
Define which categories of information can be used, where they may be processed and what must remain protected.
Specify which actions AI may perform autonomously and where deterministic validation or human approval is required.
Define how external models, platforms and tools are evaluated, approved and monitored for enterprise use.
Set expectations for monitoring, change control, incident handling, auditability and retirement.
Each domain should be specific enough to guide real decisions while avoiding unnecessary restrictions on low-risk, high-value use cases.
Define permitted, restricted and prohibited uses by employee role, business context and risk level.
Define which information may enter AI systems, what requires additional approval and how retention or residency requirements are handled.
Define what AI may read, recommend, draft, update, send, approve or execute and where human or deterministic controls are mandatory.
Define approved providers, evaluation expectations, security requirements, change review and exceptions for new models or tools.
Define when review, escalation, approval or user choice is required based on consequence, confidence and business context.
Define logging, incident handling, release review, policy exceptions, periodic reassessment and system retirement expectations.
| Category | Typical examples | Policy treatment |
|---|---|---|
| Approved | Drafting, summarization, internal search, low-risk classification and routine productivity use. | Allowed within approved tools and data boundaries. |
| Controlled | Customer communication, system updates, workflow automation and access to business-sensitive information. | Allowed with approved architecture, ownership, controls and monitoring. |
| Restricted | High-consequence decisions, sensitive data, financial commitments or automated actions with material impact. | Requires explicit risk review, approval and stronger controls. |
| Prohibited | Uses that violate law, contractual obligations, enterprise policy or approved authority boundaries. | Not permitted unless the policy itself is formally changed. |
May be used broadly in approved systems when the workflow does not create other policy concerns.
Should be limited to approved platforms and workflows with appropriate access, retention and sharing boundaries.
Requires stronger restrictions, approved providers and a clear business purpose before entering model context.
Should only be used where necessary, authorized and appropriately protected for the specific workflow.
Should never be placed casually into prompts or model context and should remain in secure credential systems.
Important actions should continue to rely on authoritative enterprise systems rather than generated text or conversational memory.
AI may retrieve approved information without changing enterprise records.
AI may suggest a decision or next step while a person retains authority.
AI may prepare content or records for human review before submission or publication.
AI may execute approved actions when deterministic checks confirm the request is allowed.
AI may prepare a higher-consequence action but cannot execute until an authorized person approves it.
Reserved for workflows where risk, controls, evidence and reversibility justify broader authority.
Make it obvious which platforms are approved for enterprise use and where employees can find them.
Use concrete examples of what information may or may not be entered into approved or public AI systems.
Define when employees are expected to review AI output against source-of-truth information.
Clarify which decisions remain human even when AI provides analysis or recommendations.
Provide a clear route for unusual requests, policy questions, sensitive use cases and potential incidents.
Make clear that using AI does not remove employee responsibility for actions they are authorized to approve or execute.
| Policy area | What to define | Why it matters |
|---|---|---|
| Approved providers | Which model, platform or service providers are authorized for defined classes of work. | Prevents uncontrolled shadow AI while preserving usable options. |
| Security review | Minimum security, privacy, data-processing and access expectations. | Vendor choice changes the system's data and operational risk. |
| Evaluation | How quality, latency, cost, tool use and workflow fit are tested before production. | Model reputation alone does not prove workload suitability. |
| Change management | How major model or platform changes are tested and approved. | Provider updates can alter production behaviour. |
| Exceptions | Who can approve a provider outside the standard list and under what conditions. | Allows flexibility without abandoning governance. |
Use authentication and service identity to control who or what can access protected workflows.
Scope tools, systems and data so the AI only has the authority required for the approved use case.
Use deterministic rules to block requests that violate eligibility, policy or transaction constraints.
Require authorized human confirmation before restricted actions move forward.
Capture enough workflow evidence to review whether policy was followed in production.
Version material changes so policy-impacting updates can be reviewed before release.
Document the proposed use, business need, data, systems and reason the standard policy is insufficient.
Evaluate consequence, data sensitivity, autonomy, reversibility and available controls.
Assign an authorized decision owner and document any conditions required for use.
Implement the additional permissions, validation, oversight or monitoring required by the exception.
Reassess the exception after a defined period or when the workflow materially changes.
Owns enterprise risk appetite, policy mandate and alignment with business strategy.
Maintains the framework, intake, exception process and relationship between policy and production controls.
Defines requirements for data, access, retention, vendors and sensitive use cases.
Translate enterprise policy into workflow-specific rules, exceptions and operational expectations.
Implement policy as identity, permissions, validation, observability and controlled deployment.
Use approved systems within policy and escalate uncertain or restricted use cases instead of improvising.
Check whether new business uses are emerging outside the assumptions of the current framework.
Reassess approved platforms, model changes, data practices and enterprise fit.
Determine whether an incident exposed an unclear rule, missing control or unowned decision.
Review whether new data, actions, models or permissions alter the approved operating boundary.
Update policy language, roles, risk tiers, approved tools and exception processes.
Use real questions and edge cases to make the policy clearer and more usable over time.
Define acceptable use, data, authority, oversight, vendor and production-operation domains.
Match policy requirements to consequence, autonomy, data sensitivity and operational exposure.
Implement identity, permissions, deterministic validation, approval gates and system boundaries.
Map what data is needed, where it lives, who can access it and what remains authoritative.
Support logging, release review, model changes, exception handling and incident response.
Translate enterprise rules into role-specific operating guidance employees can apply in real workflows.
Rollout should translate policy language into role-specific guidance, practical examples and an obvious path for questions. The objective is not to make every employee a governance expert. It is to make the approved behaviour easy to recognize.
Give employees a clear summary of approved tools, data rules, prohibited uses and the exception process.
Differentiate guidance for general employees, managers, developers, administrators and high-risk workflow owners.
Show practical allowed and restricted scenarios that reflect the work employees actually perform.
Employees are more likely to follow policy when approved AI access is straightforward and fit for purpose.
Teams need a fast route for legitimate use cases that fall outside the standard policy instead of improvising around it.
Managers should know how to answer common questions and escalate unusual cases before risky behaviour becomes normal.
Employees use AI with inconsistent guidance, tooling and data practices.
The organization defines approved and restricted use, but enforcement remains mostly behavioural.
Employees have clear access to sanctioned AI systems and practical data-handling rules.
Identity, permissions, validation and auditability reinforce policy in production systems.
Higher-risk uses move through structured review without forcing every low-risk workflow into the same process.
The framework evolves with incidents, new vendors, regulation, new workflows and expanding AI authority.
An AI policy framework is the set of enterprise rules governing acceptable use, data handling, authority, human oversight, model and vendor use, monitoring, change control and accountability across AI systems.
It should cover approved and prohibited uses, data categories, model and platform rules, action authority, human review, security expectations, auditability, incident response, exception handling and policy ownership.
No. The framework should be enterprise-wide, but review and control requirements should scale with the consequence and sensitivity of the specific workflow.
Make approved tools easy to access, define clear data rules, communicate practical examples, monitor where appropriate and provide an exception path for legitimate needs.
Define authority levels explicitly and require stronger deterministic validation, permissions, human approval and auditability as consequence increases.
Review the framework periodically and after material incidents, new vendors, new data uses, significant workflow changes or expanded AI authority.
Yes. Enterprise standards can be centralized, but business process owners need responsibility for workflow-specific rules, exceptions and acceptable outcomes.
Yes. Peak Demand can help structure policy requirements and implement the identity, permissions, validation, integration, approval, audit and operating controls needed to make them real in production.
Peak Demand can help structure the enterprise policy, map it to real workflows, and implement the controls required to make those rules operational.