Not governance
A generic AI policy
Policy is necessary, but it is incomplete if nobody can map the language to production permissions, controls and workflow decisions.
Enterprise AI governance is not a policy document sitting beside the technology. It is the operating system for deciding what AI is allowed to do, which data it can use, who owns the outcome, where human approval is required, how changes are reviewed and how production behaviour is made auditable.
Peak Demand approaches governance as part of production architecture: policy defines intent, deterministic controls enforce authority, and operating ownership keeps the system governable after launch.
Enterprises need more than acceptable-use language. They need clear ownership, risk classification, data boundaries, permissions, approval logic, auditability, model-change processes and operational accountability that can survive real deployment conditions.
Policy is necessary, but it is incomplete if nobody can map the language to production permissions, controls and workflow decisions.
If every low-risk change requires central review, teams route around governance instead of using it.
High-consequence rules should not depend on the model remembering an instruction every time.
Internal summarization and autonomous customer transactions should not be governed as if they create the same consequence.
Enterprise governance still needs to cover the workflow, integrations, permissions, data and operational use around the model provider.
Models, vendors, data, business rules and system authority change. Governance has to operate continuously.
The objective is to make governance visible in both the operating model and the system architecture. Each layer answers a different question about who can decide, what can happen and how the enterprise proves control.
Define acceptable use, prohibited use, risk appetite, data expectations, transparency principles and enterprise-level boundaries for AI.
Classify workflows by consequence, data sensitivity, autonomy, customer impact, regulatory exposure and reversibility.
Define who can approve use cases, data access, model choices, business-rule changes, authority expansion and production releases.
Implement identity, permissions, validation, deterministic rules, approvals, rate limits, tool boundaries and safe failure handling.
Capture relevant inputs, tool calls, decisions, failures, outcomes, escalations and changes so production behaviour can be reconstructed.
Review incidents, model changes, new risks, business outcomes and control effectiveness throughout the life of the system.
| Risk level | Typical use | Governance expectation |
|---|---|---|
| Low | Internal drafting, summarization, low-sensitivity knowledge assistance. | Approved tools, data boundaries, user accountability and basic monitoring. |
| Moderate | Customer communication, workflow classification, document extraction or recommendation. | Defined ownership, quality evaluation, logging, escalation and explicit source-of-truth rules. |
| High | AI that writes to enterprise systems, makes commitments, changes records or triggers downstream actions. | Strong identity, permissions, deterministic validation, approved tools, auditability and rollback. |
| Critical | Highly consequential, regulated, safety-sensitive or financially material actions. | Explicit human approval, restricted authority, rigorous validation, incident planning and senior risk ownership. |
The goal is proportional governance: stronger controls where consequences are higher, and lighter-weight paths where risk is genuinely low.
Portfolio or business leadership decides whether the workflow belongs in the enterprise AI portfolio and whether the expected value justifies the risk.
Data and security owners decide what sources may be accessed, under what purpose, and with what minimum-necessary boundaries.
AI engineering evaluates model quality, latency, cost, security, context limits and production fit before release.
Business and risk owners define which actions the system may take autonomously, which require validation and which require human approval.
Material changes to business rules, prompts, models, tools or permissions should follow a controlled production process.
Operations needs authority to disable, reroute, roll back or contain the system when production behaviour becomes unsafe or unreliable.
| Governance intent | Production control | Evidence |
|---|---|---|
| Only authorized users can trigger the workflow | Authentication, service identity and role-based permissions. | Access logs and authorization records. |
| AI may only access approved data | Scoped credentials, API permissions, retrieval boundaries and data filters. | Tool-call logs and source access records. |
| AI cannot bypass business rules | Deterministic validation and transaction logic outside the model. | Validation logs, rule outcomes and rejected actions. |
| High-consequence actions require approval | Human-in-the-loop approval gates before execution. | Approval event, approver identity and action record. |
| Failures must be reviewable | Structured logging, traces, error capture and workflow state history. | Incident record and reconstructable execution path. |
| Changes must be controlled | Versioning, staging, evaluation and rollback before production release. | Release history, test evidence and change approvals. |
Verify the user, service or caller before allowing access to protected data or actions.
Scope tools and data access to the minimum authority the workflow actually requires.
Validate schemas, fields, eligibility, transaction rules and source-of-truth state before execution.
Restrict volume, amount, frequency, scope or other consequence-bearing actions.
Pause higher-consequence actions until an authorized human confirms the next step.
Define safe stop, retry, rollback, compensation and escalation behaviour for downstream failures.
Define why the workflow needs the data and whether the model actually requires all of it.
Scope credentials and retrieval so the AI receives only the information required for the task.
Identify which structured system remains authoritative when model output or conversational context conflicts with enterprise records.
Define what is logged, how long it is retained and which data should not persist beyond the workflow.
Map where data is processed and stored when geography or contractual requirements matter.
Reassess permissions when workflows, vendors, models or organizational roles change.
Separate read-only assistance from workflows that can create or modify enterprise records.
Define how data and derived records are removed or archived when the workflow or engagement ends.
Requiring manual review of every AI output can eliminate the value of automation. The better model is selective oversight: humans intervene where judgement, policy, confidence or consequence warrants it.
Escalate when the system cannot establish enough certainty to proceed safely.
Move to a person when the request falls outside the normal workflow or approved rule set.
Require explicit human approval before materially consequential actions are executed.
Escalate when model interpretation conflicts with source-of-truth data or downstream system state.
Preserve a clear path to a person where service design, accessibility or customer expectation requires it.
Human reviewers should receive the relevant history, facts, tool state and reason for escalation.
Capture the relevant facts and context that informed the workflow without retaining unnecessary data.
Record which systems were called, what action was requested and whether the call succeeded.
Keep evidence of deterministic checks, rejected actions and business-rule outcomes.
Capture who approved an action, when it happened and what was approved.
Know which model, configuration or workflow version was active when an outcome occurred.
Record whether the workflow completed, escalated, failed, rolled back or produced a downstream result.
Define the workflow, consequence, data sensitivity and proposed authority.
Assign owners, approve data and define required controls before implementation.
Translate governance into permissions, validation, approval gates, observability and workflow logic.
Test normal cases, edge cases, prohibited actions, system failures and human escalation.
Monitor incidents, changes, outcomes, controls and evolving business requirements.
Remove access, archive required evidence and decommission systems when the workflow ends.
| Model lifecycle step | Governance question | Required evidence |
|---|---|---|
| Selection | Is this model appropriate for the workflow, data, latency and risk profile? | Evaluation criteria, vendor review and architecture decision. |
| Benchmarking | Does it perform reliably across representative production scenarios? | Test set results, edge-case performance and tool-use evaluation. |
| Release | Can the change be deployed without creating unacceptable regression risk? | Staging results, approvals, rollback plan and release record. |
| Monitoring | Did behaviour, cost, latency or workflow outcomes change after release? | Production metrics, incident data and business KPI comparison. |
| Replacement | Would another model materially improve quality, cost, control or data handling? | Comparative evaluation and migration plan. |
Review incidents, control failures, escalations and open production risks.
Compare business outcomes, reliability, error patterns, cost and control effectiveness.
Reassess risk classifications, vendors, models, data access and authority across production systems.
Review material model, prompt, tool, permission, policy and workflow changes before release.
Determine what failed, why controls did or did not catch it and what must change.
Update policy, risk appetite, roles, control standards and operating expectations as maturity grows.
How many production workflows have the required identity, permissions, validation and approval controls for their risk tier.
How often AI systems create material failures, unauthorized actions or control violations.
Whether known failure patterns are actually being eliminated through better controls and operating practices.
How quickly low- and moderate-risk projects can move through governance without unnecessary friction.
Whether the enterprise can reconstruct important production events with sufficient evidence.
Whether human reviewers receive enough context and intervene at appropriate points.
How often model or workflow releases introduce material regressions or require rollback.
Whether teams view governance as a usable delivery framework rather than something they need to avoid.
Map policy, risk tiers, decision rights, ownership and governance cadence around the enterprise AI portfolio.
Implement identity, permissions, deterministic validation, approval gates, system boundaries and observability.
Define what data the workflow needs, where it comes from, what can be written and what must remain authoritative.
Verify prohibited actions, policy exceptions, unavailable systems, low-confidence cases and human escalation.
Support logging, incident ownership, change control, model evaluation and production evidence.
Use production evidence to decide where permissions, automation and workflow coverage can safely increase.
Enterprise AI governance is the set of policies, decision rights, risk classifications, technical controls, audit practices and operating processes used to manage how AI systems are designed, deployed, changed and monitored across the organization.
Policy states what the organization expects. Governance turns those expectations into ownership, approvals, controls, monitoring and evidence that can be applied to real production systems.
No. Governance should be proportional to consequence. Low-risk assistive use cases can follow lighter processes, while systems with sensitive data, system-write access or high-consequence actions require stronger review and controls.
Identity, permissions, validation, business rules, transaction limits, approval gates and other high-consequence authority should generally be enforced through software rather than relying only on model instructions.
Logging should be appropriate to the workflow and may include model or workflow version, tool calls, validation outcomes, approvals, failures, escalation and final business outcomes while respecting data-minimization requirements.
Use selective oversight based on consequence, confidence, policy exceptions, system conflicts and user needs rather than forcing humans to review every low-risk output.
No. Models, vendors, integrations, business rules and risk change over time. Governance should include recurring review, controlled releases, incident learning and periodic reassessment.
Yes. Peak Demand can help define governance operating models and implement the technical control layer around identity, permissions, validation, integrations, auditability, human oversight and production operations.
Peak Demand can help define governance requirements, map risk and decision rights, implement deterministic controls, and build the production architecture required to make enterprise AI governable in practice.