Processes for responsible use of AI systems

To meet the ISO/IEC 42001 Annex A.9.2 requirement for “processes for responsible use of AI systems,” you must put defined, repeatable operational processes around how AI is requested, approved, used, monitored, and escalated—so AI use stays within intended purpose, policy, and human oversight. Auditors will look for documented guidance, oversight controls, and proof they work in day-to-day operations. ¹

Key takeaways:

• You need more than an “AI policy”; you need workflow-level processes for approval, oversight, and human intervention. ¹
• “Responsible use” must be operationalized through roles, permitted-use rules, monitoring, and incident/escalation paths. ¹
• Evidence matters: keep approvals, training completion, usage logs (where applicable), exceptions, and post-incident reviews tied to specific AI systems and use cases. ¹

Annex A.9.2 is a deceptively short control with a heavy operational burden: “responsible use” fails most often at the edges, where staff adopt new AI tools, reuse models for unintended purposes, or automate decisions without adequate human review. Your job, as CCO/GRC lead, is to translate the control into processes that a business team can follow without improvisation.

Treat this requirement as a process design problem across the AI system lifecycle: intake (what are we trying to do), decisioning (is the use allowed and appropriately controlled), deployment (how it’s made available), operations (how it’s monitored and corrected), and decommissioning (how it’s retired and access removed). The required processes should cover both AI you build and AI you consume from third parties, because “use” includes employees interacting with external AI systems in daily work.

This page gives requirement-level implementation guidance you can implement quickly: who it applies to, step-by-step process design, evidence to retain, common audit traps, and a practical execution plan you can run as a compliance program workstream. ¹

Regulatory text

Control requirement (Annex A, Control A.9.2): “The organization shall establish processes to ensure AI systems are used responsibly.” ¹

Operator interpretation: You must define and run documented processes that govern AI use in practice, not just in principle. At minimum, the processes should include:

• Guidelines for appropriate use (permitted vs. prohibited activities, constraints, and user duties)
• Oversight mechanisms (clear accountability, review/monitoring, and compliance checks)
• Human intervention procedures (how humans supervise, override, pause, or roll back AI-driven outcomes) ¹

Auditors will test whether these processes are (1) written, (2) adopted by the business, and (3) evidenced through records tied to specific AI systems and use cases. ¹

Plain-English requirement: what “responsible use” means operationally

“Responsible use” means you constrain AI use to approved purposes, approved data handling, and approved decision authority, with humans accountable for outcomes. The control is satisfied when employees and systems have:

• Clear guardrails (what the AI can/can’t be used for; what data can/can’t be entered; what decisions can/can’t be automated)
• A decision process for approving AI use cases and material changes
• Ongoing oversight to detect misuse, drift from intended purpose, or harmful outcomes
• A human intervention path that is real (trigger criteria, responsible roles, and documented actions) ¹

Who it applies to

This requirement applies to:

• Organizations using AI internally (employee productivity tools, support bots, analytics, decision support)
• Organizations providing AI-enabled products/services (customer-facing AI, embedded AI features)
• Organizations consuming third-party AI (SaaS features marketed as “AI,” external LLM tools, outsourced model services) ¹

Operationally, it applies anywhere AI output can affect:

• Customer outcomes (advice, eligibility, pricing, prioritization, support)
• Internal decisions (HR screening, fraud triage, security operations, finance)
• Content generation (marketing claims, legal drafts, code generation)
• Data handling (prompts containing sensitive data, retention, re-use by providers)

If you have multiple business units, apply the same minimum processes enterprise-wide, then add stricter rules for higher-impact use cases. ¹

What you actually need to do (step-by-step)

Use the steps below as a build sheet for your “processes for responsible use of AI systems” control.

1) Establish ownership and a RACI for AI use

Create explicit accountability for:

• AI system owner (business accountability for intended use and outcomes)
• Technical owner (operations, configuration, access, monitoring)
• Compliance/Legal (policy requirements, restrictions, approvals)
• Security/Privacy (data classification rules, access control expectations)
• Human-in-the-loop approver (required when AI output can drive decisions or external commitments) ¹

Deliverable: a RACI mapped to AI systems and to the intake/approval workflow.

2) Publish “appropriate use” rules that are enforceable

Write rules users can follow without interpretation:

• Permitted use cases (e.g., drafting internal summaries)
• Prohibited use cases (e.g., generating final customer commitments without review)
• Data handling rules (what can be entered into AI tools; how to redact; what requires approval)
• Output handling rules (fact-checking expectations; labeling; citations when required)
• Access and segregation rules (who can use what tools; which environments are allowed) ¹

Make this operational: embed the rules in request forms, tooling banners, and training.

3) Implement an AI use-case intake and approval workflow

Create a standardized intake (ticket or form) that captures:

• Intended purpose and users
• AI system/tool involved (including third party)
• Data types involved
• Whether output affects customers, employees, or regulated decisions
• Required human review checkpoints
• Rollback/pause plan if harm or misuse is detected ¹

Approval should be risk-based:

• Low-impact internal use: streamlined approval with documented acknowledgment of rules
• Higher-impact use: formal review by compliance/privacy/security plus explicit human-intervention design

4) Define human oversight and intervention procedures

Document the “human intervention” mechanics:

• What triggers intervention (complaints, anomalies, known failure modes, policy violations)
• What actions are allowed (override output, require second review, suspend the feature, roll back to a prior version, disable access)
• Who can execute actions and who must be notified
• How you document the decision and lessons learned ¹

Key operational detail: define what “stop the line” looks like for each AI system (kill switch, config toggle, access removal, feature flag, vendor escalation).

5) Put monitoring and review into BAU operations

Responsible use fails when approvals happen once and are never revisited. Add recurring operational checks:

• Access reviews for AI tools and privileged functions
• Review of exceptions (who got approved to do what, and why)
• Sampling of AI outputs in higher-risk processes for adherence to policy (where feasible)
• Incident tracking for misuse, hallucinations causing harm, or data leakage via prompts ¹

If you cannot monitor outputs directly, document compensating controls: stronger gating, mandatory human review, narrower scopes, and tighter data entry rules.

6) Manage third-party AI as part of responsible use

For AI that is provided by a third party, your responsible-use process must include:

• Confirming intended use aligns with the provider’s terms and your internal rules
• Configuring tenant controls (retention, sharing settings, admin access)
• Ensuring user guidance reflects tool-specific risks (what data is prohibited, where outputs can be used)
• Vendor escalation and suspension steps if the tool behaves unexpectedly or creates policy violations ¹

Practical tip: treat each external AI capability as an “AI system” for the purposes of your register, ownership, and approval workflow.

Required evidence and artifacts to retain

Keep artifacts that prove the processes exist and are followed:

• AI acceptable use standard (permitted/prohibited uses; data handling rules) ¹
• AI system inventory or register with owners and intended purposes ¹
• Use-case intake forms and approvals (including review notes and conditions) ¹
• Human intervention runbooks (triggers, roles, actions, communication steps) ¹
• Training materials and completion records for relevant staff ¹
• Exception log (who approved, why, duration/conditions, review outcomes) ¹
• Incident/issue records tied to AI use and corrective actions ¹
• Third-party due diligence records for AI tools that materially affect operations ¹

If you use Daydream to manage third-party risk and control evidence, map each AI tool provider to the responsible-use controls, store approvals and exceptions as evidence, and route renewals through the same responsible-use review so the process survives tool sprawl.

Common exam/audit questions and hangups

Expect auditors to probe these areas:

• “Show me your documented process for approving new AI use cases.” ¹
• “How do you define ‘responsible use’ for staff? Where are prohibited uses documented?” ¹
• “Where is human intervention required, and how does it work in production?” ¹
• “Prove the process is followed: give examples of approved, rejected, and exception cases.” ¹
• “How do you control employee use of third-party AI tools?” ¹

Common hangup: teams show a policy but cannot show operational records (tickets, approvals, monitoring outputs, or incident runs).

Frequent implementation mistakes (and how to avoid them)

1. Mistake: treating this as a one-time policy publish.
   Fix: require intake/approval for new use cases and material changes; retain the ticket trail. ¹

2. Mistake: no defined human intervention path.
   Fix: create system-specific runbooks with a named owner, action steps, and escalation contacts. ¹

3. Mistake: “responsible use” rules that are too abstract.
   Fix: write examples: “Allowed: summarize internal meeting notes without client data. Not allowed: paste client contracts into external AI tools.” ¹

4. Mistake: ignoring third-party AI features embedded in existing tools.
   Fix: update the AI inventory and approval scope to include AI features turned on in SaaS platforms. ¹

5. Mistake: exceptions handled in email/Slack with no record.
   Fix: maintain a formal exception log with approver, scope, conditions, and review evidence. ¹

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement, so this page does not cite specific actions. Practically, weak responsible-use processes increase the likelihood of preventable harms: unauthorized disclosure of sensitive data via prompts, unreviewed AI-generated commitments to customers, discriminatory or incorrect decision support, and operational incidents that your team cannot quickly contain because there is no intervention procedure. ¹

A practical 30/60/90-day execution plan

First 30 days (foundation)

• Appoint owners and publish the AI RACI by system and use case. ¹
• Stand up an AI inventory with “intended use” fields and third-party identification. ¹
• Draft and issue the appropriate-use rules with data handling constraints and prohibited uses. ¹

By 60 days (process live)

• Launch the use-case intake and approval workflow; require it for new AI deployments and new third-party AI tools. ¹
• Implement exception handling and documentation (central log plus approval conditions). ¹
• Train targeted user groups (developers, product, support, HR/finance analysts, anyone using external AI tools). ¹

By 90 days (oversight and assurance)

• Publish human intervention runbooks for higher-impact AI systems and test at least one tabletop scenario per high-impact system. ¹
• Add ongoing monitoring/review routines (access review, exception review, incident review, sampling where feasible). ¹
• Run an internal audit-style evidence check: pick several AI systems and produce the full evidence chain from approval to oversight. ¹

Frequently Asked Questions

Does Annex A.9.2 require a standalone “Responsible AI Policy”?

The control requires processes that ensure responsible use; a policy helps, but auditors will expect workflows, oversight, and human intervention procedures with records that prove adoption. ¹

Does this apply to employees using public AI tools for productivity?

Yes, if the organization allows or cannot practically prevent use, you need processes and guidance that define appropriate use, data entry restrictions, and oversight expectations. ¹

What counts as “human intervention” in practice?

Human intervention means defined authority and steps for a person to review, override, pause, or roll back AI outputs or AI-driven actions, plus documentation of what happened and why. ¹

How do we prove “responsible use” to an auditor?

Show the AI inventory, the intake/approval records, training completion, exceptions, and at least a few examples of monitoring or intervention actions taken when issues arose. ¹

We buy AI from a third party. Can we rely on their controls?

You can consider third-party capabilities, but Annex A.9.2 still expects your organization to run processes that govern how the AI is used in your context, including oversight and intervention paths. ¹

What’s the fastest way to operationalize this without blocking the business?

Start with a lightweight intake/approval workflow, publish clear permitted/prohibited uses, and require human review for higher-impact outputs; then expand monitoring and exception discipline as adoption grows. ¹

Footnotes

1. ISO/IEC 42001:2023 Artificial intelligence — Management system