A.9.3 Objectives for responsible AI use: evidence and audit

What A.9.3 asks for and why use objectives are hard to prove

If an audit or a customer questionnaire asks how your organization governs the use of AI, Annex A control A.9.3 is the requirement behind the question. In plain terms, it asks you to identify and document the objectives that guide responsible use of your AI systems, such as accountability, fairness and human oversight. Writing those objectives down is the easy part. The harder part is showing that the people using an AI system every day are working toward them, and that someone checks whether they are. An objective that lives only in a policy slide does not answer an assessor's question about how a specific use was judged against it.

The gap usually sits between the stated objective and the daily activity. A fairness objective, for example, means little until you can name the decisions the system influences, the groups affected, the checks run on outputs and who reviews the results. Human oversight objectives need a named reviewer, a defined point where a person can intervene and a record that intervention happened. Before your next assessment, confirm three things: each objective you have documented maps to at least one concrete use, each use has an owner who reports against the objective, and you can produce dated records of those checks. If any of the three is missing, expect follow-up questions.

Do not confuse A.9.3 with A.6.1.2, which covers objectives that guide responsible development of AI systems. A.6.1.2 sits in the life cycle area and concerns how systems are designed and built, while A.9.3 concerns how the organization uses them, including systems bought from third parties. Organizations often write one objectives document and assume it satisfies both, then discover that development records do not show how deployed use was assessed. Review the ISO/IEC 42001 standard page to confirm the exact wording of both controls in your edition, and keep separate evidence for development and use.

Which AI systems and teams the A.9.3 objectives must cover

Before an organization can document objectives for responsible use, it has to decide which AI systems, teams and levels of granularity those objectives govern. These choices change how much work A.9.3 creates and who owns it. The decisions are recorded in the statement of applicability under clause 6.1.3, including the reason A.9.3 is included or excluded.

In-house AI systems

Models, prompts, retrieval pipelines and decision tools the organization builds or fine-tunes itself. These sit closest to the organization's control, so the objectives for their use can be tied directly to configuration, access and monitoring. Check whether each in-house system has a named owner who can state its intended use and the objectives it must meet. Systems built by one team for internal staff are easy to overlook and still fall within scope.

Third-party AI services in use

Hosted models, AI features inside SaaS products and AI-enabled tools that staff sign up for. The organization does not control the underlying system, but it still uses it and must set objectives for that use, such as which data may be entered and where outputs may be relied on. Confirm with each supplier what it documents about its own AI system, and record the gap between that documentation and your own use objectives.

Business units deploying AI informally

Teams that adopt AI tools without central review often create the largest unmanaged scope. An inventory built from procurement records, expense claims, browser extensions and single sign-on logs tends to surface these uses. Decide whether each unit must register its uses against the same objectives or whether a lighter rule applies to low-risk uses, and document that rule as part of the scope.

Per system, per use case or per organization

Objectives can be set once for the whole organization, for each AI system, or for each use case of a system. A single organization-wide statement is quick to write but rarely shows how a customer support bot differs from a credit screening tool. A per-use-case approach gives the most evidence but needs more upkeep. Choose the level that matches your risk profile and state the choice in the scope record.

Exclusions and their justification

A.9.3 may be excluded only with a documented reason in the statement of applicability, such as the organization making no use of AI systems within scope. Assessors will test whether an exclusion holds up against what the inventory actually shows. Confirm the reasoning with your certification body before finalizing it, since a weak exclusion for a control that clearly applies is a common readiness gap.

How an assessor tests the objectives for responsible use

This table walks through the four checks an assessor typically performs for A.9.3, with the evidence requested at each step and the findings that show up most often. Read it as a sequence: each step depends on the one before it, so a gap in objective definition usually surfaces again during sampling and failure review.

Test step	What the assessor confirms	Evidence requested	Common finding
1. Objectives exist and are approved	Responsible use objectives are identified, written down and approved by someone with authority over AI use	Approved objectives document, version history, approval record, and the scope it covers	Objectives exist only as slogans such as fairness or transparency, with no approver or date
2. Objectives are specific and owned	Each objective names an accountable owner and a way to tell whether it is being met	Objective register with owner, measure, target condition and review frequency	No owner or measure is recorded, so nobody can show the objective was checked
3. Objectives trace to AI uses	Each objective maps to the specific AI systems and use cases it governs	Traceability matrix linking objectives to the AI system inventory and use cases	Objectives are company-wide statements that do not show which systems they apply to
4. Uses are checked against objectives	A sample of AI uses was reviewed against the relevant objectives before and during use	Sampled use records, review checklists, sign-offs and dated evidence of each check	Checks are described in policy but no completed review records exist for the sampled uses
5. Revisions follow changes in use	Objectives were revisited when a use changed, such as new data, a new user group or a new model version	Change records linked to objective reviews, with dated revision entries	Objectives were never revisited after a use changed, so they no longer match how the system is used
6. Failed checks lead to action	When a check failed, the organization recorded the problem, decided on a response and confirmed the fix	Exception log, root cause notes, corrective action records and follow-up review results	Failures are not logged, or they are closed without evidence that the use was corrected or stopped
7. Management oversight	Leadership reviews whether objectives are being met and whether resources match them	Management review minutes or reports that cite objective performance and open actions	Reviews happen but do not mention A.9.3 objectives or the results of their checks

How to set up objectives that can be checked

This sequence turns broad aims for responsible AI use into objectives with named owners, measurable checks, and a record of results. Each step produces an artifact an assessor can trace from the objective to the AI system it governs.

Identify candidate objectives from policy and risk sources
Start from the AI policy the organization has already adopted and from the potential AI-related organizational objectives and risk sources listed in Annex C of ISO/IEC 42001. Draft a short list of candidate objectives for responsible use, such as accountability for decisions, fairness across affected groups, human oversight of consequential outputs, and data handling limits. Keep each candidate tied to a policy statement so the link can be traced later. The output is a draft objectives register with a source reference on every line.
Assign a named owner to each objective
For every candidate objective, name one accountable role, not a team, who can approve the objective and answer for its results. Confirm that the owner has the authority and the information to act, and that the role appears in the AI roles and responsibilities already defined under the internal organization controls. The output is the objectives register with an owner column completed, signed off by the executive who owns AI governance. An objective without a named owner should not move to the next step.
Write measurable checks for each objective
Translate each objective into one or more checks that someone can perform and repeat. A check states what is measured, the data or evidence source, the threshold or pass condition, and what happens on failure. For example, an illustrative fairness objective might be checked by comparing error rates across defined groups in a test set against a threshold the owner sets. Where a measure is not yet possible, record the gap and the interim control instead of leaving the check blank. The output is a check specification for each objective.
Map each objective to the AI systems it governs
Build a matrix that links every objective to the AI systems in the inventory, including in-house models and third-party AI services. Mark which objectives apply to which system and where a system is excluded, record the reason. This mapping is what lets an assessor follow an objective to the system and the evidence that tests it. The output is an objectives-to-systems matrix kept with the AI system inventory, with a named owner for the matrix itself.
Run the checks and record the results
Execute each check on the schedule in the check specification, using the data and tooling named there. Record the date, the system version tested, the result, the person who ran the check and any follow-up actions. Logs from monitoring or testing tools can serve as raw evidence, but the record that shows the objective was checked and by whom should be kept as its own document. The output is a check log that shows each objective was tested on a set cadence, with findings and actions traceable to an owner.
Act on failed checks and close the loop
When a check fails or returns an unclear result, the owner decides whether to pause the use, restrict it, adjust the system, or accept the residual risk with documented approval. Track each finding to closure in the check log and update the objectives register if the objective itself needs to change. The output is a record of decisions for each failed check, showing who approved the action and when the issue was closed.
Set review triggers for objectives
Define the events that force a review of each objective, such as a material change to the AI system, a new use case, a change in the affected population, an incident, or a finding from an internal audit or management review. Also set a routine review interval that the organization can sustain and document. Confirm the triggers with the owners and record them in the register. The output is a review schedule and trigger list attached to the objectives register, so that objectives stay current as systems and uses change.

Evidence that shows use is checked against the objectives

An assessor testing A.9.3 wants to see that the objectives were written down, tied to real AI systems, and checked on a schedule with results recorded. The six artifacts below form one chain: the register defines what is checked, the mapping shows where it applies, and the check, exception and review records show that the checks actually happened.

Approved objectives register

A controlled document listing each objective that guides responsible use, such as accountability, fairness or human oversight, with a plain-language definition, the accountable owner and the approval date and approver. Keep it under document control with version history. Between audits, confirm each owner still holds the role, and confirm the register was reviewed when a new AI system or use case entered scope. Ask the implementation partner to set up the register template, but the organization must approve the objectives and name the owners.

Objective-to-use mapping

A table that links each objective to the AI systems and uses it governs, including in-house models and third-party AI services. The mapping shows an assessor that no system sits outside the objectives without a stated reason. Refresh it whenever an AI system is added, changed or retired, and check that the inventory and the mapping still match. A gap here usually means a system is in production with no objective checked against it.

Check procedures

Written steps that describe how each objective is tested in practice: what is measured, the data or logs used, the threshold that counts as a pass, and who performs the check. For example, a fairness objective may call for comparing outcomes across groups using a stated method, which is illustrative and must be set by the organization. Procedures should be specific enough that a second reviewer could repeat the check and reach the same result.

Dated check records

Completed records showing that each check ran, when it ran, who ran it, the evidence reviewed and the result. These are the most frequently requested artifacts at assessment. Tie each record to a review calendar so every objective has a check due date and an entry showing completion. Automated tooling, such as Langfuse traces or garak reports for language model systems, can supply raw evidence, but a person should sign off on the interpretation.

Exception and corrective action log

A running record of every case where use did not meet an objective, with the date found, the affected system, the risk judgment, the containment step, the corrective action and the verification that it worked. An empty log can look as weak as a missing one, so assessors ask how exceptions are found. Review open items at each management review and close them only after a follow-up check confirms the fix.

Review minutes and decisions

Minutes from the reviews where leadership examines check results, exceptions and objective changes, with the attendees, decisions taken and actions assigned. These show that the objectives are governed by management rather than left to the technical team alone. Schedule the reviews on the same calendar as the check records, and keep the minutes linked to the register version they examined so the trail stays complete between audits.

Which open-source tools produce A.9.3 evidence

Runtime records show what an AI system actually did in production or test use. For A.9.3, they help you check whether use stayed consistent with the objectives you documented, such as accountability, fairness or human oversight. Langfuse (MIT) is an observability platform that records traces for LLM applications. Each trace captures the operations in a request, including LLM calls, retrieval steps, tool executions and custom logic, along with timing, inputs, outputs, token usage and metadata.

Operation-level traces: Each request is broken into its steps, so reviewers can see which tools or retrieval sources influenced an output.
Input and output capture: Stored prompts and responses let you sample uses and test them against stated objectives, subject to your privacy rules.
Metadata tagging: Tags for user group, use case or approval reference can connect a trace to the use it was approved for.
Usage and timing data: Token and latency records show patterns of use that may indicate drift from the intended purpose.
Retention settings: Retention and redaction choices determine what evidence survives and what personal data you hold.

Confirm with your own environment whether Langfuse's capture, redaction and retention settings meet your data protection obligations before you rely on its traces as evidence.

What meeting A.9.3 costs in money and effort

This table breaks the cost of meeting A.9.3 into its components, showing who is paid for each, whether it is one-time or recurring, and what pushes it up or down. Read it as a checklist for a written scope and itemized quote, since no prices or durations are supplied for this control.

Cost component	Who is paid or who spends time	One-time or recurring	What drives it up or down
Drafting and approving objectives	Internal governance, legal, product and risk staff; sometimes outside advisers	One-time to draft, recurring to review	Up with the number of AI systems and use cases; down by grouping systems under shared objectives
Engineering checks and logging	Internal engineers or an implementation partner	One-time to build, recurring to maintain	Up with the number of checks per objective; down by reusing checks across systems
Log storage and review	Infrastructure team; storage in the organization's own cloud or data center	Recurring	Up with log volume and retention periods; down by logging only the events tied to each objective
Licenses and tooling	Vendor licenses if commercial tools are chosen; self-hosted open-source tools carry no license fee but need hosting and upkeep	Recurring where licenses apply	Up with commercial seat or usage terms; down by choosing self-hosted open-source options where they fit
External adversarial testing	Security testing firm or internal red team	Recurring, typically per test cycle	Up with the number of AI systems and their exposure; down by scoping tests to high-impact objectives
Independent review of objectives	Independent assessor or consultant, separate from any certification body	Recurring, usually tied to review cycles	Up with third-party dependence and number of objectives; down by a clear written scope
Auditor sampling	Certification body, charged under its own engagement terms	Recurring at each audit	Up with the number of AI systems sampled and gaps found; down by keeping objective records current and sampleable

Who owns the objectives, who prepares evidence and who assesses

Your organization owns the objectives for responsible use of each AI system. Under A.9.3, the objectives that guide use must be identified and documented, and the people accountable for them must be named inside the business. Decisions such as which objectives apply to a given tool, what trade-offs are acceptable between fairness and accuracy, and when human oversight is required are management decisions, and they cannot be delegated to an outside party.

Setting the objectives: Select the objectives that fit each AI use, such as accountability, fairness, transparency or human oversight, and record the reason each one applies.
Approving the register: A named owner signs off each objective and each change to it, so the approval trail shows who decided.
Running the reviews: Schedule and perform periodic checks of actual use against the objectives, and record the findings and follow-up actions.
Acting on results: Decide what happens when use drifts from an objective, including stopping or restricting a use case.
Keeping ownership current: Update owners when staff or systems change, so the register does not go stale.

Ask whether each objective has a named owner, a review date and a documented decision record before any outside party touches the register.

Where organizations usually fall short on A.9.3

A.9.3 asks for objectives that guide responsible use, and auditors look for more than a policy statement. The gaps below appear repeatedly when organizations try to show that their AI use is steered by documented objectives, and each one has a practical fix that produces evidence an assessor can test.

Objectives with no owner

Objectives such as fairness or human oversight often sit in a policy document with no named person accountable for them. Without an owner, nobody can say who approves a change to an objective or who answers when use drifts from it. Assign each objective to a named role, record that role in the objectives register, and keep the owner's name and date of last review next to the objective itself.

Objectives that cannot be tested

Statements like "use AI responsibly" give an assessor nothing to check. A usable objective names a condition that can be measured or observed, such as a required human review step before a decision is communicated, or a documented threshold for an error rate in a specific use. Rewrite each objective so that a reviewer could reach a yes or no answer from the evidence.

No link to specific AI uses

An organization may hold a sound list of objectives but cannot show which AI systems those objectives govern. Map each objective to the named AI uses it applies to, for example a résumé screening tool or an internal document summarizer. Where an objective applies to every use, say so explicitly so the scope is clear to an assessor and to the teams running the systems.

Third-party AI with no objectives

Staff often adopt AI features inside existing software, and those uses rarely pass through an internal review. If a vendor's AI runs on the organization's data or makes decisions about its customers, it still needs objectives that guide its use. Inventory vendor-provided AI features, assign each one the objectives it must meet, and record how the vendor's documentation supports those objectives.

Checks that run once

Many teams confirm objectives at deployment and never look again, even though models, data and user behavior change. Set a review frequency for each objective, define the trigger for an earlier review such as a model update or a new use case, and keep the outcome of each check. A single approval from a year ago is weak evidence that objectives still guide use today.

Records that cannot be dated or traced

Assessors ask who decided an objective, when it was set and when it was last checked. Spreadsheets edited in place and undated meeting notes do not answer those questions. Keep objectives under version control or in a system that records author, approver, date and change history, and retain each review result as a dated record linked to the AI use it covers.

Questions buyers ask about A.9.3 cost, effort and outside help

Is A.9.3 more work than the other A.9 use controls?

A.9.3 is usually less work than A.9.2 but more judgment-heavy than A.9.4. A.9.2 requires a full set of processes covering approval, acceptable use and oversight, so it touches more people and documents. A.9.3 asks for a defined set of objectives, such as accountability, fairness or human oversight, and a way to check use against them. The effort lies in choosing objectives that fit your AI systems and in agreeing who reviews them. A.9.4 is more operational, because it depends on monitoring use over time. Your effort on each control depends on how many AI systems you run and how many of them are third-party.

Can a small team implement A.9.3 on its own?

Often yes, if the AI footprint is small and one person can own the objectives record and the review cadence. A small team needs a named owner with authority to approve objectives, a short list of AI systems in use, and a simple record linking each objective to a check. The harder part is independence: someone other than the person who built a tool should confirm that use still matches the stated objectives. If internal staff lack time or AI governance experience, a fixed-scope outside engagement can help prepare the records, but the organization must still make and own the decisions.

What does A.9.3 need from business and engineering teams?

A.9.3 needs both groups to contribute different inputs. Business owners need to state what each AI use is for and which outcomes matter, such as fair treatment of customers or a required human decision point. Engineering teams need to describe how the system behaves, what data it touches, and where logs or test results can show whether the objective holds. Compliance or risk staff typically approve the objectives and decide how they are reviewed. Expect these teams to meet at least when objectives are first set and again when a new AI use or significant change is proposed.

How long does A.9.3 take, and what changes the timeline?

The timeline depends on scope rather than on a fixed duration. The main drivers are the number of AI systems in use, whether they are built in-house or bought from third parties, how many objectives each system needs, and whether the records and logs already exist. Organizations with existing risk registers and change processes usually move faster than those starting from no documentation. Ask any provider for a written scope that lists the systems, the objectives to be documented and the evidence to be prepared, then compare the timeline against that list rather than against a general estimate.

Do open-source tools remove the need for a license budget?

Open-source tools remove most software license fees, but they do not remove the cost of the work. Tools such as Langfuse (MIT) can record traces of AI operations, and garak (Apache-2.0) can probe language models for failures such as prompt injection and data leakage, producing results that can serve as evidence against objectives. Budget still goes to hosting, configuration, integration, staff time and any consultant fees. Confirm each tool's license terms with its maintainers before you deploy it, and check whether the tool captures the signals your chosen objectives need.

Can an outside partner certify our organization against ISO/IEC 42001?

No. An outside implementation partner can prepare systems, controls and evidence for A.9.3, but only an independent certification body can issue a certificate. ISO does not certify organizations itself, and certification bodies are external. DeployOpen prepares systems and evidence and does not issue audit reports, attestations or certifications. Choose an implementation partner that is independent of the certification body you plan to use, and confirm in writing which tasks the organization keeps, such as approving objectives and signing off on risk decisions.

What should we ask for in a written quote for A.9.3 work?

Ask for a fixed scope that lists the AI systems covered, the objectives to be drafted, the review process to be designed, the evidence to be produced and the systems or tools to be configured. The quote should state who approves objectives, what the organization must provide, and what happens if new AI systems are added during the work. Ask which deliverables are one-time, such as the initial objectives record, and which recur, such as periodic reviews or monitoring. Confirm that the provider will not perform the audit or issue any certificate.

How DeployOpen scopes and prepares A.9.3 evidence

DeployOpen is a Chainscore Labs company that plans, implements and documents self-hosted open-source platforms inside the client's own infrastructure. For A.9.3, we help the organization define its responsible use objectives, connect them to the systems that show whether use follows them, and prepare the records an independent auditor will review. The organization owns its objectives, and only an independent certification body issues any certificate.

How we scope

Confirm which AI systems and business uses fall under the organization's ISO/IEC 42001 scope and statement of applicability.
Agree in writing which responsible use objectives apply, such as accountability, fairness or human oversight, and who approves them.
Define the systems, owners and review cadence that the written scope covers, so deliverables and timelines are fixed before work starts.

How we build

Set up self-hosted tracing so requests to each AI system record inputs, outputs and metadata that can be checked against each objective.
Configure scanning of language model deployments with open-source probes such as garak, keeping attempt reports as objective-level test evidence.
Map each objective to a documented check, owner and recurring review, using control mappings that the organization confirms before relying on them.

How we hand over

Deliver a documented objective register with owners, measurable checks, review records and the evidence location for each objective.
Provide runbooks that explain how staff run checks, record exceptions and escalate objectives that use has drifted away from.
Walk the organization's compliance and internal audit staff through the evidence set so they can answer an assessor's questions themselves.

How we support

Support ongoing operation of the checks and evidence collection through a retainer, if the organization wants continued help.
Update objectives, tooling and documentation when AI systems change, following the written change process agreed in scope.
Keep the organization in charge of decisions, and direct questions about certification to its chosen independent auditor or certification body.