A.2.3 Alignment with other organizational policies

What A.2.3 asks for when AI sits beside existing policies

A.2.3 asks the organization to work out where its other policies can be affected by, or apply to, its objectives for AI systems. In practice, the organization already has quality, information security, privacy and safety policies, and the question is which of them bite on AI, where they conflict with what the AI policy promises, and where no policy covers AI at all. The control is less about writing new text and more about mapping the existing policy set against the AI objectives and recording what the mapping found.

The risks it addresses are fairly predictable. A privacy policy may restrict training data that the AI policy assumes is available. A security policy may set access rules that an AI development team cannot meet. A safety or quality policy may impose release gates that no one has connected to model changes. Where these rules are never compared, the organization ends up with duplicated obligations, contradictory instructions to staff, or gaps in which an AI system falls under no owner. The difficulty is organizational: each policy has its own owner and review cycle, and AI touches all of them at once, so no single function sees the whole picture without deliberately bringing the owners together.

A.2.3 sits between A.2.2 AI policy, which sets the commitments the organization makes for AI, and A.2.4 Review of the AI policy, which keeps those commitments current. Alignment is the step that tests the AI policy against its neighbors before it is approved and again whenever a neighboring policy changes. Because ISO/IEC 42001 uses the harmonized structure shared by management system standards, it is designed to run alongside quality, safety, security and privacy standards, so an organization that already runs an ISO/IEC 27001 system can usually reuse its policy structure. Confirm with your own policy owners which documents exist, who signs them, and where the cross-references need to be recorded.

Which policies and AI objectives are in scope for alignment

Alignment starts with a scoping pass: which existing policies touch AI, which AI objectives and systems they affect, and who answers for each one. The scope follows the organization's AI objectives and the statement of applicability under clause 6.1.3, so each decision should be traceable to a documented reason.

Quality and information security policies

Start with the quality management and information security policies, since they usually already govern change control, supplier evaluation, access and incident handling for the systems that will host or run AI. Check whether model training pipelines, data stores and inference endpoints fall inside the existing security scope or sit outside it. Record each policy clause that applies to AI, and note where the existing wording assumes conventional software and does not address model updates or training data.

Privacy and personal data policies

Privacy policies apply wherever an AI system processes personal data, including training data, prompts, outputs and logs. Confirm which AI objectives involve personal data, which lawful bases or notices the privacy policy relies on, and whether retention rules cover model artifacts and inference logs. Ask the privacy lead to confirm which AI systems require a data protection impact assessment, so that the AI policy and the privacy policy describe the same processing.

Safety and sector-specific policies

Product safety, clinical, credit, employment or other sector policies can set stricter limits on AI use than a general AI policy would. For each AI objective in a regulated domain, identify the sector rule that constrains the output, the human review it requires and the escalation path when the system fails. Where a sector policy conflicts with an AI objective, document the conflict and the decision taken rather than leaving it implicit.

AI objectives and systems mapped to policies

Build a matrix that lists each AI objective and each AI system in use or development, then marks every policy that touches it. This matrix is the working evidence for A.2.3, because it shows an auditor how the organization reasoned about overlap. Include systems the organization only uses from a third party, since those still create obligations under internal policies even when the organization does not build them.

Policy owners and accountable teams

Each policy in scope needs a named owner who can confirm whether AI changes its requirements and who approves cross-references. Ownership often sits with quality, security, privacy or legal functions, while AI objectives sit with product or data teams. Confirm that each owner knows which AI systems fall under their policy, and record how disagreements between owners are resolved, so that roles defined under the AI management system match the policy owners in practice.

Customer and contractual commitments

Customer contracts, security questionnaires and supplier terms often make written promises about AI use, data handling or incident notice that carry the same weight as internal policy. Inventory these commitments, compare them with the internal policies they depend on, and flag any promise that no policy supports. Record policies with no AI touchpoint as well, together with the reason they were excluded, so the scope decision is complete and defensible.

How an assessor tests policy alignment under A.2.3

This table shows what an assessor does for each test step, what records they ask to see, and the findings that most often come up when AI objectives and other organizational policies are not cross-referenced. Read it as a checklist of records to have ready, since the assessor samples and reviews what you hold but does not supply the answers.

Test area	Test approach	Evidence requested	Common findings
Side-by-side policy reading	Assessor reads the AI policy next to the quality, security, privacy and safety policies and marks every point where one refers to or depends on another.	Current versions of the AI policy and each related policy, with approval dates and owners.	AI policy makes commitments that no other policy mentions, or related policies silently exclude AI systems.
Objective tracing	Assessor samples several AI objectives and traces each to the policy clauses that govern it, such as data protection, security or safety.	Documented AI objectives, the mapping from each objective to governing policy clauses, and the owner of each clause.	Objectives with no owning policy, or objectives that cite a clause that has since been renumbered or removed.
Consultation of policy owners	Assessor checks whether owners of the other policies were consulted when the AI policy or its cross-references were set or changed.	Review minutes, sign-off records or comments from privacy, security, quality and safety owners.	Cross-references written by the AI team alone, with no evidence that the policy owners saw them.
Change records	Assessor checks that changes to each linked policy were recorded and that the AI policy was considered at the time of change.	Version history for each policy, change requests and a log showing whether the AI policy was checked.	Policies updated without the AI policy being checked, leaving cross-references pointing at old wording.
Statement of applicability consistency	Assessor compares the cross-references against the statement of applicability under clause 6.1.3 to confirm the included and excluded Annex A controls agree with them.	Current statement of applicability with justifications, and the cross-reference register.	Controls marked as implemented that the policy cross-references never mention, or exclusions justified by a policy that does not exist.
Recorded gap decisions	Assessor looks for places where a related policy does not cover an AI risk and checks that a decision was recorded.	Gap register entries with the decision taken, the accountable owner and the follow-up action.	Gaps identified in working notes but with no recorded decision, owner or date for resolution.
Traceability into operation	Assessor picks one cross-reference and checks whether the referenced control is operating in practice, not only on paper.	Operating records such as privacy reviews, security test reports or quality checks that the cross-reference relies on.	Cross-references that cite a procedure which is documented but not performed for AI systems.

How to map AI objectives to existing policies, step by step

This sequence shows how to test each AI objective against the policies the organization already runs, so that A.2.3 ends with a traceable record of where those policies touch AI and what was decided for each overlap.

Inventory existing policies and their owners
Start by listing every policy that could touch AI systems: information security, privacy, data protection, quality, software development, procurement, safety, records retention and acceptable use. For each one, record the document title, current version, approval body, review date and the named owner accountable for it. Ask owners to confirm the list is complete, since AI often sits in policies written before AI was in scope. The output is a policy inventory with owners, which becomes the first tab of the cross-reference register.
List the AI objectives to be mapped
Pull the AI objectives from the AI policy drafted under A.2.2 and from the objectives requirements in clauses 5 and 6 of ISO/IEC 42001. Write each objective as a single testable statement, such as a commitment on data quality, safety of users or protection of personal data. Keep objectives that come from Annex C, the list of potential AI-related objectives and risk sources, if the organization has adopted them. The output is a numbered objective list that the rest of the mapping refers to by identifier.
Map each objective to the policy clauses that touch it
For each objective, read the inventoried policies and mark every clause that governs, constrains or applies to the same activity. A privacy clause on personal data may apply to a model training objective, and an access control clause may apply to a model registry. Record the clause reference, not just the policy name, so an auditor can follow the link. Where the NIST AI RMF to ISO/IEC FDIS 42001 crosswalk is useful, use its mapping of MEASURE 2.7 (AI system security and resilience) and MEASURE 2.10 (privacy risk) to the guidance for A.2.3 as a starting point, noting that NIST states its posting is not an endorsement. The output is a cross-reference register linking objectives to clauses.
Record gaps and the decision for each
Review the register for objectives with no governing clause, and for clauses that conflict with an AI objective or use outdated language. For each gap, record one decision: update an existing policy, write a new section, or justify why no change is needed, with the reasoning written down. The justification should name the clause and explain why it already covers the objective. The output is a gap log with one decision per item, owner and status, which the internal audit and management review can later check.
Obtain owner sign-off with a dated record
Send each policy owner the cross-reference rows and gap decisions that concern their policy, and ask them to confirm the mapping in writing with a date. Keep the signed confirmations with the register and the gap log, and version them so later changes can be traced. Confirm that the change history of each updated policy is kept, for example through a documented version control system. The output is a set of dated owner confirmations and a final register version that the organization can present as its A.2.3 evidence.

Evidence that proves the cross-references and how to keep it current

An auditor testing A.2.3 wants to see that the AI policy and the organization's other policies were compared, that gaps were decided, and that the record still matches reality. The cards below cover the artifacts that prove this and the open-source tools that can hold them under version control and clear ownership.

Cross-reference register

A register lists each other policy that touches AI objectives, such as quality, information security, privacy and product safety, and names the clauses that apply. For each entry, record the policy owner, the AI objective affected and the date the link was last checked. Ask whether every entry points to a current policy version, since a register that cites retired clauses fails the same test as a missing one. Store the register in a wiki page so the cross-references stay in one place that owners can update.

Policy change log with owner approvals

When a privacy, security or quality policy changes, the AI policy may need a matching change. Keep a log that records what changed, when, who made the change and who approved it. XWiki keeps every page change under version control, recording what changed, when and by whom, and lets you compare versions and roll back. Confirm with the vendor whether its history is retained long enough to cover your audit period.

Mapping of AI objectives to policy clauses

This table ties each AI objective to the clauses of the policies that govern it, so an auditor can trace an objective to its controls. Use the NIST crosswalk as a reference point for how security and privacy evaluation relate to the guidance for A.2.3, but do not treat it as a substitute for your own mapping. Assign one owner per row and review rows when either side changes.

Gap decision records

Where a policy does not cover an AI objective, the organization must decide whether to extend the policy, add a control or accept the gap. Record the gap, the options considered, the decision, the approver and the review date. BookStack roles let you set who can edit each policy page, and permissions can be overridden per shelf, book, chapter or page, so only the named owner can close a gap. Confirm that the tool can hold decision records in the form your auditor expects.

Statement of applicability entries

The statement of applicability must list the Annex A controls you include or exclude and justify each choice. For A.2.3, the entry should cite the specific policies and register rows that show the alignment work. An entry that says the control is implemented without pointing to evidence will not hold up. CISO Assistant ships more than 200 frameworks, including ISO 27001, NIST CSF, SOC 2, PCI DSS, NIS2, DORA and GDPR, and runs assessments with automatic mapping between them. Confirm with the vendor whether ISO/IEC 42001 is included before relying on it.

Meeting and review records

Minutes from policy review meetings, management review inputs and internal audit findings show that someone checked alignment on a planned schedule and after relevant changes. Record attendees, the policies reviewed, the findings and the actions with owners. Auditors look for evidence that these reviews happened and that actions closed, so keep the records linked to the register and change log rather than in separate files.

What aligning AI policy with existing policies costs

This table compares the main cost components of mapping the AI policy against existing quality, security, privacy and safety policies, showing who is paid, whether each cost is one-time or recurring, and what moves it. No fee or duration figures are supplied for this control, so use the table to structure your internal estimate and your request for a written scope and quote.

Cost component	One-time or recurring	What drives it	How to keep it down
Policy owner time for review and sign-off	Recurring	Number of existing policies touched, number of AI objectives, and how many owners must approve each cross-reference	Limit the first review to policies that actually reference AI or its risks, and batch sign-offs into one review cycle
Governance or compliance lead time to build the register	One-time build, then recurring upkeep	State of existing cross-references, how many AI systems are in scope, and whether policies live in one searchable place	Start from existing policy indexes and control mappings, and confirm effort with your own compliance lead before committing
Legal or privacy review where personal data is involved	Recurring when AI processing changes	Whether AI systems process personal data, and how often new data uses or sources are added	Scope privacy review to AI systems that handle personal data and reuse the existing privacy assessment where it applies
Tooling and hosting	Recurring for hosting and staff time; license cost is zero for the supplied open-source tools	XWiki (LGPL-2.1-or-later), BookStack (MIT) and CISO Assistant (AGPL-3.0-only) each carry their own license terms, and self-hosting still requires hosting and administration	Confirm license terms with your legal team, and avoid subscription spend by self-hosting within your own infrastructure
External review by a consultant or implementation partner	One-time for the initial alignment, optional recurring for re-checks	Size of the policy set, internal capacity, and whether the partner prepares the register or only reviews it	Request a fixed-scope written quote that names the policies, systems and deliverables covered, and keep final approval internal
Auditor time for sampling	Recurring at each audit cycle	Number of cross-references the auditor samples and how easily the organization can show the link from each AI objective to its policy	Keep a current cross-reference record with dated versions so sampling takes less time and fewer follow-up questions

Who maps the policies, who prepares the evidence and who assesses it

Your organization owns the AI policy, the other policies it must align with, and every decision about how they connect. The AI governance lead coordinates the work, but each policy owner, such as the heads of quality, information security, privacy and product safety, should confirm the cross-references that touch their area. Nothing in the mapping is valid until the accountable owners have reviewed and signed it off, and the sign-off record itself becomes evidence.

Policy owners: Identify which clauses in their policies apply to AI objectives, such as data handling, incident response, supplier checks or safety testing.
AI governance lead: Decides how conflicts between policies are resolved and keeps the decision log that explains each cross-reference.
Approvers: Sign off the final mapping and record the date, version and reason for any policy that is excluded or reworded.
Staff time: Ask internally how many people must attend workshops and how often reviews repeat, since this effort recurs whenever policies change.

Before the audit, confirm that every cross-reference has a named owner, a version and a sign-off date.

Where organizations usually fall short on policy alignment

Most A.2.3 findings come from the AI policy being treated as a standalone document. The gaps below share one root cause: the links between the AI policy, the other organizational policies and the AI objectives exist in people's heads but not in records an auditor can follow.

AI policy written in isolation

The AI policy is drafted by one team and never compared with the quality, information security, privacy and safety policies that already govern the same systems. A.2.3 expects the organization to work out where those existing policies affect or apply to AI objectives. Check each existing policy for a clause that covers AI-driven decisions, training data, model changes or automated outputs, and record which clauses apply and why.

Policies updated without a cross-reference check

A privacy or security policy is revised for an unrelated reason, and nobody asks whether the change affects AI systems that process personal data or depend on the same access controls. The fix is a short cross-reference check added to each policy change record, listing the AI policy and affected AI objectives as reviewed. The completed check is the evidence that the link was considered at the time of change.

AI objectives with no owning policy

Objectives such as limiting bias in a credit model or keeping a customer-service chatbot within approved topics appear in a project plan but are not assigned to any policy. Map each AI objective to the policy that sets its rule and the owner who enforces it. An objective without a policy home is a finding because the organization cannot show how it will be controlled.

Gaps noticed but never decided

Reviewers identify a conflict, such as a retention rule in the privacy policy that clashes with an AI logging requirement, and the discussion ends in a meeting without a record. Log every identified gap in a register with the policies involved, the decision taken, the accountable owner and the date. An auditor will sample these entries to test whether the alignment process actually produces outcomes.

Owner sign-offs that live in email

Policy owners confirm that their policy does or does not affect AI objectives through email threads that are later deleted or cannot be linked to a policy version. Move each sign-off into the document system with the policy version, the date and the named approver. Version history in a wiki or document platform, such as the page history kept by XWiki, can show exactly which version each owner reviewed.

Statement of applicability citing outdated policies

The statement of applicability under clause 6.1.3 refers to policy names, section numbers or versions that have since been renamed, merged or rewritten, so the justification no longer matches the text. Before each assessment, check every policy reference in the statement against the current approved version and correct any mismatch. Keep the dated check as a record so the auditor can see the references were verified.

Questions buyers ask about A.2.3 alignment

How much effort does aligning the AI policy with other policies take?

The effort depends mostly on how many policies already touch AI and how many of them are current. Most organizations start with the policies that already exist: information security, privacy, quality, safety and procurement. Each one needs a review to find clauses that affect AI objectives, such as data handling rules, supplier requirements, or change control for models. The work grows when policies are scattered across wikis, when owners disagree about scope, or when AI systems are new enough that no one has written down which policy governs them. Ask any provider to scope the policy count, the number of AI objectives in use, and the number of owners who must sign off, because those three inputs drive most of the workload.

How long does the A.2.3 alignment work take?

No duration is set by the standard, and the timeline depends on the inputs. The main factors are the number of existing policies, whether they are documented and version controlled, the number of AI systems in scope, and how quickly policy owners can review drafts. Organizations with a current policy set and clear AI objectives usually move faster than those building both from scratch. Ask for a timeline in a written, fixed scope that lists the policies reviewed, the owners involved, the review rounds included and the deliverables, so you can compare proposals on the same basis.

What does the work require from our own team?

The work needs three things from your side: named owners for each relevant policy, an AI governance lead who can decide conflicts between policies, and access to the AI objectives and the systems they cover. Policy owners confirm which clauses apply to AI and approve changes. The governance lead resolves cases where two policies give different instructions, such as a retention rule that conflicts with a model training need. Your team also needs to make the AI objectives available in writing, because A.2.3 is tested against them. Confirm these commitments in the scope before work starts so that owner time is planned, not assumed.

Can we do the A.2.3 mapping in-house without outside help?

Yes. Organizations can do the mapping internally if policy owners are engaged, the AI objectives are written down, and the cross-references are recorded in a way an auditor can follow. The mapping itself is a documented comparison: each relevant policy, the clauses that affect AI objectives, the decision taken, and the owner who approved it. The harder part is consistency across owners, which a neutral reviewer can help with. Keep the records in a version-controlled location with dates and authors, because the auditor will test whether the alignment was done and maintained, not only whether it exists today.

When does an implementation partner add value, and what must stay with us?

A partner adds value when policies are numerous, owners are hard to coordinate, or internal teams lack time to compare documents line by line. The partner can prepare the cross-reference records, draft changes, and set up a review cycle. Ownership stays with your organization. Your leadership approves the AI policy, accepts risks, makes decisions on conflicts, and signs off on the final alignment. Your team also owns the AI objectives and the evidence that they are followed. A partner prepares systems and records. It does not issue audit reports or certificates, and an independent certification body is responsible for any assessment outcome.

Will an auditor accept a mapping prepared by a consultant?

An auditor tests the organization's records, not the consultant's report. The question is whether your organization owns the policy, the mapping and its approvals, and whether those records show that AI objectives were checked against other policies. A consultant's work can be part of the evidence, but the auditor will expect your own owners to have reviewed and accepted it. Independence matters too: the certification body must remain independent of the organization it assesses, and a firm that designed the management system may need to take care in how it supports an assessment. Confirm the auditor's position on consultant-prepared records before the engagement begins.

How does A.2.3 relate to the NIST crosswalk mapping of MEASURE 2.7 and 2.10?

The NIST AI Resource Center crosswalk, contributed by Microsoft and not endorsed by NIST, maps two AI RMF subcategories to the guidance for A.2.3: MEASURE 2.7, which covers AI system security and resilience, and MEASURE 2.10, which covers privacy risk. This gives you a useful starting point. Security and privacy policies are often the first ones to apply to AI objectives, so the crosswalk points you to where cross-references should be recorded. It does not replace your own review, and it does not establish compliance with either framework. Treat it as a reference for the cross-check you perform, and record your own decisions about which policies apply.

How DeployOpen scopes and prepares A.2.3 evidence

DeployOpen prepares the systems, documents and cross-references an organization needs to show how its AI policy aligns with its quality, security, privacy and safety policies. The organization keeps ownership of its policies and its AI objectives, and an independent certification body decides whether the control is met.

How we scope

Agree a written, fixed scope listing the policies, objectives and systems included in the A.2.3 review.
Inventory the existing quality, security, privacy and safety policies and their current owners and review dates.
Confirm with the organization which policies the AI objectives touch, and record the scope before work starts.

How we build

Map each AI objective to the policy clauses that affect or govern it, and note gaps for owner review.
Set up cross-reference records in the chosen documentation platform, with version history and access roles.
Test the cross-references against the ISO/IEC 42001 harmonized structure and the related policy frameworks in scope.

How we hand over

Deliver the alignment matrix, cross-reference index and version records as documented evidence for the organization.
Document the method, assumptions and open items so internal staff can maintain the alignment after handover.
Walk the policy owners through the records so they can answer an auditor's questions about each cross-reference.

How we support

Provide ongoing support and upgrades for the documentation platform as a retainer when the organization asks for it.
Update cross-references when policies change, following the organization's own review cycle for A.2.4.
Confirm with the organization and its independent auditor which evidence format and depth they expect before finalizing.