Why Polish audit firms need practical AI governance standards
A Polish audit firm should identify which AI uses touch client data or audit judgements, then map the applicable audit-quality, confidentiality and data-protection rules to each use. Record who reviews consequential output and what evidence the engagement file needs.
Scale the proposed controls to the use, data and audit-file impact. Compare a public-guidance summary with AI-assisted audit analytics to identify the different review and evidence needs; the firm's size alone does not determine which obligations apply.
The safest standard is a layered governance baseline
For the Polish audit firm's proposed use, ask the responsible reviewers to distinguish applicable law, professional requirements, voluntary guidance and internal controls. Record current sources and applicability decisions for the uses, data, suppliers, review, records and incidents; this checklist does not establish one composite legal standard.
The aim is simple: AI may support research, drafting, classification, summarisation or analysis, but it should not become an undocumented decision-maker. A qualified person remains responsible for the audit judgement, the quality of client-facing work and the confidentiality of the underlying data.
Map AI risks and efficiency gains before scaling use
The core standards to map against
EU AI Act review. Inventory the intended uses and refer classification, actor-role and applicability questions to the responsible reviewer. Include risk assessment, assurance, fraud review, client reporting and staff decisions where relevant; do not classify a use from the product label alone.
GDPR and client confidentiality. Client files, payroll data, financial records, contracts and management accounts should not be entered into public tools unless the firm has approved the tool, reviewed the data-processing terms and confirmed retention, training and access controls. The key control is not a vague promise of security. It is a documented rule for what data may enter each approved AI system.
Audit quality management. AI use should sit inside the firm's quality management system, with policies for supervision, review, independence, evidence and documentation. If AI contributes to an audit file, the workpaper should show the input, output, review performed, limitations considered and final human judgement.
Professional ethics and accountability. Partners and engagement leaders need clear ownership. AI can help prepare analysis, but it cannot carry professional responsibility. The firm should define who approves AI use on engagements, who reviews exceptions and who signs off client-facing material.
What controls should be in place before wider use?
Start with an AI register. List every tool in use, the owner, purpose, data category, vendor, access method, retention setting, training setting and permitted users. Include embedded features inside document platforms, audit software and communication tools, not only standalone chat tools.
Then define use-case tiers. Low-risk use may include public research, formatting help or internal brainstorming with no client data. Medium-risk use may include summarising client-provided documents after approval. Higher-risk use may include audit planning, anomaly detection, judgement support, valuation support or client advice, and should need stronger review and evidence.
Keep engagement-specific answers on permitted data, review and sign-off with their source, approver and review date. Refer unresolved consent or disclosure questions to the responsible professional; an answer bank is not a Polish audit-specific AI standard.
Keep AI governance updated as tools and rules change
How to evidence compliance without slowing every team down
Define the evidence needed for the particular use and applicable engagement requirements. Record the AI's role, material output, review, corrections and final judgement. Decide separately whether the input, an input summary or a reference to the controlled source record is needed; do not retain complete prompts or client files by default. Confirm confidentiality, minimisation, access and retention before collecting them. A short note is appropriate only where it meets the actual requirements.
Use templates for common workflows. A one-page AI use assessment can cover purpose, data sensitivity, tool approval, expected benefit, review owner, known limitations and required record. For repeatable workflows, build the controls into the process so staff do not have to remember them manually.
A practical implementation path for audit firms
Begin with an inventory of current uses, including embedded features and staff workarounds. Assess unapproved uses and assign the authority for any restriction or pause; then define the permitted tools, data and review rules. Use audit, accounts, tax and advisory examples in staff training.
Choose the number and scope of pilots against review capacity and the approvals needed. Public technical research, internal knowledge search, document summaries and audit-planning support are examples to assess, not automatically suitable starting uses. Measure elapsed time, review effort, errors and record quality against a baseline.
Assign the register's review owner and schedule against the uses and applicable requirements. Monthly review is one option, not an established minimum. Check vendor-term changes and relevant file samples, and update staff guidance when the review identifies a change.
Build controlled AI workflows with audit-ready evidence
Conclusion
Test the proposed checklist against an actual workflow and its engagement-file record. Identify the applicable legal and professional requirements separately, record who reviewed the output and check where the controlled evidence is held. Use the checklist as a local planning aid, not as a Polish audit standard.
Want to turn this into a practical AI plan?
For accountancy and audit teams, the next step is to identify where AI can reduce manual work without weakening review quality, client confidentiality or regulatory controls.
