AI for audits

Introduction

Many organizations have enough information for an audit, but that information is scattered across documents, spreadsheets, emails, risk files and individual action lists. Preparing for an internal or external audit therefore often consists of manual collection, checking and enquiries. An integrated audit eco-environment brings audit planning, standards frameworks, controls, evidence, findings and corrective actions. AI can use this coherence to periodically perform analyses and make connections visible.

Problem definition

• Proof of an activity is not directly linked to the relevant control or standard requirement.
• The relevant documentation has been distributed.
• Findings and measures are followed separately from the risk treatment plan.
• 'Audit readiness' is only known around the audit moment.
• A GapFit analysis requires a lot of manual comparison.
• Patterns across multiple audits remain out of the picture.

AI only delivers reliable value when the analysis takes place within a defined, up-to-date and controllable information environment (within a context, for example with a control framework).

Four AI applications with direct audit value

1. Periodic audit readiness check

AI can check at fixed times whether the audit file is sufficiently ready. The AI model compares required controls including evidence with the actual content of the audit eco-environment.
Possible problems:

• Controls without evidence;
• Outdated, expired, or unapproved supporting documents;
• Open actions that pose an audit risk;
• Inadequately handled previous findings;
• Missing information per process owner.

Audit readiness thus becomes a periodically monitored process instead of a one-off effort just before the audit.

2. GapFit analysis based on a Control Framework

In a GapFit analysis, AI compares the chosen framework of standards with the described and demonstrably implemented method. A Control Framework forms a strong basis for this, because standard requirements, controls, risks, responsible parties and evidence are explicitly linked to each other.

• What internal information does the control support?
• Where is description or execution possibly incomplete?
• Which (standard) requirements have been described but have not yet been sufficiently demonstrated?
• What is still missing to make an audit more (time) efficient?

3. The Control Framework as a reliable AI context

Without context, it is not clear which version, source or interpretation is leading. The Control Framework limits the analysis and makes it clear which controls are relevant, which risks are controlled with them and which evidence is accepted. Especially when evidence is stored directly in the framework, a high-quality context is created with established standard requirements, approved documents, current registrations, owners, findings, actions and associated risks. This lowers the chance of untraceable or unclear AI answers.

4. Connecting audit findings to the risk treatment plan

AI can analyze audit findings, deviations, open actions and control effectiveness in relation to the risk analysis and the risk treatment plan.

• Which findings affect risks with the highest residual risk score?
• Which controls are repeatedly insufficiently effective?
• Where are actions lagging behind while the risk remains high?
• Which processes or locations show recurring patterns?
• Which measures are likely to provide the most risk reduction?

The result is a substantiated list of priorities for management, auditors and process owners, although they remain responsible for validation and decision-making.

What does a usable AI result look like?

• Question, scope and reference date;
• standards, controls, documents and evidence used;
• facts and missing information;
• link with risks and the risk treatment plan;
• certainty, assumptions and source references;
• prioritization on impact, urgency and residual risk;
• required human review and approval.

Step-by-step entry

1. Set up a Control Framework . Which standards and associated controls are required.
2. Make the Control Framework up to date. Establish standards, controls, risks, owners and criteria.
3. Link reliable evidence. Use approved sources that demonstrate the effectiveness of the control measure.
4. Start with one auditable analysis. For example, missing evidence for one standard requirement.
5. Validate with known audit situations. Compare with assessments from experienced auditors.
6. Expands the scope of application. Connect audits, anomalies, actions, control effectiveness and risks.

Examples of good prompts

• "Perform an audit readiness check for standard XYZ. Show missing, expired or unapproved evidence per check and cite the internal source."
• "Compare our Control Framework with the chosen standard. Only name possible gaps and indicate for each gap what information is missing."
• "Analyze audit findings from the past twelve months in relation to the risk treatment plan. Rank the five biggest current risks."
• "Which controls repeatedly have an unsatisfactory assessment and which open measures influence the residual risk?"
   

Frequently asked questions

Sources

• Why do organizations choose an audit eco-environment?
• AI and QMS software.
• AI in QHSE software: which questions really deliver value?
• ISO 19011 – Guidelines for audits of management systems.
• NIST AI Risk Management Framework.
   

Conclusion

The greatest value of AI in an audit eco-environment arises when audits, controls, evidence, risks and measures are not treated as separate parts. An up-to-date Control Framework, especially with stored evidence, provides a strong foundation for auditable AI analyses. This allows an organization to periodically monitor its audit readiness, perform a targeted GapFit analysis and more quickly see which audit findings cause the greatest current risks. AI supports the professional assessment; auditor, QHSE professional, process owner and management remain responsible.