December 11, 2025

How To Use Agentic AI To Support Integrated ISO Audits

Deirdre Dolan

Sr. Director of Product Marketing

Increasing trust with AI for audit and advisory firms.

Audit and advisory firms conducting integrated ISO audits face a capacity constraint that manual procedures cannot solve. Client demand for ISO 9001, ISO 27001, ISO 14001, and ISO 45001 certifications exceeds available auditor hours, yet traditional approaches to evidence gathering, control testing, and compliance documentation consume the same time regardless of engagement volume.

Agentic AI addresses three capability gaps that directly impact capacity. It eliminates repetitive high-volume tasks like document collection and control validation, provides continuous compliance monitoring rather than point-in-time assessments, and identifies compliance risks before external auditors discover them. These capabilities allow firms to handle more concurrent certifications without proportional headcount increases.

This shift represents a competitive advantage as regulatory frameworks evolve. ISO/IEC 42001:2023 now provides governance standards for AI systems, while the EU AI Act Article 14 establishes mandatory human oversight requirements. Firms implementing AI within these frameworks early gain capacity to expand their ISO practice while maintaining audit quality and regulatory compliance.

1. Automate compliance evidence collection

Manual evidence gathering creates bottlenecks across concurrent ISO audits. Managers spend hours updating spreadsheets to track which controls have complete testing documentation and which still need evidence. When evidence requests, test procedures, and workpaper reviews occur in disconnected systems, engagement status remains invisible until someone manually compiles reports.

Agentic AI transforms this workflow by streamlining evidence collection within the audit engagement platform. Practitioners initiate point-in-time data pulls from client systems (ERP platforms, security tools, document repositories) to gather engagement evidence. The system organizes uploaded evidence within the engagement, assists with tagging evidence to specific ISO clauses, and validates for inconsistencies before managers begin review cycles.

Practical implementation requires several key components:

API access to source systems: Direct integration with ERP platforms, security monitoring tools, and document repositories allows automated data extraction without manual downloads or file transfers.
Data governance protocols: Clear policies define what gets collected, retention periods, and access controls to maintain confidentiality and regulatory compliance.
Validation protocols: Human auditors verify AI-generated evidence mappings before relying on them during external audits, ensuring accuracy and professional judgment application.

Platforms like Fieldguide's engagement automation system help practitioners manage evidence collection across concurrent engagements through agentic AI that executes substantive procedures within assessor-defined parameters, while also providing AI-assisted drafting of test procedures and evidence analysis under professional oversight.

2. Use real-time KPI monitoring

Annual or quarterly audit cycles create compliance gaps. Organizations demonstrate control effectiveness at a single point in time, then operate without independent monitoring until the next assessment. Agentic AI shifts compliance from point-in-time to continuous monitoring, with alerts on deviations flagged as they occur rather than months later during fieldwork.

The IIA's GTAG-3 guidance establishes the professional framework for continuous auditing. Firms must integrate continuous monitoring into audit planning, develop forward-looking risk indicators, and coordinate between engagement phases and ongoing monitoring.

Cross-standard monitoring capabilities

AI systems integrate directly with operational systems that generate compliance data across different ISO standards:

ISO 27001 information security: Systems monitor access control violations continuously, flagging suspicious patterns that might indicate compromised credentials.
ISO 45001 occupational health and safety: Health and safety control assessments, incident documentation review, and risk evaluation procedures maintain consistent tracking across engagements.

This approach catches issues earlier, giving organizations time for corrective action before annual audits. Instead of relying on retrospective testing at a single point in time, firms demonstrate compliance through documented year-round monitoring that provides stronger evidence of control effectiveness.

Market adoption trends

Research shows assurance leaders adopting automation for high-volume, low-value tasks expect to significantly increase technology spend through 2027 as organizations prioritize AI and analytics capabilities. Gartner forecasts that AI agents will augment or automate 50% of business decisions by 2027, with executive AI literacy emerging as a performance differentiator driving higher financial outcomes for departments that invest early.

Purpose-built platforms support this shift. Fieldguide's Engagement Hub provides integrated workflows and continuous monitoring across ISO 27001, ISO 14001, and ISO 45001 frameworks, helping practitioners maintain real-time visibility into control performance across concurrent certifications.

3. Identify risks with predictive analytics

Machine learning models assign probability scores to control failures, flag deviations from established baselines through anomaly detection, and prioritize areas where auditors should focus limited fieldwork hours. Predictive analytics differs from descriptive analytics, instead of showing last year's control failures, ML predicts which controls face elevated failure risk this year.

Technical approach

Research on ML-based enterprise financial audit frameworks shows primary ML models include Support Vector Machine, Random Forest, and K-Nearest Neighbors. These models analyze audit indicators including project counts, high-risk cases, historical violations, detected fraud, workload, and satisfaction scores to generate risk predictions.

Practical application

A manufacturing organization maintains equipment performance data as part of ISO 9001 quality management requirements. Historical maintenance logs show current equipment performance patterns similar to those preceding past failures. The AI system flags this degradation four weeks before the scheduled audit, allowing the organization to perform corrective maintenance that prevents what would have become an audit finding for inadequate preventive maintenance controls.

Critical limitations

Predictions are probabilistic, not deterministic. A 75% probability of control failure means the control might still function properly. Human auditors must validate predictions through professional judgment and testing rather than treating ML outputs as definitive assessments.

4. Enhance compliance with document management

Natural language processing reads policy documents the way humans do, extracting meaning from text. The system ingests hundreds of procedures, maps each to specific ISO requirements, then flags gaps where required policies are missing. Version tracking identifies changes that might introduce compliance issues before external auditors discover them.

Organizations maintaining ISO certifications across multiple standards manage hundreds to thousands of policies scattered across document systems, SharePoint sites, and department file shares.

The realistic outcome: upload 500 procedures, and the system flags 23 gaps that would have surfaced as audit findings:

Security procedures: Missing coverage for ISO 27001 Annex A controls
Quality procedures: Lacking required process performance metrics
Environmental procedures: Missing operational criteria specifications

Auditors review flagged gaps, determine whether they represent actual nonconformities or false positives from semantic interpretation errors, and update documentation before external assessment. This represents a lower-risk AI use case compared to evidence automation or predictive analytics, making it suitable for pilot implementations where audit and advisory firms want to demonstrate value before expanding to higher-stakes applications. Fieldguide's document management capabilities help practitioners organize and analyze policies through AI-powered review under professional oversight.

5. Enhance efficiency with AI-generated audit checklists

Traditional audit checklists follow one-size-fits-all templates—every ISO 27001 audit uses the same control questions regardless of the organization's risk profile or previous performance. Agentic AI planning works differently. The system analyzes past audit findings to predict high-risk areas, then generates checklists weighted toward those risks.

Risk-based resource allocation

Auditors spend time on high-risk areas instead of routine checks that consistently pass. When previous audits failed supplier management controls, AI weights supplier questions higher in the next assessment, allocating additional testing time where control failures are more likely. Research shows natural language processing can create audit checklists by scanning past audit reports and regulatory requirements.

AI platforms generate checklists in two ways:

Template customization selects relevant sections from comprehensive control databases based on audit scope. This approach offers more predictable results with clearer audit trails, making it easier to show external auditors how questions were selected.
Fully generative approaches create custom questions from scratch using historical patterns and current context. This approach provides greater flexibility but needs more rigorous human review because of documented AI hallucination risks.

Professional audit guidance requires human auditor validation before using any AI-generated content in certification decisions. Firms need data from at least three to five prior audit cycles before AI can establish meaningful patterns for question generation.

What are the limitations and risks of AI in auditing?

External auditors cannot adequately verify AI-generated conclusions when they lack access to model internals, creating fundamental audit evidence reliability problems.

Organizations deploying AI in audit programs face four critical limitations:

Hallucinations and bias: Training data patterns can produce serious errors in risk assessments when systems misinterpret customer profiles or market trends.
False positives and negatives: AI bias overwhelms analysts with false alerts or misses critical threats, directly affecting audit conclusion reliability.
Perpetuated systemic biases: Models trained on past audit decisions replicate and amplify human biases embedded in those decisions.
Vendor capability failures: Gartner's October 2025 survey found 45% of leaders report vendor AI capabilities fail to meet promised performance.

Before deployment, validate accuracy on known outcomes, track false positive and negative rates, and document governance frameworks comprehensively. External auditors require proof of validation procedures and human oversight controls.

How to implement AI in your integrated ISO audit program

Successful AI implementation requires realistic planning and disciplined execution. Over 40% of agentic AI projects will be canceled by the end of 2027 due to underestimated costs and deployment complexity, making a structured approach essential.

Follow this implementation framework:

Assess readiness across five dimensions: Leadership alignment on AI strategy, current data quality and infrastructure, technology stack compatibility, cultural readiness for adoption, and workforce AI literacy. Research shows structured evaluation across these dimensions predicts implementation success.
Pilot with bounded use cases: Start with ITGC password control tests, journal entry transaction reviews, or department budget analysis. These provide clear success criteria and manageable scope before enterprise-wide deployment.
Plan realistic timelines: Readiness assessment takes 2-3 months, pilot programs require 3-6 months, initial production deployment needs 6-12 months, and scaled implementation spans 18-24 months. These contrast sharply with typical vendor estimates of 3-6 months for "full deployment."
Measure what matters: Track cycle time reduction for evidence collection, error rate improvement in control testing, staff adoption rates, and user satisfaction scores. Demand proof of tangible business outcomes rather than accepting vendor claims.
Document for external auditors: Maintain AI governance frameworks aligned with ISO/IEC 42001, demonstrate validation controls, evidence human oversight requirements, and provide clear audit trails.

This structured approach establishes the governance foundation while delivering measurable efficiency improvements.

Making AI work within your audit practice

The fundamental question for audit and advisory firms isn't whether to adopt AI for ISO audits, but how to implement it responsibly. Start with lower-risk use cases like document validation and evidence automation that deliver results within months. Build governance frameworks aligned with ISO/IEC 42001:2023 before external auditors require them.

Most importantly, treat AI as augmentation rather than replacement. The technology can handle repetitive tasks while human auditors maintain professional judgment on control effectiveness and certification decisions. Firms that establish this foundation now will have measurable capacity advantages over those still operating entirely manually.

For audit and advisory firms conducting integrated ISO audits, Fieldguide's engagement automation platform manages multiple concurrent certifications. Field Agents execute substantive procedures within practitioner-defined parameters, while Engagement Hub supports continuous monitoring across ISO 9001, ISO 27001, ISO 14001, and ISO 45001 frameworks.