AI and the Future of Audit: From Manual Testing to Full Population Coverage (Demo Room #12)

Welcome to The Demo Room – your front-row seat to the future of RegTech, RiskTech, and AI innovation. 

In this series, we document our research interviews with the most forward-thinking vendors tackling the industry's biggest challenges. Each blog is built around a comprehensive product demo, providing clear insights into how these innovations address industry challenges.

This edition features AuditBoard, a technology leader whose connected GRC platform is used by more than half of the Fortune 500 to modernize GRC and internal audit.

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Generative AI is reshaping business models and risk profiles, creating opportunities for efficiency and competitiveness while heightening exposure to cybersecurity threats, fraud, model risk, and data governance failures. The Institute of Internal Auditors notes a “palpable” urgency among audit leaders to understand, govern, and adopt AI, even as regulatory clarity, controls, and internal capabilities lag.

Elsewhere, regulatory expectations are intensifying. New frameworks – including DORA, NIS2, SEC cybersecurity disclosure rules, the EU AI Act, and evolving global standards for resilience, privacy, and financial reporting – require more granular testing, more frequent assurance, and clearer evidence of risk-control linkage.

But capacity remains limited. According to the 2024 North American Pulse of Internal Audit, 17% of audit functions have just 1–3 full-time employees, and another 35% operate with 4–9. In other words, more than half of all internal audit teams are small audit shops facing enterprise-scale expectations.

The Problem for Firms

Internal audit is being asked to deliver broader, faster, and more forward-looking compliance assurance. Yet most teams operate in fragmented, manually intensive environments. Controls sit across spreadsheets, GRC platforms, IT ticketing systems, ERP workflows, and business applications. Evidence is scattered. Ownership is inconsistent. Decisions rely heavily on interpretation rather than connected data.

This fragmentation makes it difficult to maintain a reliable, real-time view of control effectiveness. Audit teams spend disproportionate time collecting documents, reconciling inconsistencies, and validating information across multiple systems. Blind spots grow between audit cycles, where risks evolve quietly and control failures compound unnoticed.

Boards and regulators now expect near-real-time oversight of control health, not retrospective reports assembled months after testing. Yet only about 30% of audit functions have begun implementing continuous auditing, and nearly half have no plans to adopt it.

A Solution: AI for Full Population Coverage

AuditBoard addresses these challenges by unifying audit, risk, and compliance in a single connected environment – bringing together risks, controls, evidence, issues, and regulatory obligations in one governed architecture. This connected risk foundation enables AuditBoard’s AI strategy to perform with far greater accuracy and contextual awareness than generic LLM features layered on top of legacy systems.

AuditBoard is helping clients move beyond periodic sampling and towards full population coverage. Traditional audit methods capture only snapshots; AuditBoard enables analysis across entire control populations, surfacing anomalies and emerging patterns that would otherwise remain hidden. Domain-trained AI models accelerate this process by labeling evidence, mapping regulatory changes, highlighting gaps, and generating structured recommendations with confidence scores.

Rather than relying solely on generative AI, AuditBoard applies a calibrated blend of deterministic machine learning, retrieval-based models, and LLMs. Each capability is built for a specific GRC use case:

• LLM-powered summarization and comparison condense dense audit and control documentation, highlight overlaps and inconsistencies, surface themes across multiple audits, and generate cross-audit summaries that reveal recurring issues and systemic risks.

• Retrieval models automatically identify and link relevant risks, controls, and regulatory obligations from internal libraries, supporting consistent mapping and speeding up regulatory change assessments.

• Deterministic machine learning is used where structure and precision matter most, such as quantifying risk or scoring control effectiveness.

AuditBoard’s AI capabilities are embedded directly into core workflows, not bolted on as side modules or experimental add-ons. This ensures auditors can create, test, and verify controls without leaving the platform, dramatically reducing time spent gathering evidence or reconciling conflicting data. The upcoming ability to interpret scanned documents and annotated evidence will further expand coverage by reducing reliance on manual review.

Parker & Lawrence’s view

We expect continued growth in AI adoption among large, security-conscious enterprises, where brand trust, data governance, and model oversight are as critical as functionality. AuditBoard is well positioned: domain-specific AI training, strong adoption within the Fortune 500, and an architecture built for regulated environments provide meaningful advantages.

The providers that win in this space will be those that deliver governable innovation: fast deployment of practical AI capabilities, without compromising evidence trails, model transparency, or data isolation. AuditBoard’s strategy positions it well, with execution continuing to match the growing expectations of an AI-enabled audit market.

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