IntelliAudit: Using Large Language Models to Evaluate Audit Controls

• URL: http://arxiv.org/abs/2608.07688v1
• Date: Fri, 07 Aug 2026 18:21:06 GMT
• Title: IntelliAudit: Using Large Language Models to Evaluate Audit Controls
• Abstract summary: We present IntelliAudit, a retrieval-grounded multi-agent system for IT audit evidence evaluation.<n>Given a control and an evidence corpus, IntelliAudit retrieves relevant artifacts, generates an evidence-grounded assessment.<n>We evaluate it across multiple simulated organizations using expert review and audit-readiness user feedback.
• Score: 0.1759008116536278
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: IT audits require auditors to judge whether heterogeneous organizational evidence satisfies semantic security and compliance controls. This judgment is difficult to automate because relevant evidence is distributed across policies, records, spreadsheets, and operational artifacts, and because audit conclusions depend on evidentiary sufficiency rather than keyword matching. We present IntelliAudit, a retrieval-grounded multi-agent system for IT audit evidence evaluation. Given a control and an evidence corpus, IntelliAudit retrieves relevant artifacts, generates an evidence-grounded assessment, challenges adverse findings, adjudicates disagreements, and produces an auditor-facing recommendation with cited evidence, rationale, missing-evidence analysis, and remediation guidance. We instantiate IntelliAudit on ISO/IEC 27001 and evaluate it across multiple simulated organizations using expert auditor review and audit-readiness user feedback. The evaluation shows that IntelliAudit can support control interpretation, evidence-grounded reasoning, and audit-preparation workflows, while also revealing the importance of human oversight for calibrating sufficiency judgments and correcting overly permissive recommendations. These results suggest that retrieval-grounded multi-agent systems can assist audit evidence review, but should remain decision-support tools rather than autonomous certification systems.

Related papers

• VERA-8B: Evidence-Grounded Audit Risk Reasoning from SEC Filings [0.6445605125467574]
  We develop VERA-8B, a new end-to-end audit reasoning system.<n> VERA-8B identifies audit risks before enforcement actions occur.<n>We are the first to unify SFT and GRPO for evidence-grounded audit reasoning.
  arXiv  Detail & Related papers  (2026-08-28T14:55:49Z)
• From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents [47.678562263387214]
  Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings.<n>Yet a completed trajectory is not automatically evidence: generated artifacts may be unsupported or incomplete, executed rounds may be invalid or confounded, and later modifications may obscure earlier findings.<n>We introduce an evidence-grounded framework that couples bounded verification of consequential artifacts with post-execution claim qualification.
  arXiv  Detail & Related papers  (2026-08-05T13:37:38Z)
• Manipulation-Proof Oblivious Audits against Deceptive Model Providers [4.614773550999029]
  In regulatory contexts, audits are typically declared or easily detected, enabling model providers to manipulate the process.<n>This vulnerability is particularly acute in the context of fairness evaluations.<n>We introduce a novel audit protocol designed to significantly increase the post-audit detectability of such manipulations.
  arXiv  Detail & Related papers  (2026-08-05T02:12:54Z)
• Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents? [61.49434544687523]
  We introduce REFLECT, a meta-evaluation benchmark targeting fine-grained failure detection in agentic environments.<n> REFLECT defines a detailed taxonomy of process- and outcome-level failure modes, instantiated by performing controlled and localized interventions.<n>Our experiments show that even the best-performing models achieve overall accuracies below 55% across reasoning, tool-use, and report-quality failures.
  arXiv  Detail & Related papers  (2026-05-18T23:55:08Z)
• Making AI-Assisted Grant Evaluation Auditable without Exposing the Model [0.0]
  We propose a TEE-based architecture that helps reconcile requirements through remote attestation.<n>The architecture allows an external verifier to check which model, rubric, prompt template, and input representation were used.<n>We position the design relative to confidential AI inference, attestable AI audits, zero-knowledge machine learning, algorithmic accountability, and AI-assisted peer review.
  arXiv  Detail & Related papers  (2026-04-28T04:10:04Z)
• When AI reviews science: Can we trust the referee? [73.47745294608072]
  We map attacks across the review lifecycle -- training and data retrieval, desk review, deep review, rebuttal, and system-level.<n>We use two advanced LLM-based referees to isolate the causal effects of prestige framing, assertion strength, rebuttal sycophancy, and contextual poisoning on review scores.
  arXiv  Detail & Related papers  (2026-04-26T08:03:32Z)
• DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality [49.62610727661819]
  Existing fact-checkers are primarily designed for general-domain, factoid-style atomic claims.<n>We propose Audit-then-Score (AtS), where benchmark labels and rationales are explicitly revisable.<n>We instantiate AtS as DeepFact-Bench, a versioned DRR factuality benchmark with auditable rationales.
  arXiv  Detail & Related papers  (2026-03-06T05:05:57Z)
• Preventing the Collapse of Peer Review Requires Verification-First AI [49.995126139461085]
  We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
  arXiv  Detail & Related papers  (2026-01-23T17:17:32Z)
• PaperAudit-Bench: Benchmarking Error Detection in Research Papers for Critical Automated Peer Review [54.141490756509306]
  We introduce PaperAudit-Bench, which consists of two components: PaperAudit-Dataset, an error dataset, and PaperAudit-Review, an automated review framework.<n>Experiments on PaperAudit-Bench reveal large variability in error detectability across models and detection depths.<n>We show that the dataset supports training lightweight LLM detectors via SFT and RL, enabling effective error detection at reduced computational cost.
  arXiv  Detail & Related papers  (2026-01-07T04:26:12Z)
• Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Study [0.0]
  We argue that the efficacy of an audit depends on the auditability of the audited system. We present a framework for assessing the auditability of AI-integrating systems.
  arXiv  Detail & Related papers  (2024-10-29T13:43:21Z)
• From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing [1.196505602609637]
  Audits can take many forms, including pre-deployment risk assessments, ongoing monitoring, and compliance testing. There are many operational challenges to AI auditing that complicate its implementation. We argue that auditing can be cast as a natural hypothesis test, draw parallels hypothesis testing and legal procedure, and argue that this framing provides clear and interpretable guidance on audit implementation.
  arXiv  Detail & Related papers  (2024-10-07T06:15:46Z)
• Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem [0.971392598996499]
  We provide the first comprehensive field scan of the AI audit ecosystem. We identify emerging best practices as well as methods and tools that are becoming commonplace. We outline policy recommendations to improve the quality and impact of these audits.
  arXiv  Detail & Related papers  (2023-10-04T01:40:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.