AI-Augmented Auditing: Systems & Impact

• AI-Augmented Auditing is the integration of AI technologies (LLMs, ML classifiers) with human expertise to systematically detect anomalies and manage risks.
• It employs modular architectures and mixed-initiative workflows, as seen in systems like AdaTest++ and AuditCopilot, to generate tests and identify financial irregularities.
• By merging real-time analytics with human oversight, the approach scales traditional audits, enhances compliance, and mitigates both known and unforeseen risks.

AI-augmented auditing is the systematic integration of artificial intelligence—most prominently LLMs, machine learning classifiers, and AI agents—into the workflows, tools, and protocols of auditing. This integration supports, automates, and scales the detection and analysis of failure modes, anomalous behavior, and risks in both AI and traditional enterprise systems. AI-augmented auditing is now central in domains including financial compliance, LLM safety, generative model evaluation, regulatory conformance, and workforce task assessment. Rigorous system designs synthesize computational algorithms, HCI principles, and collaborative models to combine human insight with AI’s generative and discriminative capabilities.

1. Foundational Motivations and Scope

The deployment of AI systems across critical, high-stakes sociotechnical domains has catalyzed demand for more rigorous and scalable auditing workflows. Human-only approaches—such as red-teaming, formal reviews, or manual Journal Entry Tests (JETs)—struggle to keep pace with the volume, complexity, and subtlety of both AI-driven harm and financial irregularities. Conversely, purely AI-driven audits are limited by lack of domain expertise, context synthesis, or real-time oversight, and risk missing unanticipated ("unknown unknown") issues such as under-reported biases or edge-case failures (Rastogi et al., 2023, Kadir et al., 2 Dec 2025). The core rationale for AI-augmented auditing is to combine human strengths—schematization, critical assessment, domain authority—with AI's abilities for rapid test generation, scalable anomaly detection, and structured exploration, yielding superior breadth and depth in risk discovery.

2. Architectures and System Designs

AI-augmented auditing architectures are distinguished by modular, interactive, and data-centric designs. Examples include:

• AdaTest++ (Rastogi et al., 2023): A mixed-initiative workflow where auditors seed topics and tests, receive LLM-driven suggestions, apply outcome labels (“Pass,” “Fail,” “Not Sure”), and organize tests in a tree-structured hierarchy. Prompt panels accept both free-form instructions and distilled prompt templates (T1–T5) to operationalize human hypotheses and exploratory directions.
• Vipera (Huang et al., 7 Oct 2025): A client-server system for auditing text-to-image models, combining a zoomable scene-graph visualization with LLM-powered criteria/prompt suggestions, structured evidence collection, and tightly coupled UI guidance to support exploration of vast multimodal output spaces.
• AuditCopilot (Kadir et al., 2 Dec 2025): A prompt-driven LLM pipeline for anomaly detection in double-entry bookkeeping, integrating structured financial records, precomputed anomaly hints (e.g., Isolation Forest scores), and LLM-based binary/explanatory output to surface suspicious transactions.
• Enterprise Financial Risk Framework (Yuan et al., 8 Jul 2025): A layered data pipeline with machine learning classification at its core (notably a 200-tree Random Forest), coupled to real-time streaming inputs, feature engineering, risk scoring APIs, and dashboard-based alerting for audit and compliance tracking.

Central to these systems are closed-loop workflows, real-time data ingestion, and micro-batch updates, with audit actors able to steer, filter, and interpret AI output as well as inject new hypotheses or organizational schemas.

3. Core Methodologies and Algorithms

Numerous algorithmic frameworks are harnessed for AI-augmented auditing. Select examples:

• LLM-Guided Test Generation (AdaTest++, Vipera): LLMs are prompted—with either hand-crafted, user-customized, or template-based instructions—to generate variations of test cases, criteria, or prompts for systematic coverage of the AI input–output space (Rastogi et al., 2023, Huang et al., 7 Oct 2025). Templates such as T1–T5 in AdaTest++ parameterize output style or input features, enabling both slice-based hypothesis testing and broad exploration.
• Supervised/Unsupervised ML for Risk Detection: Random Forests (F1=0.9012), SVMs (mean F1=0.8756), and KNNs (F1=0.8545) have been benchmarked on multi-source enterprise audit data, with the Random Forest algorithm yielding the best fraud/compliance anomaly detection owing to its high-dimensional feature handling and overfitting resistance (Yuan et al., 8 Jul 2025). Key derived features include audit frequency, historical violation ratio, and employee workload. Class-imbalance is addressed with SMOTE.
• LLM-Prompted Anomaly Detection in Bookkeeping: AuditCopilot leverages LLMs conditioned by a hybrid prompt comprising transaction data, global ledger statistics, rule-based anomaly hints, and explicit anomaly criteria (Kadir et al., 2 Dec 2025). Performance is evaluated via precision, recall, and F1: best F1=0.94 (Mistral-8B) on synthetic data, 0.83 (Gemma-7B) on private data, both exceeding classical JETs/Isolation Forest baselines.
• Zero-Knowledge Proofs for Regulatory Auditing: ZKMLOps embeds cryptographic ZKPs into MLOps lifecycles for high-trust auditability that preserves confidentiality. Succinct non-interactive protocols (Groth16, STARK, ezkl) are benchmarked according to proof generation/verification time and size; for example, Groth16 achieves proof sizes of ~800 B with ms-level verification latency, enabling scalable, provenance-preserving audits on model inferences under regulatory regimes (e.g., EU AI Act) (Scaramuzza et al., 30 Oct 2025).

4. Human–AI Collaboration and Interaction Paradigms

Effective AI-augmented auditing depends on explicit frameworks for human–AI collaboration, drawing on sensemaking theory and mixed-initiative HCI research (Rastogi et al., 2023). Key elements observed across systems:

• Foraging and Sensemaking Loops: Auditors iteratively gather data or test cases (foraging), reflect on patterns or failures, and formalize/refine hypotheses (sensemaking)—with AI systems offering candidate directions, surfacing surprises, or instantiating hypotheses at scale.
• Labeling Controls and Ambiguity Management: Audit systems include explicit mechanisms for auditors to defer judgment (“Not Sure”), surface task ambiguity, and adjust prompt context in light of under-specified cases.
• Collaborative Reporting and Discussion: Tools like WeAudit scaffold report generation using structured prompts that capture not only observed harms but also the reasoning and population affected, and then promote collaborative sensemaking via peer verification and discussion threads (Deng et al., 2 Jan 2025).
• Human Agency Scales: The Human Agency Scale (HAS) formalizes the division of labor between human and AI actors, from fully automated (H1) to essential human-in-the-loop (H5), allowing quantifiable mapping of task preferences and technical feasibility gaps (Shao et al., 6 Jun 2025).

5. Evaluation Protocols and Empirical Findings

Systematic evaluation of AI-augmented auditing systems employs a combination of user studies with domain experts or practitioners, structured scenario/task assignments, quantitative audit-coverage and failure discovery metrics, and qualitative transcript/thematic coding (Rastogi et al., 2023, Huang et al., 7 Oct 2025, Kadir et al., 2 Dec 2025).

Select empirical results:

System	Modality	Key Performance Metric(s)	Outcome/Highlight
AdaTest++	LLM text	Tests/min, Fails/min	~1.7 tests, ~0.8 fails/min; 80% failures via LLM
AuditCopilot	Financial	F1 (synthetic/private), Prec	Mistral-8B F1=0.94; Gemma-7B F1=0.83
Enterprise RF	Financial	F1, Accuracy, Recall	RF F1=0.9012, best among SVM and KNN
Vipera	T2I images	#Criteria, NASA-TLX scores	D (full system) doubles criteria vs. baseline
WeAudit	T2I images	Time to 1st report, Tag dist.	~12 min, 82.6% pairwise mode, 56% stereotyping tags

Audits have uncovered: identity-based biases, allocational and representational harms, logic/arithmetic failures, misinformation, and previously under-reported failure cases (Rastogi et al., 2023, Huang et al., 7 Oct 2025). Integrated explanation delivery (e.g., AuditCopilot’s JSON explanations) directly supports triage and interpretability.

6. Applications and Generalizations

AI-augmented auditing is widely applied in:

• Enterprise Risk and Compliance: Real-time monitoring, fraud detection, compliance alerts, and workforce risk management with ML classifiers supported by real-world enterprise data lakes (Yuan et al., 8 Jul 2025).
• Financial Ledger Review: Double-entry bookkeeping anomaly detection, leveraging LLMs for nuanced statistical and semantic explanations beyond rule-based or classic ML baselines (Kadir et al., 2 Dec 2025).
• Generative AI Safety and Fairness: Auditing LLM classifiers, text-to-image systems, and broader generative models for bias, logic failure, representational, and allocational harms, using interactive tools and sensemaking workflows (Rastogi et al., 2023, Huang et al., 7 Oct 2025, Deng et al., 2 Jan 2025).
• Workforce/Task Automation Audits: Quantifying and aligning human and expert preferences for task automation vs. augmentation across broad occupational datasets, supporting policy and organizational planning (Shao et al., 6 Jun 2025).

Systems like Vipera demonstrate the generalizability of mixed-initiative, LLM-augmented exploration strategies to any generative model audit, while ZKMLOps prototypes cryptographic compliance for regulated high-risk AI across ML contexts (Scaramuzza et al., 30 Oct 2025).

7. Open Challenges and Future Directions

Current limitations include lack of universally verified audit labels (AuditCopilot), prompt-sensitivity, prompt-source drift, explainability gaps for complex model outputs, and the need for richer collaborative scaffolding (AdaTest++, WeAudit). Proposed future work includes integration of domain-specific rules or regulatory constraints into LLM prompts, deeper provenance and traceability for audit findings, generalized cryptographic attestation for model behavior, and multi-modal extensions of sensemaking and reporting workflows (Rastogi et al., 2023, Kadir et al., 2 Dec 2025, Scaramuzza et al., 30 Oct 2025).

A plausible implication is that as auditing practice matures, operational maturity will require automated and human-augmented evidence pipelines, rigorous collaboration scaffolds, and adaptive model governance covering both algorithmic and sociotechnical failure surfaces. This trajectory is reinforced by ongoing empirical validation that AI-augmented auditing enhances speed, breadth, coverage, and actionability over purely human or automated alternatives (Rastogi et al., 2023, Huang et al., 7 Oct 2025, Kadir et al., 2 Dec 2025).