AI-Powered Internal Audits The Next Step in Digital Quality

When algorithms join the audit team The traditional internal audit has been an episodic, labor-intensive exercise: teams of auditors sampling records, interviewing stakeholders, mapping processes and drawing conclusions reflected in a report that triggers corrective and preventive actions. That model served industry for decades, but quality systems are increasingly data-rich and digitally connected. For regulatory affairs professionals charged with assurance and inspection readiness, artificial intelligence (AI) offers an inflection point - not a panacea. AI-powered internal audits promise to transform the cadence, scope and depth of oversight by enabling continuous, risk-based surveillance of processes and artefacts. Yet the opportunity comes with a proportional set of governance, validation and regulatory considerations that must be addressed deliberately. Regulatory context and the imperative for digital quality Regulators have signaled a steady, if careful, openness to modernized approaches to computerized system assurance. Existing frameworks - 21 CFR Part 11, EU GMP Annex 11, PIC/S guidance and overarching principles in GAMP 5 - provide the regulatory scaffolding for computerized and automated systems in regulated environments. More recent agency guidance and initiatives encourage risk-based approaches to software assurance and recognize the role of continuous monitoring in maintaining quality. Simultaneously, global regulatory attention on AI and machine learning (for instance, ongoing policy developments such as the EU AI Act and FDA discussion documents on AI/ML-enabled medical devices) underscores that AI used in regulated decision-making must be subject to transparency, performance control and lifecycle governance. For regulatory affairs functions, the imperative is twofold. First, digital quality - in the form of electronic records, automated workflows and integrated systems - must be verifiable and defensible to inspectors. Second, when AI becomes part of the audit toolchain, it becomes part of the regulated ecosystem: the outputs inform regulatory decisions, and the method used to generate audit evidence will itself be scrutinized. Recognizing AI as an enabler of deeper, timelier insight must be accompanied by equal recognition that it reshapes compliance obligations. How AI transforms internal audit practices AI augments audit capabilities across several dimensions. Natural language processing (NLP) accelerates document review, enabling extraction of regulatory obligations, cross-referencing of procedures with training records and identification of deviations between documented and practiced processes. Anomaly detection algorithms sift through manufacturing process data or electronic quality management system (eQMS) logs to flag patterns unusual enough to justify investigation. Predictive models prioritize CAPA candidates by likelihood of recurrence or patient impact, helping allocate scarce remediation resources more effectively. Network and graph analysis reveal supplier interdependencies and systemic risk exposures beyond what a manual review would surface. Most importantly, AI facilitates a shift from periodic sampling to near-continuous assurance. Instead of auditing a handful of batches or records retrospectively, AI can continuously monitor pre-defined risk indicators and surface signals when they cross thresholds. That enables faster detection and response to quality trends, potentially reducing the scale and recurrence of nonconformities. For regulatory affairs, this continuous posture enhances inspection readiness because it shortens the window between deviation occurrence and detection, increasing the quality of preserved evidence and the organization's ability to narrate corrective actions. Governance, validation and change control: the regulatory rubicon Embedding AI into an internal audit program requires a disciplined governance structure. From a regulatory standpoint, the AI component should be treated as part of the computerized system landscape: documented requirements, traceable design and rigorous lifecycle control are non-negotiable. Validation must be risk-based, commensurate with the system's influence on quality decisions. The GAMP risk-based mindset applies: critical functionality and decision points must be demonstrated as fit for intended use. Validation of AI differs from validation of deterministic software because trained models can behave probabilistically and drift over time as input distributions change. The validation approach therefore needs to include not only initial performance qualification against labeled datasets and known scenarios, but also post-deployment monitoring plans, retraining triggers, and procedures for rollback or human escalation when model confidence is low or outputs fall outside acceptance criteria. Version control, reproducible training pipelines, and documented data provenance form the backbone of auditability. Change control becomes a central compliance consideration. Model updates, hyperparameter tuning, or retraining processes must be governed through controlled change management workflows. For regulated functions, there should be explicit linkage between algorithmic changes and review/approval steps that involve quality, regulatory, and domain experts. Demonstrating control over algorithm evolution will likely be as important to inspectors as showing that the initial model was validated. Data integrity, explainability and inspection readiness Data integrity remains the cardinal compliance principle. AI systems are only as reliable as the data used to train, validate and feed them. Regulatory affairs professionals must ensure the inputs are complete, consistent, and traceable to source systems that meet applicable controls. Logs of data ingestion, transformations, and feature engineering steps should be retained and accessible for inspection. Explainability of AI outputs is another practical and regulatory concern. Inspectors will seek to understand why an algorithm flagged certain records or prioritized specific actions. That does not require revealing proprietary model internals in full, but it does require providing interpretable evidence: clear documentation of model inputs, the logic or heuristics that map signals to flags, representative examples of true positives and false positives, and metrics that contextualize performance (sensitivity, specificity, precision, recall). Tools such as model cards or auditability artifacts can help packetize this information for inspection review. Operationalizing AI: practical roadmap for regulatory affairs Adoption should begin with a pragmatic, staged approach informed by regulatory risk appetite. Pilot projects in low-to-moderate risk areas - for example, automating document cross-checks between SOPs and training records, or using NLP to triage incoming nonconformance reports - provide testing grounds without exposing critical control points. Pilots should be designed with clear acceptance criteria, labeled test sets, and pre-defined governance touchpoints that include regulatory input. Cross-functional teams are essential. Regulatory affairs, quality, IT, data science and the business process owners must collaborate from scoping through deployment. Regulatory owners should articulate how AI outputs will feed regulatory narratives, what artefacts must be retained, and how model performance will be reflected in regulatory reporting. Legal and privacy teams must assess the use of personal data under GDPR or equivalent regimes, and cybersecurity teams should vet models for adversarial vulnerabilities and data leakage. A sound operational model includes continuous performance monitoring and escalation. Thresholds for alerting human reviewers should be conservative during early deployment, with transparent mechanisms for annotating false positives and feeding those annotations back into the model lifecycle. Documentation that maps AI outputs to audit conclusions - including rationale for human overrides - will be crucial when defending the outcome during inspections. Ethical and regulatory risks to monitor The use of AI in internal audits raises ethical considerations that intersect with regulatory obligations. Automation bias - the tendency of humans to defer to algorithmic recommendations - can erode critical professional skepticism if not actively managed. Regulatory affairs professionals should ensure that AI augments rather than replaces human judgment, with clear governance around when human intervention is mandatory. Bias in training data can skew audit outcomes, for instance by systematically under-detecting issues in certain facilities or product lines. Robustness testing against diverse data, and deliberate efforts to uncover and correct biases, mitigate this risk. Privacy implications - especially when audits touch on personnel records, patient-level data, or supplier commercial information - require careful legal oversight and data minimization strategies. Measuring success and continuous improvement Success metrics for AI-enabled audits must capture both technical performance and regulatory value. Technical metrics include detection accuracy, false positive/negative rates, latency of alerting, and model drift indicators. Regulatory value metrics are arguably more consequential: reduction in time-to-detection of quality issues, improvement in completeness of audit evidence, decreased recurrence of systemic nonconformities, and inspector feedback regarding transparency and readiness. Continuous improvement demands a learning loop: false positives and misses must be systematically analyzed, contributing to model retraining or process changes. Equally important is the human process improvement that arises from AI insights. When algorithms reveal previously unseen process interdependencies or data quality gaps, organizations should translate those insights into sustainable corrective actions and process redesign. Inspection dynamics and regulatory dialogue Regulators are learning alongside industry. Organizations that proactively prepare documentation describing AI roles in their audit ecosystem - system architecture, validation evidence, performance monitoring plans and human oversight mechanisms - are better situated in inspections. Where AI materially influences regulatory submissions or quality decisions, early engagement with regulators through established mechanisms (e.g., pre-submission meetings, inspection readiness discussions, or regulatory intelligence channels) can clarify expectations and reduce surprise. Transparency, rather than opacity, will likely be rewarded. Providing regulators with examples of audit outputs, traceable decision logs, and an explanation of governance controls communicates confidence and control. Conversely, trying to obscure AI involvement or provide insufficient artefacts risks eroding trust and inviting deeper scrutiny. Anchoring principles for adoption AI-powered internal audits represent a natural evolution of digital quality systems, offering richer, timelier insights and the potential for continuous assurance. For regulatory affairs professionals, the opportunity will be realized only by anchoring adoption in a set of core principles: treat AI as part of the regulated system and govern it accordingly; adopt a risk-based and staged validation approach; preserve data integrity and auditability; maintain human oversight and skepticism; and document transparency for inspection and regulatory dialogue. Organizations that internalize these principles and invest in governance, cross-functional capability and thoughtful validation will find that AI changes the nature of audit from an episodic compliance exercise into an ongoing, intelligence-driven competency. The result is not merely greater efficiency but a higher-quality defence posture - one that aligns with both current regulatory expectations and the forward trajectory of digital quality. Regulatory affairs will be central to that transition: translating algorithmic outcomes into regulatory narratives, defending the integrity of AI-enabled evidence, and shaping how oversight evolves in an era where the audit team includes both seasoned auditors and sophisticated algorithms.