Data integrity has been a regulatory focus for more than a decade, but 2026 raises the stakes. Laboratories are now increasingly AI-enabled, cloud-connected and automated, with instruments, LIMS, ELNs and analytics platforms continuously collecting and processing vast amounts of GxP data. That creates huge opportunities for efficiency and insight, but also introduces new risks to data reliability, traceability and regulatory compliance.
For pharma QA and QC teams, the key question is no longer just “Is our data ALCOA+?” but “Can we demonstrate robust data integrity across complex, interconnected and algorithm-driven systems?”
Data integrity in 2026: expectations are expanding, not fading
Regulators have not relaxed their stance on data integrity. EMA, FDA and MHRA continue to emphasise that reliable data underpins every quality decision, product release and patient safety outcome. Guidance such as the MHRA’s GxP data integrity expectations and industry interpretations from PDA and ISPE, reinforce familiar ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate plus Complete, Consistent, Enduring and Available), but now apply them to far more complex digital environments.
In practice, this means:
- Audit trails are expected to be enabled, reviewed and meaningful, not just technically present.
- Electronic systems must support role-based access, segregation of duties and secure time-stamps.
- Paper-to-electronic hybrids are increasingly seen as fragile and error-prone.
In an AI-enabled lab, the same principles apply, but must also cover datasets, training data, model versions and algorithm outputs.
AI and advanced analytics: new opportunities, new risks
Laboratories are adopting AI and machine learning to:
- Flag out-of-trend results and pattern anomalies
- Optimise instrument maintenance through predictive analytics
- Support root-cause investigations and deviation trending
- Automate data capture from instruments and reports
These tools can significantly strengthen quality decision-making, but only if the underlying data and governance are sound.
Key risks include:
- Opaque models (“black boxes”) where it is not clear how an output or recommendation was generated.
- Data provenance issues, uncertainty about which datasets trained a model, whether they included incorrect, biased or incomplete data and how they were curated.
- Uncontrolled algorithm changes, such as automatic model retraining that alters decision thresholds without formal change control.
- Fragmented audit trails, where laboratory systems, data lakes and AI platforms each hold partial traces of what happened.
From a data integrity perspective, any AI-driven lab process must be demonstrably traceable, reproducible and reviewable by humans.
Strengthening controls in the AI-enabled laboratory
To build robust data integrity in this environment, labs need to update both their technical controls and their quality culture.
a) Treat the data life cycle and the model life cycle together
Traditional data life-cycle thinking (generation, processing, review, reporting, archiving) must now sit alongside model life-cycle management. This includes:
- Clear definition of intended use for each AI tool (e.g. trend detection vs release decision support).
- Governance of training, testing and validation datasets, including versioning and access control.
- Change control for model updates, retraining and configuration changes, with documented impact assessment.
- Periodic performance monitoring to detect model drift and unexpected behaviours.
QA should ensure that models influencing GxP decisions are covered by the same validation logic as other GxP computerised systems, with a risk-based approach to testing and documentation.
b) Tighten audit trails and access management
In an AI-enabled lab, it is not enough to rely on instrument audit trails alone. Strong controls should ensure:
- End-to-end traceability from raw data through transformations, AI processing steps and final reports.
- Role-based permissions that prevent a single user from both generating and approving critical data.
- Regular audit-trail review procedures, focused on unexplained changes, overrides or data deletions.
- Secure time synchronisation across instruments, servers and cloud environments, so event sequences remain coherent.
Where data is exported to data lakes or analytics platforms, those environments must also meet GxP data integrity expectations.
c) Embed human oversight and critical thinking
Regulators are clear: AI can support, but not replace, human judgment in GxP environments. That means:
- Ensuring a “human in the loop” for critical quality decisions, especially product release and OOS assessments.
- Training analysts, reviewers and QA staff to understand how AI tools work at a conceptual level so they can challenge anomalous outputs.
- Documenting when and why AI recommendations are accepted or overridden, creating a transparent decision record.
This human oversight is central to both AI regulation (such as risk-based AI frameworks) and classical data integrity expectations.
Building a data integrity improvement roadmap for 2026
Rather than treating data integrity as a one-off remediation project, leading labs are building continuous improvement roadmaps that respond to the realities of digital and AI-driven work.
Practical steps include:
- Map your data and AI landscape
Catalogue which systems generate GxP data, where AI or advanced analytics are used and how information flows between platforms. - Update your risk assessments
Incorporate AI-specific risks (data drift, model bias, unapproved retraining) into existing data integrity and computerised system risk assessments. - Modernise SOPs and training
Revise SOPs to cover AI usage, data lineage, model change control and audit-trail review and then train staff with real-world scenarios rather than abstract rules. - Rationalise legacy processes
Where possible, migrate away from fragile, manual or hybrid paper–electronic processes that are hard to control in an AI-enabled ecosystem. - Monitor and review
Establish metrics for data integrity issues, audit-trail findings and AI-related deviations and use them to drive regular management review discussions.
Conclusion
In 2026, a strong data integrity posture is not just about ticking ALCOA+ boxes; it is about proving that your laboratory can generate, process and interpret data reliably in a highly digital, automated and AI-supported world. By aligning data and model life cycles, tightening controls around audit trails and access and reinforcing human oversight, pharma organisations can harness AI’s benefits while maintaining the trust of regulators and ultimately, the safety of patients.
If you’re looking to strengthen your Quality Assurance team, connect with QA Resources today. We’ll help you find experienced QA professionals who can support your compliance goals and keep your organisation inspection-ready.