31 July 2025 · articles
Audit by Design: Securing AI-Driven Drug Development
The pharmaceutical industry is racing to harness AI's potential to transform drug discovery and development, but outdated regulations weren’t built for machine learning. Caught between innovation and compliance, companies must rethink how they build and govern AI in a tightly regulated, high-stakes environment.
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The pharmaceutical industry stands at a fascinating crossroads. AI promises to revolutionise drug discovery, slash development timelines from decades to years, and unlock treatments for previously intractable diseases. Yet the same regulatory frameworks that ensure patient safety, MHRA GxP Data Integrity Guidance and Definitions and EMA Annex 11, weren't designed with machine learning pipelines in mind.
The result? Pharmaceutical companies find themselves caught between innovation and compliance, trying to harness AI's transformative potential while maintaining the rigorous audit trails that regulators demand. It's a challenge that goes beyond traditional IT security - it's about reimagining how we build, deploy, and monitor AI systems in highly regulated environments.
The Regulatory Reality of AI in Drug Development
When the MHRA published its GxP Data Integrity Guidance and Definitions in 2018, artificial intelligence in pharmaceuticals was still largely theoretical. The guidance focused on ensuring the integrity of data in GxP-regulated environments, setting expectations for audit trails, system validation, and controls over both paper-based and electronic records. These principles were well-suited to traditional software systems used in pharmaceutical manufacturing, research, and quality control.
Fast forward to today, and AI systems present unique challenges that don't fit neatly into existing regulatory boxes. Machine learning models evolve continuously, data flows through complex pipelines, and decisions emerge from algorithms that can be difficult to explain. Yet regulators still expect the same level of traceability and accountability they've always demanded.
The EMA's Annex 11, which governs computerised systems in Europe, adds another layer of complexity. It requires comprehensive documentation of system lifecycles, change control procedures, and data integrity measures - all reasonable requirements that become significantly more challenging when applied to AI workloads.
Consider a typical AI-driven drug discovery pipeline. Data flows from multiple sources - clinical databases, genomic repositories, chemical libraries, through preprocessing algorithms, feature extraction systems, and predictive models. Each step transforms the data, and each transformation must be traceable, reproducible, and auditable. Traditional audit trails, designed for linear workflows, struggle to capture the complex interdependencies of modern AI systems.
Where Traditional Security Falls Short
Most pharmaceutical companies approach AI security the same way they've always handled IT security - bolt-on solutions applied after the fact. But AI systems operate differently from traditional applications, and these differences create new vulnerabilities that conventional security tools miss.
Take version control, a fundamental requirement for regulatory compliance. In traditional software development, version control is straightforward - you track changes to code, configuration files, and documentation. AI systems require tracking not just code changes, but also data versions, model parameters, training configurations, and the complex relationships between them. A model trained on version 2.1 of a dataset with hyperparameters adjusted for a specific clinical endpoint creates a unique artefact that must be completely traceable.
Then there's the explainability challenge. Regulators need to understand how AI systems reach their conclusions, particularly when those conclusions influence clinical decisions or regulatory submissions. Black box models that work brilliantly in other industries become liability risks in pharmaceuticals, where every decision must be defensible under regulatory scrutiny.
Data lineage presents another significant challenge. AI systems consume vast amounts of data from disparate sources, transform it through multiple processing steps, and generate insights that feed into downstream decisions. Traditional audit systems, designed to track individual transactions, struggle to maintain complete lineage across these complex data flows.
The Network Security Foundation
Before diving into AI-specific security measures, pharmaceutical companies need to establish the network infrastructure that makes secure AI possible. This isn't about adding more firewalls - it's about creating a network architecture that provides visibility, control, and compliance capabilities at every level.
Modern AI workloads operate across hybrid environments, moving data between on-premises systems, private clouds, and specialised AI platforms. Each data movement creates potential vulnerabilities, and each system boundary requires careful security controls. Traditional network architectures, with their perimeter-focused security models, struggle to provide the granular control and visibility that AI workloads demand.
The answer lies in software-defined networking approaches that treat security as a fundamental network service, not an add-on feature. When network connectivity, security controls, and monitoring capabilities are integrated into a single platform, pharmaceutical companies gain the real-time visibility they need to maintain compliance while supporting AI innovation.
This foundation becomes particularly important when dealing with protected health information and proprietary research data. AI systems often require access to sensitive datasets for training and validation, but traditional network security tools provide limited visibility into how that data is actually used. Advanced network platforms can track data flows at the packet level, providing the detailed audit trails that regulators expect.
Building AI Systems with Audit Trails by Design
The key to regulatory compliance in AI systems isn't retrofitting audit capabilities - it's building them into the system architecture from the beginning. This "audit by design" approach treats compliance as a fundamental system requirement, not an afterthought.
Start with data governance. Every piece of data entering an AI system should be tagged with metadata that tracks its source, processing history, and intended use. This metadata travels with the data through every transformation, creating an unbroken chain of custody that regulators can follow from raw input to final output.
Model development workflows need similar rigor. Every training run should be automatically logged with complete environment specifications - data versions, code commits, hyperparameters, hardware configurations, and performance metrics. These logs shouldn't be stored as separate files that can be lost or corrupted; they should be integrated into the AI development platform as immutable records.
Automated testing and validation frameworks become crucial for maintaining compliance at scale. Manual testing processes that work for traditional software become bottlenecks when dealing with AI systems that may retrain daily or even hourly. Automated frameworks can validate model performance, check for data drift, and verify compliance with predetermined criteria, all while maintaining complete audit trails of the validation process.
The network infrastructure supporting these systems must provide similar auditability. Every data movement, every model inference, and every system interaction should be logged and monitored in real-time. This level of visibility requires network platforms designed specifically for compliance-critical workloads, not general-purpose networking solutions adapted for regulatory environments.
Securing AI Workloads Across Hybrid Environments
Modern pharmaceutical AI systems rarely operate in single environments. A typical drug discovery pipeline might use on-premises systems for sensitive clinical data, private cloud resources for computationally intensive training, and public cloud services for collaborative research. Each environment transition creates security and compliance challenges that must be carefully managed.
The key is creating consistent security policies that follow workloads regardless of where they execute. This requires network platforms that can enforce the same security controls across data centres, private clouds, and public cloud environments, providing unified visibility and policy management across the entire hybrid infrastructure.
Zero-trust security models become particularly relevant for AI workloads. Traditional network security assumes that systems within the network perimeter are trustworthy, but AI systems often access multiple data sources and interact with numerous external services. Zero-trust approaches verify every access request and monitor every data movement, providing the granular control that regulatory compliance demands.
API security deserves special attention in AI environments. Machine learning systems rely heavily on APIs for data access, model serving, and system integration. Each API endpoint becomes a potential attack vector, and each API call must be authenticated, authorised, and logged. Advanced network security platforms can provide comprehensive API protection while maintaining the performance levels that AI workloads require.
Managing AI Model Lifecycle for Compliance
AI models aren't static software applications - they evolve continuously as new data becomes available and requirements change. This dynamic nature creates unique challenges for regulatory compliance, requiring new approaches to change management and system validation.
Model versioning systems must track not just the model itself, but the complete environment in which it was developed and deployed. This includes data versions, preprocessing pipelines, training configurations, and deployment specifications. Each model version becomes a compliance artefact that must be preserved and auditable throughout the system's lifecycle.
Continuous monitoring takes on special importance in AI systems. Models can degrade over time as input data changes, and these changes must be detected and documented for regulatory purposes. Traditional application monitoring tools, focused on system performance metrics, miss the statistical and business performance measures that matter for AI compliance.
Change control processes need updating for AI systems. Traditional change control, designed for infrequent software updates, becomes unwieldy when applied to AI systems that may update continuously. New approaches must balance the need for oversight with the reality of machine learning operations, providing appropriate controls without stifling innovation.
The network infrastructure supporting these dynamic systems must provide consistent security and monitoring capabilities regardless of how frequently models change. This requires platforms that can automatically adapt security policies as systems evolve, maintaining compliance without manual intervention.
Real-Time Monitoring and Threat Detection
AI systems generate vast amounts of operational data - training metrics, inference logs, data quality measurements, and performance statistics. This data represents both a compliance asset and a security challenge. Properly analysed, it provides the insights needed to maintain regulatory compliance and detect security threats. Poorly managed, it becomes a liability that exposes sensitive information and creates new attack vectors.
Advanced monitoring platforms designed for AI workloads can analyse this operational data in real-time, detecting anomalies that might indicate security threats or compliance violations. Unlike traditional security tools that focus on network traffic and system logs, AI-specific monitoring platforms understand the unique patterns of machine learning workloads.
These platforms can detect subtle signs of data poisoning attacks, where malicious actors attempt to corrupt training data to influence model behaviour. They can identify unusual data access patterns that might indicate unauthorised research activities. And they can spot model performance degradation that could signal system compromise or data quality issues.
The key is integrating these AI-specific monitoring capabilities with comprehensive network visibility. When network monitoring platforms understand AI workloads, they can provide context that makes security analysis more effective. A sudden increase in data movement might be concerning in isolation, but when correlated with scheduled model training activities, it represents normal operations.
Preparing for Regulatory Inspections
Regulatory inspections in AI-enabled pharmaceutical companies require a different approach from traditional software audits. Inspectors need to understand not just what the system does, but how it learns, how it makes decisions, and how those decisions can be validated and reproduced.
Documentation systems must be designed for inspector access from the beginning. Traditional documentation approaches, with their focus on static system descriptions, don't adequately capture the dynamic nature of AI systems. New documentation frameworks must provide real-time access to system state, model performance, and operational history.
Demonstration capabilities become crucial during inspections. Inspectors increasingly expect to see systems in operation, not just review documentation. This requires AI platforms with robust demonstration and simulation capabilities that can show exactly how systems process data and generate outputs.
Data export and analysis tools must be inspector-ready. Regulatory officials may request specific datasets, audit trails, or performance analyses during inspections. Systems should be designed to generate these materials quickly and completely, without requiring extensive manual preparation.
The network infrastructure supporting these capabilities must provide inspector-friendly interfaces while maintaining security controls. This means designing systems that can provide appropriate access to regulatory officials without compromising overall system security or exposing sensitive research data.
The Path Forward: Practical Implementation
Implementing audit-by-design AI systems requires careful planning and phased deployment. Start with pilot projects that can demonstrate compliance capabilities without disrupting critical research activities. These pilots should focus on specific use cases - perhaps target identification or candidate screening - where regulatory requirements are well-understood and success criteria are clear.
Network infrastructure upgrades often represent the most significant initial investment, but they provide the foundation for all subsequent AI security initiatives. Choose platforms that provide comprehensive visibility and control across hybrid environments, with specific capabilities for compliance-critical workloads.
Staff training and change management deserve equal attention to technology deployment. AI compliance requires new skills and processes that may not exist in traditional pharmaceutical IT organisations. Invest in training programs that help staff understand both the technical and regulatory aspects of AI system management.
Vendor partnerships become particularly important for AI security initiatives. Choose technology partners who understand pharmaceutical regulatory requirements and can provide ongoing support for compliance activities. This includes not just software vendors, but also network infrastructure providers who can ensure that the underlying connectivity supports regulatory requirements.
Building Tomorrow's Compliant AI Infrastructure
The pharmaceutical industry's AI transformation is inevitable, but its success depends on building systems that satisfy both innovation and regulatory requirements. This isn't about choosing between speed and compliance - it's about designing systems that deliver both.
The companies that succeed will be those that treat compliance as a system design requirement, not a constraint. They'll build AI platforms with audit trails baked into every component, network infrastructures that provide comprehensive visibility and control, and operational processes that make regulatory compliance a natural outcome of good system design.
Cloud Gateway's Network-as-a-Service platform provides the secure, observable foundation that pharmaceutical AI systems require. With comprehensive connectivity across hybrid environments, real-time monitoring capabilities, and built-in compliance features, it enables pharmaceutical companies to pursue AI innovation without compromising regulatory requirements.
The future of pharmaceutical AI isn't about choosing between innovation and compliance - it's about building systems sophisticated enough to deliver both. Companies that start building this foundation today will be best positioned to capitalise on AI's transformative potential while maintaining the regulatory standards that protect patients and ensure public trust.
In an industry where patient safety depends on rigorous oversight, audit-by-design AI systems aren't just a regulatory requirement - they're a competitive advantage that enables sustainable innovation in drug development.
Get in touch with Cloud Gateway to discuss your options.
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