Why do chemical, pharmaceutical and process industry companies need a tailored AI security & compliance strategy?

GxP compliance for AI starts with a clear definition of the regulatory scope: which parts of the system affect the integrity, safety or efficacy of a product? Based on this you define validation boundaries, acceptance criteria and test cases. A complete validation plan includes data provenance, model training, test protocols, release criteria and change control processes.

Technically, this means storing training and test data versioned and traceable, documenting all model epochs and hyperparameters, and ensuring reproducibility. Additionally, implement audit trails that comprehensively log every change and every access to sensitive data and models.

Operationally, define roles for data stewards, model owners and quality assurance. These roles are responsible for maintaining SOPs, conducting periodic reviews and producing the documentation auditors require. Automated compliance checks and report generators significantly reduce manual effort during audits.

Finally, robust testing procedures are crucial: performance tests, robustness tests, security and privacy tests, and regular retraining criteria. Red-teaming complements these measures by simulating real attack vectors and thus testing the system’s resilience against manipulation or data errors.

For sensitive lab and process data we recommend a hybrid architecture with secure self-hosting for critical workloads. This allows full control over data sovereignty, encryption and network segmentation. At the same time, less sensitive components can run in certified cloud environments provided they are ISO- or TISAX-compliant and offer clear SLAs.

Key building blocks are data separation, encryption at-rest and in-transit, HSM support for key management and role-based access controls. Lineage mechanisms must document at every step how raw data were transformed into training data and model outputs.

Connections to existing systems (LIMS, MES, SCADA) should be implemented via dedicated integration layers that can perform transformations and anonymizations. This protects the production environment while providing models with the necessary abstracted information.

Additionally, logging and monitoring stacks are required to provide audit trails for training runs, inference requests and system configurations. These logs are central evidence in the validation and audit process.

Self-hosting is mandatory when sovereignty over sensitive formulations, patient-related data or proprietary process parameters must not be outsourced to third-party infrastructures. In such cases self-hosting ensures full control over access, network and storage — essential for GxP and export control requirements.

Cloud solutions can be used for less sensitive analytics, model development in early phases or for scalable inference services — provided the cloud provider meets relevant certifications (ISO 27001, SOC2) and you implement additional controls like private VPCs, KMS and strict IAM policies.

Hybrid architectures combine the advantages of both approaches: training data and sensitive code remain on-premises, while cost- or compute-intensive steps run in certified cloud environments. The decisive factor is always a clear data classification and a defined data flow that is auditable at all times.

Our recommendation: start with a clearly bounded PoC, evaluate security requirements and then decide on the architecture based on concrete risks and regulatory constraints.

Privacy Impact Assessments (PIAs) should be an integral part of every project phase — not a downstream documentation step. Already in the use-case definition we analyze data types, identify personal data and assess risks to data subjects. This provides the basis for technical measures such as pseudonymization, minimization and purpose limitation.

In the design and implementation phase we implement the measures defined in the PIA into the data pipelines: automatic anonymization, access controls and retention policies. Each measure is linked to metrics that are regularly reviewed, e.g. residual risk after pseudonymization or number of accesses to raw unprocessed data.

PIAs are cyclical: they must be updated when data flows, models or application areas change. These updates are in turn part of change-control documentation that auditors will inspect. Automated logging and reporting greatly simplify this process.

Finally, PIAs connect privacy with compliance: they are a tool to justify technical decisions, organizational measures and communication plans to regulators and affected parties.

Red-teaming goes beyond classic penetration tests: it simulates targeted attacks on data, models and interfaces, including prompt injection, data poisoning and adversarial inputs. For industrial AI systems this also includes scenarios like manipulated sensor values, forged lab samples or deliberate falsification of process parameters.

An effective red-team examines both technical and organizational measures: How does monitoring react to drift? Are alerts escalated correctly? What processes exist to withdraw a contaminated model and roll back to clean baseline data?

Methodologically we work with automated attack pipelines as well as manual, domain-specific scenarios that require knowledge of process chemistry, measurement principles and laboratory procedures. The results are translated into concrete measures: additional checks, conservative output controls or stricter access rules.

Importantly, repetition is key: red-teaming is not a one-off test but a continuous part of risk management, especially after model updates or architectural changes.

The duration strongly depends on the use case and the validation scope. A technical PoC that evaluates feasibility and fundamental risks can be completed in days to a few weeks. The path to production — including integration, validation, audit documentation and training — typically takes between three and nine months.

Factors that influence the duration are data availability and quality, complexity of integrations (LIMS, MES, SCADA), regulatory requirements (GxP, Annex 11) and organizational decision-making processes. Complex clinical or highly regulated use cases may require additional time for external certifications or regulatory reviews.

A pragmatic approach is modular rollout: start with a small, well-defined use case, validate and document it thoroughly, and then scale iteratively. This reduces risk and delivers measurable benefits faster.

Our co‑preneur methodology accelerates this path because we take operational responsibility while transferring internal knowledge so your teams can become autonomous faster.

Important KPIs include both technical and governance-oriented metrics: model performance (accuracy, precision/recall), robustness (adversarial resilience), data drift & concept drift rates, and auditability metrics such as coverage of audit trails and completeness of data lineage.

Security metrics include number and severity of security incidents, time-to-detect and time-to-remediate, as well as counts of successful and failed access attempts. Governance metrics measure compliance checks: proportion of validated models, PIAs completed on schedule, and number of open compliance findings.

For operations, business KPIs are relevant: reduction in manual documentation time, reduction of production downtime through preventive maintenance, and faster release cycles for SOP changes. These KPIs link technical measures to economic value.

Finally, we recommend presenting KPIs in dashboards tailored to different audiences: operators, IT/security, compliance officers and management each need different views and levels of granularity.