How do chemical, pharmaceutical and process plants in Dortmund make AI-powered systems resilient and compliant with regulations?
Innovators at these companies trust us
Security risks nobody should ignore
In the chemical and pharmaceutical industries, faulty models or uncontrolled data flows are not just a compliance issue — they endanger production, product safety and patient safety. Without clear data governance, audit logs and access controls, risks can quickly arise that lead to costly production outages or regulatory sanctions.
Why we have the local expertise
Reruption is based in Stuttgart and regularly travels to Dortmund to work on-site with production and R&D teams on secure AI solutions. We understand the structural transformation in North Rhine-Westphalia: the transition "from steel to software" and the tight interconnection of logistics, energy and manufacturing in the region. This perspective allows us to link technical security requirements with operational processes in Dortmund.
Our work follows the Co‑Preneur approach: we act like co-founders, take responsibility for implementation and operation, and deliver not just rules on paper but runnable, tested systems. Concretely, this means secure self-hosting options, strict data isolation and audit-readiness tailored to local production workflows.
Our references
For clients in manufacturing sectors we have repeatedly implemented secure, industry-driven solutions. At STIHL we supported projects from customer research to market readiness over two years — a project that gave us deep insights into production processes and the security of training data. At Eberspächer we worked on AI-supported noise reduction in manufacturing and developed robust data pipelines and evaluation procedures needed in sensitive plant environments.
Our technology and spin-off experience with clients like BOSCH and TDK demonstrates how technical prototypes become viable, compliant products. For consulting and analysis projects with heavily regulated datasets, FMG engaged our expertise to ensure secure document research and governance.
About Reruption
Reruption was founded to enable companies to proactively prepare for disruption — not through mere consulting, but by genuinely co-enterprising. Our focus rests on four pillars: AI Strategy, AI Engineering, Security & Compliance, and Enablement. We combine strategic clarity with engineering depth and deliver prototypes that move into production.
For Dortmund-based companies this means: we bring the methods and artifacts needed for AI projects to be operated in line with TISAX, ISO 27001 and data protection — including technical measures such as model access controls, audit logging, privacy impact assessments and secure hosting architectures.
Would you like us to assess the security posture of your AI projects in Dortmund?
We come to you, scan your architecture for risks and deliver an implementation plan with priorities for compliance and secure production within a few weeks.
What our Clients say
Hans Dohrmann
CEO at internetstores GmbH 2018-2021
This is the most systematic and transparent go-to-market strategy I have ever seen regarding corporate startups.
Kai Blisch
Director Venture Development at STIHL, 2018-2022
Extremely valuable is Reruption's strong focus on users, their needs, and the critical questioning of requirements. ... and last but not least, the collaboration is a great pleasure.
Marco Pfeiffer
Head of Business Center Digital & Smart Products at Festool, 2022-
Reruption systematically evaluated a new business model with us: we were particularly impressed by the ability to present even complex issues in a comprehensible way.
AI Security & Compliance for chemical, pharmaceutical and process industries in Dortmund
Introducing AI in regulated industries requires more than good algorithms: it demands a complete security and compliance ecosystem. In Dortmund, where production sites, logistics centers and research departments are tightly interlinked, requirements for data sovereignty, traceability and availability are particularly high. This section walks systematically through market analysis, use cases, implementation approaches and the factors that determine success or standstill.
Market analysis and regulatory context
The chemical and pharmaceutical industries in NRW face double pressure: stricter regulatory requirements on one hand, increasing automation and data-driven processes on the other. While authorities and auditors are increasingly emphasizing traceability and auditability, production managers demand safety and resilience. For Dortmund-specific players this means security architectures must be aligned with both local IT/OT boundaries and corporate-wide compliance policies.
There is also the influence of adjacent sectors like energy and logistics: supply chain disruptions or energy shortages immediately affect chemical production processes, which is why resilient, secure AI applications should be prioritized there. Market analyses show that companies investing early in governance benefit sooner from efficiency gains and quality improvements.
Specific use cases for the industry
In practice we see three particularly relevant use cases: laboratory process documentation, safety copilots for operating personnel, and secure knowledge search systems. Laboratory process documentation needs strict data classification, lineage and retention policies so results are reproducible and auditable. Safety copilots require real-time availability guarantees, deterministic output controls and strict access controls to avoid faulty recommendations.
Knowledge search systems using internal models must implement data governance and privacy-by-design so that sensitive formulations, production parameters or studies cannot be extracted without authorization. Measures such as role-based access, query filtering, output sanitization and secure self-hosting options help keep data sovereignty within the organization.
Implementation approach: from PoC to production
A pragmatic roadmap begins with a technically focused PoC: validating model behavior, performance measurements and initial security checks. Our AI PoC offering (€9,900) delivers a working prototype within a few days that shows whether a use case is realistic — including a risk analysis and a rough production plan.
The transition to production then requires several layers: secure infrastructure (e.g., on‑prem or private cloud with data separation), model access controls & audit logging, continuous evaluation pipelines and red‑teaming to uncover vulnerabilities. Compliance automations (ISO/NIST templates) are also important to establish audit-readiness and provide repeatable evidence.
Technical architecture and secure hosting strategies
Secure self-hosting strategies are often the first choice in regulated environments because they enable data sovereignty and auditability. Architecture principles should define clear zones between OT, production networks and AI workloads, implement strict data classification and enforce encrypted data storage. Containerized models in isolated environments plus hardware-based root-of-trust protect both models and data.
For many Dortmund companies a hybrid architecture makes sense: sensitive training data stays on‑prem, while less critical models run in a private cloud. Crucial, however, are audit logs that document model accesses, inputs and outputs without gaps — this is also a core requirement in TISAX/ISO‑27001 audits.
Security and risk management
An AI Risk & Safety Framework captures threats, assesses risks and defines control mechanisms. Best practices include threat modeling for AI assets, privacy impact assessments for data‑intensive projects and regular red‑teaming to reveal adversarial weaknesses. Governance boards with representatives from compliance, IT/OT and business units ensure risks are not considered in isolation.
Documentation is also essential: decision processes, test protocols and performance metrics must be versioned and auditable. Only then can you pass external audits and quickly trace technical causes in the event of an incident.
Change management and team building
People remain the critical factor. Security concepts must be easy to use and responsibilities clearly assigned. We recommend cross-functional teams with data engineers, security architects, compliance officers and domain representatives. Training on safe prompting, handling internal models and incident response processes is essential so employees can use AI safely.
For companies in Dortmund we also recommend regular readiness exercises with production and IT teams and clear runbooks for incidents involving AI components — e.g., incorrect action recommendations from a safety copilot during a critical process phase.
Common pitfalls and how to avoid them
Common traps include unclear data ownership, missing lineage, weak access controls and insufficient evaluation cycles. Small organizational measures — clear classification rules, mandatory privacy impact assessments and automated compliance checks — prevent many problems early on.
Technically, you prevent risks through strong isolation of development and production environments, mandatory audit-logging pipelines and controlled model rollouts with canary phases. Red‑teaming and regular penetration tests round out the security concept.
ROI, timelines and scaling
Investments in AI security pay off directly: lower downtime risk, faster audit cycles and reduced liability exposure. Typical projects start with a 4–8 week PoC, followed by a 3–9 month production phase including security hardening. Scaling happens stepwise: from isolated use cases (e.g., lab documentation) to company-wide, regulated models.
In the long term a robust security foundation accelerates the adoption of further AI use cases because compliance hurdles are already addressed technically and organizationally. This is particularly valuable for Dortmund companies given the complex supply chains and energy dependencies present there.
Technology stack and integration aspects
Recommended building blocks include: encrypted databases with lineage support, MLOps pipelines with integrated audit logs, identity and access management for models, and sandboxing for model tests. Tools for compliance automation simplify ISO/TISAX evidence, and privacy engineering methods (differential privacy, pseudonymization) protect sensitive research data.
Integration with existing MES/ERP systems must be planned carefully so production OT is not impacted. We design integrations so data flows remain traceable and rollback mechanisms exist for unexpected model behavior.
Practical recommendations for Dortmund companies
Start with a well-defined, risk-assessed use case and a short technical PoC. Implement basic controls in parallel: data classification, access restrictions, audit logging and a privacy impact assessment. Build a small core team with security know-how and use external experts for red‑teaming and compliance checks.
Reruption accompanies this path with a Co‑Preneur approach: we deliver prototypes, security architectures and the organizational artifacts you need for audits and production — and we work on-site in Dortmund to integrate solutions directly into your processes.
Ready for a technical PoC with audit‑readiness?
Start with our AI PoC: a technical prototype, risk analysis and a rough production plan — ideal for assessing regulatory feasibility and security requirements.
Key industries in Dortmund
Dortmund was long a symbol of the steel industry, but it has systematically driven structural change: today the city is a central hub for logistics, IT, energy and insurance. These sectors form the basis of a diverse industrial ecosystem that also strengthens chemical, pharmaceutical and process-intensive manufacturing. Proximity to universities, suppliers and logistics axes makes Dortmund particularly attractive for data-intensive production models.
The logistics sector benefits from Dortmund's position as a transport hub: flows of goods connect production sites with markets across Europe. For chemical and pharmaceutical companies this means short supply chain routes — but also higher requirements for traceability and compliance along the supply chain.
The IT sector has grown in importance in Dortmund; numerous medium-sized software firms and service providers offer solutions for production planning, MES and data integration. This local IT expertise is an advantage: software and AI projects can be implemented quickly and closely linked to production processes when the right governance standards are in place.
Insurers and financial service providers in the region also drive demand for risk-oriented IT solutions. For chemical and pharma companies this means insurers increasingly require technical evidence and robust security concepts before granting certain coverages — a clear driver for investments in AI security.
The energy sector around RWE and other players influences the operational stability of production plants. Energy efficiency and resilient operations are therefore topics closely intertwined with AI projects: predictive maintenance or load management are typical use cases that demand strict security and availability.
New niches are also emerging in Dortmund: from specialized contract manufacturers to startups developing industrial software. This diversification fosters collaborative projects where AI solutions can be rapidly put into practice — provided governance, data protection and auditability are integrated from the start.
For the chemical and pharmaceutical industries this yields concrete opportunities: faster research cycles through secure knowledge platforms, more efficient production via AI-supported process optimization and better compliance through automated documentation and testing processes. At the same time, demands for security and traceability increase — a tension that requires targeted measures.
The conclusion: Dortmund offers the infrastructural and human resources to successfully scale AI projects in regulated industries — provided companies treat governance, technical isolation and auditability as integral parts of their AI strategy.
Would you like us to assess the security posture of your AI projects in Dortmund?
We come to you, scan your architecture for risks and deliver an implementation plan with priorities for compliance and secure production within a few weeks.
Important players in Dortmund
Signal Iduna is a major regional insurer and a significant actor when it comes to enterprise-wide risk assessment and cyber insurance. Their requirements for evidence documentation and risk management influence how manufacturing companies design security concepts, especially regarding liability and compliance for AI-supported decisions.
Wilo has transformed from a pump manufacturer into a global technology provider. With a strong focus on digital solutions for building technology and industrial processes, Wilo pursues data-driven optimization — an environment where secure models and data protection are central requirements when product data or operating parameters are shared.
ThyssenKrupp combines traditional manufacturing expertise with modern engineering services in the region. For suppliers and chemistry-related companies in Dortmund this means high demands on quality and supply chain transparency. AI projects here must be auditable and robust against disruptions.
RWE represents the energy sector, whose availability and pricing directly affect chemical production processes. Collaborations between energy suppliers and industry promote use cases like energy management and predictive maintenance — both areas where secure AI and compliance are central.
Materna brings IT services and consulting know-how to the region. IT providers like Materna are often partners in integrating MLOps pipelines and security solutions; their local presence facilitates implementation of governance standards and alignment between IT and production.
In addition, numerous medium-sized companies and suppliers shape the region: specialized machine builders, software vendors and logistics providers testing AI solutions in joint projects. This heterogeneous landscape is a strength, but it requires standardized interfaces, common security policies and clear responsibilities.
Research institutes and universities in NRW supply talent and research results that are important for advancing secure AI applications. Industry–science collaborations are often the source of innovative approaches, for example in privacy-preserving machine learning or industrial red‑teaming methods.
Overall, Dortmund is an ecosystem where established large companies, innovative mid-sized firms and IT providers collaborate. For chemical, pharmaceutical and process plants this means good opportunities for pilot projects and scaling — if compliance, data security and auditability are considered from the outset.
Ready for a technical PoC with audit‑readiness?
Start with our AI PoC: a technical prototype, risk analysis and a rough production plan — ideal for assessing regulatory feasibility and security requirements.
Frequently Asked Questions
The chemical and pharmaceutical industries are subject to strict national and European regulations that indirectly also affect AI: data protection (GDPR), product safety laws and sector-specific rules for medicines and chemicals. For AI systems there is an added demand for traceability, reproducibility and documentation. Auditors expect the lifecycle of a model, including training data, versioning and test protocols, to be reproducible.
In addition, certifications like ISO 27001 or sector-specific standards are gaining relevance. For manufacturing companies TISAX is often relevant when working with automotive suppliers or other high-security partners. These standards require technical measures for access control, encryption and physical security — aspects that must be addressed particularly carefully in AI deployments.
Practically, it is advisable to perform a compliance gap analysis early that identifies specific requirements for the use case: Are personal data processed? Do model decisions affect safety-critical processes? Answers determine the scope of documentation, the need for a PIA and the technical controls required.
Our recommendation: involve compliance and security stakeholders early in design workshops and create auditable artifacts (data lineage, test protocols, access logs). This avoids costly retrofitting and builds trust with auditors and operational managers.
Sensitive laboratory and production data should be hosted where you have the greatest control: on‑premises or in a dedicated private cloud with clear contracts and technical isolation layers. The architecture must support data classification, at‑rest and in‑transit encryption and access controls. In many cases a hybrid approach is sensible: training data remains on‑prem, while less critical model artifacts can run in a private cloud.
It is also essential to document data flows and prove lineage: who generated the data, how was it transformed, which models were trained on it? Automated lineage tools and data catalogs are helpful building blocks here, especially when multiple departments or external partners are involved.
Operationalize audit logging from the start: log model accesses, inputs and outputs, model versions and deployments. These logs are central for forensic analyses and for ISO or TISAX audits. If needed, they can be connected to SIEM systems to detect anomalies in real time.
Finally, organizational measures should run in parallel: clear roles for data owners and data stewards, defined retention policies and regular review cycles. Reruption supports architecture design, tool selection and implementation of secure hosting concepts on-site in Dortmund.
"Audit-ready" means that all relevant information, decisions and tests of an AI system are traceable, versioned and verifiable. This includes datasets and their origins (lineage), training and test protocols, model versions, access controls and change logs. Auditors expect not only technical artifacts but also organizational evidence: roles, responsibilities and processes for model review.
In the process industry additional safety evidence is important: How does the model influence control decisions? What fallback mechanisms exist? Is human review provided? Audit‑readiness means you can answer these questions with documented tests, risk analyses and incident response plans.
Technically, audit pipelines support traceability: automated tests at every model training, canary rollouts with monitoring, and structured reports for auditors. Storing checkpoints and artifacts is also important so models are reproducible.
Practically, companies should conduct regular internal audits and red‑teaming to close gaps before external reviews. Reruption helps build such audit pipelines and produces the necessary templates and documents for ISO/TISAX audits.
Protecting internal models starts with strict access controls: role‑based access control, least‑privilege principles and strong authentication (MFA) are basic measures. Models and training data should be stored in isolated environments that are network‑segmented from the general corporate network. Additionally, encryption and hardware-backed security modules (HSMs) are recommended to protect key material.
Audit logging and monitoring are crucial: every request to a model, every change to model parameters and every deployment action must be logged. Anomaly detection helps identify atypical access patterns early. For sensitive models it is worthwhile to implement query filtering and output sanitization to prevent exfiltration via targeted queries.
Organizationally, clear data steward roles and regular code and architecture reviews are essential. Penetration tests and red‑teaming should be part of the lifecycle to identify weaknesses before they are exploited. It is also advisable to limit third‑party risks: contractual clauses, technical restrictions and audit rights when dealing with vendors provide additional security.
Reruption implements technical controls, automated checks and operational processes that not only protect models but also ensure they are auditable and resilient against attack attempts.
Data protection and innovation are not mutually exclusive when privacy engineering is embedded early in the development process. Start with data minimization: collect only the data necessary for the use case. Pseudonymization and anonymization techniques reduce risk, while differential privacy or federated learning as technical measures allow training models without central personal data sets.
Privacy Impact Assessments (PIAs) should become standard in your project management: they identify risks, describe protective measures and provide a basis for decisions for data protection officers and executives. This enables targeted, data‑driven innovation without regulatory surprises.
Operational measures such as data retention policies, clearly defined retention periods and automated deletion processes help meet regulatory requirements without unduly impacting model performance. Transparent documentation builds trust with auditors and stakeholders.
Technically, many approaches can be combined: synthetic data generation for training, local model training techniques or simulated test data for early development phases. Reruption advises Dortmund and surrounding companies on pragmatic data protection solutions that enable innovation while ensuring GDPR compliance.
Red‑teaming is a targeted testing procedure that exposes vulnerabilities in AI systems and plays out realistic threat scenarios. In industrial environments red‑teaming examines not only classic IT security gaps but also model-related risks: manipulation, undesired model effects or output exfiltration via targeted queries.
A thoughtful red team evaluates attack surfaces across the entire ML lifecycle: data collection, data preparation, training processes, model serving and monitoring. The results provide concrete action plans — from masking sensitive fields to stronger validation datasets and the introduction of query rate limits.
Regular red‑teaming cycles are especially important when models are used in safety‑critical processes, such as safety copilots or process control. They increase the maturity of protections and improve the company's incident response capabilities.
Reruption conducts red‑teaming projects, produces action plans and works with your team in Dortmund to close identified gaps. The goal is a continuous security process that grows with operations and reliably passes audits.
Contact Us!
Contact Directly
Philipp M. W. Hoffmann
Founder & Partner
Address
Reruption GmbH
Falkertstraße 2
70176 Stuttgart
Contact
Phone