How do chemical, pharmaceutical and process plants in Essen reliably and legally secure AI systems?
Innovators at these companies trust us
The challenge in Essen
In Essen, the energy hub in the heart of the Ruhrgebiet, complex process chains meet strict regulatory requirements. Chemical, pharmaceutical and process companies face the task of deploying AI quickly without incurring security, privacy or compliance risks. Data sovereignty, auditability and operational safety are not side issues here – they determine approvals, liability and market trust.
Why we have local expertise
Reruption is headquartered in Stuttgart and regularly travels to Essen to work with customers on site. We understand the dynamics of North Rhine-Westphalia: energy providers, chemical parks and processing industry operate closely together here. This regional embedding enables us to practically translate regulatory expectations and local process requirements into AI security concepts.
Our teams combine technical depth with operational understanding: we build prototypes, carry out risk assessments and implement secure hosting and data solutions directly into the production environment. We think from process control to documentation – so Safety Copilots or knowledge systems are not only intelligent but also secure and auditable.
Our references
For companies with demanding manufacturing and process requirements we bring concrete project experience: at Eberspächer we worked on AI-supported noise reduction solutions in production, a project that linked operational sensor data and strict quality controls and approaches the risk profile of the process industry. Such work demonstrates how sensors, data pipelines and security requirements can be integrated.
In the field of environmental chemistry, our project with TDK on PFAS removal is technologically and regulatorily relevant: it combines chemical expertise with the need to document data and processes traceably. We have also worked with STIHL on training and production solutions that secured process documentation and compliance via digital platforms. Additionally, projects such as with FMG support the development of AI-supported research and documentation solutions that are crucial for audit readiness and regulatory evidence.
About Reruption
Reruption was founded with the mission to not only advise companies but to build real products and capabilities with them as a co-preneur. Our approach combines strategic clarity with engineering tempo: we deliver secure, tested prototypes and a clear plan for production rollout – not just PowerPoint roadmaps. This is particularly important for regulated industries where traceability and responsibility are essential.
We operate as an integrated part of the organization: fast iterations, entrepreneurial responsibility and technical depth. In Essen we work closely with compliance, IT and operations teams to implement solutions that meet TISAX, ISO-27001 and data protection requirements while not slowing down operations.
Do you need an audit-readiness assessment for your AI systems?
We check TISAX, ISO-27001 and GDPR compliance of your AI architecture and deliver a concrete action plan with priorities and effort estimates. We regularly travel to Essen to conduct on-site audits.
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 Essen: A detailed guide
Introducing AI in chemical, pharmaceutical and process plants is not purely a technology project – it is an organizational and regulatory transformation. In Essen, where power plants, chemical parks and processing facilities are tightly interconnected, innovation pressure meets strict safety requirements. Companies must therefore design security architectures, data flows and compliance mechanisms together from the outset, not as an after-the-fact fix.
In this deep dive we explain concrete use cases, technical approaches, implementation paths and common pitfalls. Our focus is on practicality: we show how TISAX, ISO 27001, data protection and audit readiness can be implemented in real projects – from prototype to productive operation.
Market analysis and regulatory context
The chemical and pharmaceutical industries are subject to European regulations such as the GDPR, GMP requirements and industry-specific standards. In addition, the process industry is shaped by environmental regulations and local safety standards. In Essen, energy policy conditions and tight supply chains add another layer: risks from failures in energy supply or material flows have direct impacts on production and safety models.
For AI this means: models must not only be performant but also reproducible, explainable and tamper-resistant. Audit logs, model version control, data provenance (lineage) and strict access controls are not optional. Companies in Essen should therefore consider audit readiness as an early KPI.
Specific use cases for the process industry
Typical but safety-critical use cases include: laboratory process documentation with automatic plausibility checks, Safety Copilots to support shift leadership, intelligent knowledge search across process manuals and secure internal models for quality prediction. All these use cases work with sensitive process and measurement data and require strict separation of corporate data and external models.
A Safety Copilot must be able to know operational regulations, understand dependencies and at the same time be robust against faulty inputs. Therefore measures such as safe prompting, output controls and red teaming are central components of implementation. Integration into SCADA/DCS systems additionally requires low-latency, secure interfaces and coordinated change management processes.
Implementation approach: architecture and data strategy
Our modular approach includes secure self-hosting & data separation as a foundation: critical models and data remain within the company’s infrastructure or in a trusted, certified data center. Where cloud services are used, we define strict data classification, encryption-at-rest and encryption-in-transit as well as explicit access controls.
Other essentials are model access controls & audit logging to ensure traceability of every prediction, data governance with classification, retention and lineage, and privacy impact assessments to address GDPR risks. Compliance automation (ISO/NIST templates) helps structure evidence for auditors and standardize recurring processes.
Security testing, red teaming and evaluation
Before production rollout we conduct systematic evaluations and red teaming to identify attack surfaces: from prompt injection through data poisoning to model exfiltration. Such tests simulate real threats and reveal whether output controls and monitoring are sufficient.
Regular performance and robustness assessments are required to detect drift and retrain models. For regulated environments we document test plans, result logs and change histories – central requirements for audit readiness and regulatory inspections.
Success factors and common pitfalls
Success factors include clear ownership structures (security, data, ML-Ops), close collaboration with operations and security teams and early involvement of the compliance department. Speed matters, but not at the expense of traceability: prototypes must be built with audit logs and security mechanisms, otherwise costly retrofits follow.
Typical pitfalls are unclear data ownership, missing documentation of data provenance, overly permissive model access and ignoring change management in OT environments. Especially in Essen, with its critical infrastructure, an unsecured AI workflow can have far-reaching consequences.
ROI, timeline and team composition
Expected timelines range from days for proofs of concept to months for production rollouts. Our AI PoC offering (9.900€) delivers a technical proof and a reliable roadmap within a few days. A realistic production path typically spans 3–9 months, depending on data quality, integration effort and regulatory requirements.
The interdisciplinary team should include data engineers, ML engineers, security architects, compliance experts and process engineers. We recommend a co-preneur structure: Reruption works embedded in the customer P&L, together with an internal champion who brings decision authority and process knowledge.
Technology stack and integration issues
Practical stacks combine secure on-prem or VPC hosting, container orchestration (Kubernetes), ML-Ops tooling for version control (MLFlow, DVC), and observability tools for audit logging and monitoring. For privacy-preserving methods we use anonymization, pseudonymization and, where necessary, federated learning approaches.
Integration challenges mainly concern interfaces to process control systems, latency requirements and certification issues in safety-critical environments. Close dialogue with OT teams is mandatory: the security strategy must bridge OT and IT silos.
Change management and culture
Technology alone is not enough. Success depends on training, clear operating processes and governance. For the process industry in Essen this means: training for operators, clear emergency procedures in case of system malfunctions and defined escalation paths. Transparent communication builds trust with operations and quality teams.
We support this transformation through enablement workshops, hands-on training and implementation of SOPs that make AI-supported processes audit-ready. This creates solutions that are not only technically robust but also organizationally anchored.
Ready for a quick AI security PoC?
Our AI PoC (9.900€) delivers a working prototype, security assessment and an actionable production roadmap in a short time. Contact us for an on-site evaluation in Essen.
Key industries in Essen
Essen has historically established itself as the heart of Germany’s energy and industrial economy. From the region emerged a density of energy providers, suppliers and processing industries that shape the regional economy. The transition to green tech and sustainable processes creates new opportunities while compliance with strict safety and environmental regulations remains central.
The energy sector is dominant in Essen: grid stability, supply security and integration of renewable sources are daily topics. For AI this means models must not only provide predictions but act reliably and transparently in critical situations. Faulty model decisions would have direct impacts on production and safety.
The chemical industry around Essen works with complex material flows and risk-prone production processes. Here, laboratory process documentation and traceability of changes are essential. AI can increase efficiency and quality, but without validation and audit readiness companies risk regulatory problems and liability cases.
The pharmaceutical and process industries additionally demand particular discipline in data management and compliance: GMP, product liability and patient-safety-relevant requirements make detailed documentation and strict quality control indispensable. AI solutions must therefore be explainable and reproducible, including clear data provenance and test protocols.
Construction and retail sectors in the region also affect supply chains and logistics. Digitization and AI help to synchronize processes – from supply chain forecasting to quality checks. In Essen these industries often work closely together, which requires common standards and interoperable data solutions.
The shift to a green-tech metropolis opens additional opportunities: energy efficiency, emissions monitoring and process optimization are fields where secure AI can deliver high returns. At the same time environmental regulations and stakeholder expectations sharpen requirements for compliance and transparency.
For companies in Essen this means: those who want to use AI must deliver both innovation and compliance at the same time. Technical solutions without governance are risky; governance without technology remains ineffective. Connecting both dimensions is the key to sustainable success.
Reruption supports this balance with a pragmatic, product-oriented approach: we build secure prototypes, define governance standards and hand over actionable roadmaps that address both regulatory and operational requirements.
Do you need an audit-readiness assessment for your AI systems?
We check TISAX, ISO-27001 and GDPR compliance of your AI architecture and deliver a concrete action plan with priorities and effort estimates. We regularly travel to Essen to conduct on-site audits.
Key players in Essen
E.ON is one of the central energy providers with extensive activities in grid infrastructure and energy supply. E.ON’s digital transformation focuses on grid stability, load forecasting and energy efficiency; AI plays an increasing role. Security and compliance requirements are particularly high here because failures mean immediate supply risks.
RWE, another major player, drives renewables, energy storage and flexibility solutions. RWE faces the challenge of translating volatile generation data into reliable operational decisions. For AI solutions, robust data security and traceability mechanisms are mandatory to meet regulatory requirements and market demands.
thyssenkrupp represents the metal-processing industry with highly automated production processes. In the context of AI security, access control to machine and sensor data and protection against manipulation are central topics so that automation and optimization functions can be safely integrated into operations.
Evonik stands for specialty chemical products and demanding production processes. Combining chemical expertise with digital methods opens potential but also risks regarding intellectual property and process safety. Data governance and strict access concepts are essential here.
Hochtief has its roots in construction and through infrastructure projects also influences logistical flows in the region. AI applications in planning, material flow optimization and safety must comply with construction and environmental regulations, which requires close alignment of technology and regulation.
Aldi, as a major retail player, uses data-driven processes for logistics and store operations. For retail in the region, data protection, secure supply chain information and compliant AI systems for employee and customer data are crucial so that automations do not create compliance risks.
These players shape the economic environment in Essen: their requirements for security, availability and compliance set standards for all suppliers and technology providers. For AI solutions this means working technically solid and audit-capable – from the data basis to operational documentation.
Reruption brings experience in translating these requirements into productive AI architectures: we work on site with teams to build solutions that meet the strict demands of the regional industry while unlocking innovation potential.
Ready for a quick AI security PoC?
Our AI PoC (9.900€) delivers a working prototype, security assessment and an actionable production roadmap in a short time. Contact us for an on-site evaluation in Essen.
Frequently Asked Questions
A proof-of-concept (PoC) for AI security & compliance is designed to deliver technical feasibility and initial security assessments in a short time. At Reruption a typical PoC starts with a clear use-case definition and a feasibility analysis. Within a few days to two weeks we can provide a functional prototype that includes initial security checks, data governance groundwork and a testing setup.
It is important that data access, legal frameworks (e.g. data sovereignty, third-party licenses) and involvement of OT/IT teams are clarified in advance. Delays often arise not from technical issues but from organizational matters – for example when interfaces to process control systems or approvals from compliance departments are still pending.
Our AI PoC offering for 9.900€ provides a clear timeframe: use-case definition, rapid prototyping, performance evaluation and an actionable production roadmap. For companies in Essen this means they receive a reliable result within a short time, on the basis of which efforts for certifications, hosting or integration projects can be precisely planned.
Practical takeaways: appoint an internal contact with decision authority, clarify data access rights early and reserve time with OT and compliance teams. With this preparation, a meaningful PoC in a few weeks is realistic.
Multiple layers of standards are relevant in the process industry. On the IT security side, ISO 27001, NIST frameworks and industry-specific requirements like TISAX (especially for suppliers) are central. For product and process safety, GMP, ISO quality standards and specific environmental regulations apply. AI-specific guidelines, for example on explainability and robustness, are increasingly required by regulators and industry associations.
For companies in Essen this means compliance must rest on multiple shoulders. IT and security teams handle encryption, access control and incident response; compliance and quality assurance teams must ensure documented test protocols, validations and change management processes. AI-specific documentation includes model cards, data sheets and audit logs.
Practically, we combine standards in modular templates: compliance automation (ISO/NIST templates) reduces effort and ensures required evidence is structured. This is essential for audits and regulatory inspections common in highly regulated environments like chemical parks or pharmaceutical plants.
Recommendation: start with a gap assessment against ISO 27001 and TISAX requirements, build in data governance elements (classification, retention, lineage) and document validation processes for models. This gives you a solid base for further certifications.
Data protection in laboratory environments touches on sensitive personal and quasi-personal data (e.g. employee data, logs). A privacy impact assessment (PIA) is the first necessary step: it identifies risks, determines legal bases and recommends technical and organizational measures. In many cases pseudonymization and strict data minimization help reduce GDPR risks.
Technical measures such as encryption-at-rest, encryption-in-transit, role-based access controls and audit logging are central. For machine learning workflows, separating identity data from measurement data and using secure self-hosting options is recommended when cloud services are legally or contractually problematic.
Operationally it is important to define responsibilities clearly: who is the data controller, who is the data processor, what are the retention periods? Documentation is decisive – especially for audits or access requests. In many cases it is more efficient to integrate privacy and compliance into the development cycle (privacy by design) rather than retrofitting later.
Practical tip: develop standard processes for consents, data access requests and incident response. Combine technical measures with training for laboratory staff so that data protection is not just a technical matter but part of everyday routine.
Secure internal models require an architecture that ensures isolation, traceability and controllability. Secure self-hosting is often the preferred choice for sensitive production data: models run in a controlled environment, data does not leave the protected zone and access is strictly regulated. Alternatively, verifiable VPC setups in certified clouds can offer a balance between security and scalability.
Key components are: a dedicated model repository with versioning (model registry), ML-Ops pipelines with reproducible training runs, audit logging for inference calls and an access control layer for users and services. Data lineage tools document which datasets were used for which model – important for audits and root-cause analyses in case of anomalies.
We also recommend security mechanisms such as rate limiting, input validation, output filtering and safe prompting for interactive systems. For OT integrations, low-latency gateways with strict firewall rules and protocol filters are necessary so models can provide decisions without compromising process control.
Conclusion: architecture must be modular, traceable and operationally secure. Invest in versioning, logging and strict access controls – these elements are often decisive for approval by compliance departments.
Security testing for AI systems should go beyond classic penetration tests. Red teaming for AI simulates attacks such as data poisoning, prompt injection, model inversion or adversarial manipulation of sensor inputs. The goal is to identify vulnerabilities early and validate countermeasures.
A structured testing program includes threat modeling, targeted attack attempts in isolated test environments and evaluation of detection mechanisms. Data poisoning tests assess how robust models are to manipulated training data; prompt injection tests focus particularly on dialogue-oriented systems for manipulable outputs.
Key countermeasures are input sanitization, training-on-clean-data, outlier detection and monitoring for behavioral changes (drift detection). Additionally, you should implement mechanisms for rollback and rapid isolation of modules if an attack is detected.
Practical advice: conduct regular red teaming exercises, document results and integrate learnings into your CI/CD pipelines. Only then will defenses become an integrated part of operations.
ROI for AI projects in the process industry is multidimensional: it includes direct efficiency gains (better yield, lower scrap rates), risk reduction (avoided downtime, compliance costs) and strategic value (accelerated innovation, market leadership). In Essen, energy savings, optimized maintenance cycles and improved laboratory processes can deliver significant savings.
Security and compliance are not just cost factors but prerequisites for monetization: without audit readiness or with insecure architecture many savings potentials cannot be realized or carry high follow-up costs. Therefore costs for security, governance and compliance measures should be included in the ROI calculation but also seen as levers for market access and risk avoidance.
We recommend a staged approach: start with a PoC, measure concrete KPIs (error reduction, time savings, inspection costs) and project these metrics into scaled operation. Also account for qualitative effects such as improved auditability and stakeholder trust, which bring long-term financial benefits.
Practical tip: document baselines before project start, define clear KPIs and plan regular reviews. This makes ROI measurable and identifiable security investments justify themselves through reduced operating costs and lower liability risk.
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Philipp M. W. Hoffmann
Founder & Partner
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Reruption GmbH
Falkertstraße 2
70176 Stuttgart
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