Why is AI Security & Compliance crucial for the manufacturing of metal and plastic components in Dortmund?
Innovators at these companies trust us
On‑site challenge
Manufacturing plants in Dortmund are caught between ageing machine parks that need modernization and the pressure to accelerate digital processes. AI projects designed to bring visual quality control, predictive maintenance or procurement copilots often collide with unclear data ownership, fragmented systems and strict compliance requirements.
Why we have local expertise
Reruption is headquartered in Stuttgart and travels to Dortmund regularly to work directly with production teams, IT departments and security officers on site. We don't claim to have a Dortmund office — we come to you, embed ourselves temporarily in the operation and deliver solutions that work in the reality of your manufacturing environment.
Our work begins with a precise understanding of your facilities: which controllers communicate with IT, how are network segments and remote accesses structured, which suppliers have access to production data? We clarify these questions on site because security solutions for metal and plastic manufacturing are only robust if we have practical access to the shop floor, MES and ERP.
Technically, we bring a team of security experts, cloud and on‑prem engineers and compliance specialists who work closely with your operations engineers. Our co‑preneur approach means: we take responsibility for outcomes, not just for recommendations — we build prototypes, validate security mechanisms and hand over audit‑ready artifacts.
Our references
In the manufacturing sector we have worked repeatedly with established industrial companies. For STIHL we supported several projects over two years — from saw training to ProTools to product simulations — gaining deep experience with production data, simulations and training systems where data security and user rights are critical.
With Eberspächer we developed AI‑driven solutions for noise reduction in manufacturing processes: sensor data integration, model validation and measures to protect sensitive production data were central — a blueprint for similar requirements in Dortmund's metal and plastic manufacturing.
About Reruption
Reruption was founded to not only advise companies but to build real products with entrepreneurial responsibility. We combine strategic clarity with technical depth, move quickly and deliver prototypes and production plans instead of long reports.
Our focus rests on four pillars: AI Strategy, AI Engineering, Security & Compliance and Enablement. For Dortmund manufacturers this means: concrete measures for TISAX/ISO compliance, secure self‑hosting architectures, audit readiness and automatable compliance templates that can be applied directly in production.
Do you have a concrete AI question for your production in Dortmund?
We travel to Dortmund regularly, analyze your situation on site and deliver a technical PoC that validates feasibility, security and compliance. Talk to us about your use case.
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 manufacturing in Dortmund: a comprehensive guide
The use of AI in manufacturing fundamentally changes production processes: from visual quality control to predictive maintenance to procurement copilots that automatically evaluate supplier offers. These potentials also introduce new attack surfaces and compliance questions. In Dortmund, a region that has shaped the structural shift from steel to software, traditional industrial IT and modern AI infrastructures meet closely — this requires a tiered security and compliance concept.
Market analysis: Dortmund's manufacturing sector is embedded in an ecosystem of logistics, energy and IT. Production sites often work with suppliers and logistics partners in real time, which means data flows don't stop at factory boundaries. Security requirements like TISAX are therefore not just IT audits but enterprise security measures that must include supply chains and partners.
Specific use cases
Quality Control Insights: AI‑based image processing can reduce scrap, but it requires image data management, anonymization, and strict access rules to prevent disclosure of sensitive design details or supplier information. Models must be versioned and their training data traceable.
Predictive Maintenance: Sensor data from machines provides indicators of impending failures. A secure architecture logically separates OT and IT while enabling secure data aggregation for ML models. Here, data lineage, retention policies and encrypted transport channels are central elements.
Procurement Copilots: Models that support ordering and supplier evaluation access pricing data, contract clauses and personal data. Privacy‑by‑design, access and role management and audit logging are essential here to minimize legal risks.
Implementation approach — practical modules
Secure Self‑Hosting & Data Separation: In many manufacturing companies self‑hosting is required because sensitive IP and internal process data must not leave the company to public clouds. We design isolated environments, container baselines, network segmentation and Hardware Security Modules (HSM) for key management.
Model Access Controls & Audit Logging: Access to models is governed by fine‑grained policies. Audit logs capture model accesses, datasets and output versions so that in the event of an incident it is clear which decision template was used and how a model was trained.
Privacy Impact Assessments & Safe Prompting: For every AI integration we conduct Privacy Impact Assessments, define sensitivity classes and implement safe prompting rules that prevent confidential information from being sent to external models or from generating sensitive outputs.
AI Risk & Safety Frameworks: Each project receives a risk profile: data risk, error predictability, operational impact on workplace safety and regulatory risks. Based on this we implement monitoring, failover strategies and human control points.
Compliance Automation (ISO/NIST Templates) & Audit‑Readiness: We provide prebuilt compliance templates for ISO 27001, TISAX and NIST, including policies, processes and evidence templates that auditors expect. Automated checks and reporting significantly reduce manual effort.
Data Governance (Classification, Retention, Lineage): Effective governance starts with classification: which data is worth protecting? How long may training data be retained? We implement lineage tools that document every data movement — indispensable for audits and for clean model reconstruction.
Evaluation & Red‑Teaming: Before going into production, models undergo systematic tests and red‑teaming: attack scenarios, adversarial tests, performance‑drift checks and failure tests. Only in this way can blind spots be uncovered before models influence production decisions.
Success factors and common pitfalls
Cultural acceptance: Security must not be seen as a brake. Successful projects link security with measurable business criteria: less scrap, fewer downtimes, faster throughput. Early involvement of operations managers and shop floor personnel is crucial.
Data quality and integration: Many factories underestimate the effort required for data cleansing, labeling and connecting PLC data with MES/ERP. Without clean data there are no reliable models — and therefore no compliance‑safe decisions.
Scaling: Prototypes often fail when scaling because security solutions were not considered from the start. Architectural decisions for self‑hosting, cost models and backup strategies must be part of the technical design from day one.
ROI considerations, timeline and team
ROI is often visible in reduced downtime, less rework and optimized procurement conditions. A typical PoC to prove technical feasibility can be realized with us in days to weeks (see AI PoC Offering). The transition to production requires an additional 3–9 months, depending on complexity and compliance effort.
Required roles: Data Engineers, Security Architects, Compliance Officers, DevOps/Platform Engineers and shop floor specialists. Our co‑preneur approach allows us to temporarily fill these roles until an internal team becomes operational.
Technology stack and integration issues
Typical technologies: containerized model deployments (Kubernetes/on‑prem), feature stores, data catalogs for lineage, SIEM for audit logs, HSMs for key protection, and API gateways with RBAC. Integration with PLCs and MES requires edge gateways that aggregate data securely and in compliance.
Change management: Security and compliance processes should be embedded in training plans and SOPs. We support with workshops, playbooks and regular compliance checks so that security requirements become part of daily operations.
Practical examples and next steps
A typical project starts with a scope workshop on site in Dortmund, followed by a technical PoC (€9,900 AI PoC Offering) in which feasibility, performance and compliance risks are tested. We then plan architecture, security measures and a migration path, including audit checklists for ISO/TISAX.
Conclusion: For Dortmund manufacturers, AI security & compliance is not an additional task but the enabler for sustainable digitization. Those who treat security and governance requirements as an integral part of product development gain not only efficiency but also legal certainty and trust in the market.
Ready for the next step toward audit‑capable AI?
Start with our AI PoC (€9,900) and receive a working prototype, performance metrics and an implementation plan including a compliance roadmap.
Key industries in Dortmund
Dortmund has completed the structural shift from a steel hub to a modern industrial and technology location. Historically rooted metal and mechanical engineering competencies remain, but the value chain is shifting: classic production meets software and data expertise. This combination offers enormous potential for the manufacturing sector that uses AI for quality optimization and process automation.
The logistics sector is strongly represented in Dortmund and forms a natural partner for manufacturers: short supply chains, real‑time transport data and reliable replenishment systems are essential for just‑in‑time production. AI models that optimize inventory and transport flows benefit from proximity to logistics networks and at the same time increase requirements for data integrity and access control.
The IT sector and specialized service providers in the region drive innovation. For manufacturers this means access to developers, data specialists and system integrators who can operationalize machine learning solutions. This creates an environment in which security solutions for AI must be considered integrally from the start.
Insurers and financial service providers in the region, for example in industrial insurance, are important stakeholders: they demand transparency, traceability and risk assessments. This increases pressure on manufacturers to present audit‑capable AI solutions and to document compliance evidence comprehensively.
The energy sector with companies like RWE influences production planning: volatile energy prices and grid integration make flexible, AI‑driven production controls attractive. Such systems require robust security concepts because interventions in production planning can have direct financial and safety‑relevant consequences.
In plastic and component manufacturing there are additional requirements for material safety, traceability and regulatory documentation. AI can detect material defects early and trace batches, but these systems must be designed so that data classification, retention and access control protect production IP.
The regional proximity of research institutions and universities provides access to fresh talent and current research results. For companies this means opportunity and obligation: they can adopt AI faster but must also ensure compliance standards in innovation projects so that results are scalable and audit‑capable.
In summary: Dortmund offers a heterogeneous, innovation‑driven environment. For manufacturers this means thinking of security and compliance solutions not as a brake, but as an enabler for growth, collaboration and market access.
Do you have a concrete AI question for your production in Dortmund?
We travel to Dortmund regularly, analyze your situation on site and deliver a technical PoC that validates feasibility, security and compliance. Talk to us about your use case.
Key players in Dortmund
Signal Iduna is one of the region's major insurers. Its proximity to industry creates a natural discourse on risk management, cyber insurance and traceability requirements. Manufacturers benefit because insurers increasingly demand audit evidence for AI‑driven processes, for example regarding liability issues in quality assurance.
Wilo, originally a pump manufacturer, has evolved into a provider of mechatronic systems and digital services. For companies like Wilo the combination of hardware supply chains and digital services is typical: security architectures must protect production data as well as customer data and telemetry.
ThyssenKrupp as an industrial and technology group forms an interface between traditional heavy industry and modern production processes. Even though ThyssenKrupp operates nationally and internationally, its presence influences the regional supply‑chain ecosystem and expectations for compliance standards across the manufacturing chain.
RWE as an energy company shapes the discussion around flexible production and load management. Energy‑related data is relevant for manufacturers when production lines are managed according to energy prices and availability. Such scenarios require secure integration points between energy data, production planning and AI decisions.
Materna is an IT service provider with a strong focus on system integration and software solutions. Proximity to integrators like this facilitates the implementation of data governance projects and secure deployments because integration know‑how and compliance processes can be efficiently combined.
Besides these large players there is a lively scene of SMEs and hidden champions that supply parts, components and specialized machines. These suppliers shape requirements for data exchange, interface security and supply chain transparency — all topics a KI compliance strategy must address.
Research institutes and technical universities provide skilled workers and innovation impulses; industry collaborations are common. This networking creates short innovation cycles but also the need to consider security concepts in early development phases so prototypes are audit‑capable later.
Overall, Dortmund's landscape consists of strong industrial customers, integrative IT service providers and utilities — an environment in which AI security & compliance is not optional but a prerequisite for successful digitization.
Ready for the next step toward audit‑capable AI?
Start with our AI PoC (€9,900) and receive a working prototype, performance metrics and an implementation plan including a compliance roadmap.
Frequently Asked Questions
The choice between TISAX and ISO 27001 depends on your supply chain, your customers and the protection needs of your information. ISO 27001 is a generic information security management system and provides a broad, internationally recognized framework. TISAX is specific to the automotive industry and its suppliers and places particular emphasis on protecting prototype data, access controls and physical security.
For many Dortmund manufacturers who supply components to automotive OEMs or larger industrial partners, TISAX is relevant because customers often request this evidence. ISO 27001, on the other hand, offers a solid foundation that can be transferred to other industries and often serves as preparation for industry‑specific audits.
Practically, a pragmatic approach is recommended: start with a gap analysis that assesses your current maturity against both standards. Based on this, measures can be prioritized so investments in policies, technical controls and evidence documentation become audit‑ready immediately.
It is important that compliance measures are not only documented but lived. This includes regular training, automated checks and technical controls that we implement together with your team to ensure traceability for auditors.
The choice between self‑hosting and cloud depends on several factors: data protection requirements, the need to protect IP, latency requirements and operational capacities. In manufacturing with sensitive design data and tight supply‑chain links, self‑hosting is often the preferred option because it enables full data sovereignty and strict network segmentation.
Cloud offerings, however, provide scalability, managed services and quick deployment. They are attractive for non‑critical workloads or when providers already demonstrate robust compliance certifications. Nevertheless, it must be carefully checked whether data ends up in third‑party regions or whether models interact with external training data — that can create legal risks.
A hybrid architecture combines the advantages of both approaches: sensitive training data and core models remain on‑prem, while non‑critical inference loads or training experiments run in certified cloud environments. Clear data flows, encryption and strict access controls are essential.
Our recommendation: start with a small self‑hosted PoC (e.g. for quality control), validate performance and security requirements and then define clear criteria for cloud usage. We support architecture, implementation and audit preparation.
Compliance‑ready production data requires systematic data governance: classification, lineage, retention policies and controlled access rights. Start with classification: assign data to categories (public, internal, confidential, strictly confidential) and define which categories may be used for ML training.
Lineage tools document where a dataset originated, which transformations have been applied and in which models it was used. This traceability is crucial for audits and for reproducibility of model decisions — in cases of product liability or failure analysis this can make the difference.
Retention policies define how long raw data, training data and model artifacts are retained. Legal requirements or customer contracts can define minimum retention periods or deletion obligations. Automated processes for data deletion and archiving relieve operations and reduce risk.
Technically, we help you introduce a data catalog, implement access control via RBAC, and implement encryption and anonymization. At the same time we train teams so governance is not a one‑time task but part of everyday work.
The duration varies greatly depending on the use case, the state of the data and compliance requirements. A technical PoC that demonstrates the feasibility of a use case can be realized in days to a few weeks. This PoC typically tests model quality, latency and simple security mechanisms.
Production readiness requires additional steps: robust data pipelines, security architecture, access controls, audit logging and possibly certifications such as ISO 27001 or TISAX. For this phase companies should expect three to nine months — complex integrations or high compliance demands can take longer.
It is important to plan security and compliance measures in parallel during the prototype phase. If security measures are only introduced at the end, delays and extra costs arise. Our approach is therefore to develop the PoC and the security baseline simultaneously.
A pragmatic path is a phased production rollout: pilot in one plant, monitoring and strict security checks, then rollout to additional sites. This creates experience and allows the security concept to be iteratively improved.
A secure AI stack combines infrastructure and application components: isolated deployment environments (e.g. on‑prem Kubernetes), encrypted data pipelines, feature stores with access control, SIEM systems for audit logging and HSMs for key management. Edge gateways secure the bridge between OT (PLC, SCADA) and IT.
Also important are tools for data lineage and governance that document which data flowed into a model. For the models themselves, versioning systems and MLOps pipelines are necessary to make training and inference runs reproducible.
Security measures also include runtime controls: output filtering, safe prompting rules and monitoring that detect outliers or performance drift. In safety‑critical contexts human oversight is indispensable; automated decisions should have rollbacks and escalation paths.
We choose technologies pragmatically according to your environment: in many Dortmund plants we rely on hybrid architectures that combine self‑hosting components with certified cloud services, complemented by integration modes for existing MES/ERP systems.
Audit preparation begins with a gap analysis: which policies, evidence and technical controls are missing? Based on this you create an audit backlog with concrete measures. Important evidence includes data catalogs, access control lists, model versioning, logging configurations and risk assessments.
Use prebuilt templates and checklists for ISO 27001 and TISAX — these significantly reduce documentation effort. We provide compliance templates that are specifically adapted to AI workloads and deliver the artifacts auditors expect.
Technically, audit logs from all relevant systems should be collected centrally and stored tamper‑proof. The ability to reconstruct model training and inference runs retrospectively is often a decisive factor in audits, especially when decisions have production‑technical consequences.
Conduct internal pre‑audits to close gaps and train relevant stakeholders in dealing with auditors. Transparency and traceability build trust — both with auditors and with partners and customers.
Contact Us!
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Philipp M. W. Hoffmann
Founder & Partner
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Reruption GmbH
Falkertstraße 2
70176 Stuttgart
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