How do machine and plant manufacturers in Stuttgart make their AI systems secure and compliant?
Innovators at these companies trust us
The local challenge
Machine and plant manufacturers in Stuttgart stand at the intersection of digitalization and regulatory responsibility: networked controllers, sensitive product data and tight supply chains increase risk. Without targeted AI Security & Compliance measures, data loss, audit deficiencies and liability issues are a real threat.
Why we have the local expertise
Stuttgart is our headquarters — we are part of the regional ecosystem and understand the specific requirements of engineering‑driven companies in Baden‑Württemberg. We regularly work on site, facilitate security workshops in production halls and support decision‑makers from executive management to IT leadership.
Our teams combine security and compliance expertise with practical engineering experience: we speak the language of PLC engineers, PLM managers and quality managers and translate regulatory requirements into actionable architectural decisions.
Our references
In projects with STIHL we analyzed product and production data over several years, defined security requirements and supported solutions for audit readiness — from saw training to ProTools. This work demonstrates how deeply we can penetrate production processes without disrupting operations.
With Eberspächer we implemented AI‑based analysis methods for noise reduction in manufacturing processes and took compliance requirements along the data pipeline into account. For BOSCH we supported go‑to‑market questions for new display technologies, always with a focus on secure data handling and IP protection.
About Reruption
We are not traditional consultants; we act as co‑preneurs: we get in, build prototypes and take responsibility for the outcome — in the customer's P&L, not in a slide deck. That enables fast, technically feasible solutions instead of mere recommendations.
Our modules for Secure Self‑Hosting & Data Separation, Model Access Controls & Audit Logging and Compliance Automation are specifically tailored to the needs of machine and plant engineering and can be quickly validated on site in Stuttgart.
Are your AI systems audit‑ready for TISAX and ISO 27001?
Let us assess together which measures are missing and how a lean PoC in Stuttgart can demonstrate your audit readiness.
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 machine and plant engineering in Stuttgart: a deep dive
The machine and plant engineering sector in Stuttgart faces a double challenge: digitalisation opens up new business models — from AI‑driven service offerings and predictive maintenance to planning agents — while networked systems increase the attack surface and regulatory complexity. A serious security and compliance strategy is therefore not a nice‑to‑have, but a prerequisite for competitiveness and market access.
Market analysis and regulatory context
Baden‑Württemberg is the number one industrial location for mechanical engineering: tightly interwoven value networks, numerous OEMs and suppliers and a strong export orientation shape the region. This means: security gaps or compliance deficiencies can quickly propagate along the supply chain and endanger international contracts. TISAX requirements in the automotive environment, ISO‑27001 audits and data protection requirements under the GDPR are therefore central checkpoints.
Moreover, collaboration with partners such as OEMs or medical technology companies requires transparent, auditable data processing. Companies in Stuttgart must not only provide technical security but also be able to demonstrate it in formal outputs and processes.
Specific use cases in machine and plant engineering
Typical AI use cases in mechanical engineering — and their security implications — include: AI‑based service (predictive maintenance), digital manuals & documentation systems (enterprise knowledge systems), spare parts forecasting and planning agents. Each of these applications brings its own risks: models trained on internal design data can expose intellectual property; service agents require strict access controls to prevent tampering.
An enterprise knowledge system, for example, must support fine‑grained data classification and retention rules so that confidential design data is not inadvertently fed into generative models. Likewise, planning agents require robust audit logs and versioning to make decisions in the production schedule traceable.
Technical architecture and implementation approaches
We recommend a multilayered architecture: separation of training and production environments, self‑hosting options for sensitive data, encrypted data storage and granular model access controls. Secure Self‑Hosting & Data Separation reduces compliance risks, while Model Access Controls & Audit Logging ensure traceable usage and protection against tampering.
Privacy impact assessments should take place early in the project. Based on these, we choose between on‑premise, private cloud or hybrid approaches, create data flow diagrams and implement data governance policies (classification, retention, lineage). For many manufacturers, the option to pseudonymize training data and host models locally is decisive.
Operationalization: processes, tools and automation
Compliance is a process, not a sprint. We rely on automation: compliance templates for ISO/NIST, automatic evidence collection, test suites for privacy and security checks, and CI/CD pipelines that enforce security checks. This makes audit readiness reproducible and maintainable.
Our modules also include Evaluation & Red‑Teaming of AI Systems and Safe Prompting & Output Controls to secure generative systems against prompt‑injection, data leakage and harmful outputs. Red‑teaming processes simulate attacks and uncover vulnerabilities before they become a problem.
Success factors and organizational requirements
Technology alone is not enough: successful AI security requires clear responsibilities, a governance council that makes compliance decisions, and training for developers and business units. In Stuttgart companies, where an engineering culture dominates, it's important to integrate security measures into existing development cycles rather than adding them on the side.
We recommend introducing an AI risk framework that assesses risks, prioritizes countermeasures and establishes metrics to measure maturity and residual risk. This framework connects technical measures with insurance, legal and operational requirements.
Common pitfalls
A common trap is underestimating data provenance: models augmented with third‑party or open data can introduce hidden bias or licensing issues. Projects also often underestimate the long‑term operational costs for security updates, patch management and audit evidence retention.
Another frequent mistake is checking compliance only at the end of development. We regularly see that retroactive adjustments are costly and time‑consuming — especially when architectures have already been deployed. That is why "compliance by design" is indispensable.
ROI considerations and timeline
The business case for AI security includes direct cost savings (avoiding fines, production outages and IP losses) and indirect gains (faster time‑to‑market, trust advantages with OEMs). A typical project to secure an AI use case can be validated as a PoC in 6–12 weeks, combined with a clear production plan.
Our AI PoC offering (9.900€) is a quick entry point: it provides technical validation including security checks, performance metrics and an actionable production plan that considers TISAX and ISO‑27001 relevance.
Technology stack and integration issues
From a technical perspective we combine proven components: encrypted object stores, role‑based access control (RBAC) for models, audit‑logging pipelines and containerized model serving infrastructures. In many cases we recommend using official security features of platforms, supplemented by our own controls for critical data.
Integration into ERP, PLM and MES requires careful API design, mapping of data classifications and a transition strategy for legacy systems. Our experience in Stuttgart shows: close coordination with OT teams is essential to avoid downtimes.
Change management and long‑term governance
Introducing secure AI systems changes responsibilities: data stewards, model owners and compliance officers must be defined. We support training and develop playbooks for incident response, model drift monitoring and regular re‑certification.
In the long term, a continuous audit cycle, automated evidence pipelines and an AI governance board that reviews technology, legal and operations monthly are advisable. Only then can an AI ecosystem remain sustainably secure in an industrial environment.
Ready for an AI security PoC in Stuttgart?
Book an AI PoC (9.900€) — we validate technical feasibility, security requirements and deliver a clear production plan on site.
Key industries in Stuttgart
Stuttgart has been a hub of engineering and industrial manufacturing for centuries. The region has evolved from traditional mechanical engineering to a modern, networked production location where software and AI increasingly complement production logic. This transformation creates opportunities but also new attack surfaces that require targeted security strategies.
The automotive cluster around Stuttgart — with large OEMs and a dense network of suppliers — drives demand for secure AI solutions. Predictive maintenance, digital twins and automated quality controls are central topics; at the same time OEMs demand strict evidence of data security and compliance along the supply chain.
Mechanical engineering itself brings a particular combination of complex controls, proprietary design data and long product lifecycles. This makes data sovereignty and robust access controls indispensable: models must neither reveal design secrets nor leak uncontrolled into cloud services.
Medical technology and industrial automation complete the picture: both industries work with highly sensitive data and are subject to strict regulatory requirements. For manufacturers that supply these sectors, TISAX‑like proof, ISO certifications and documented data governance are mandatory.
The Stuttgart economy is also strongly characterised by SMEs that act as suppliers in global networks. These companies need pragmatic, scalable security solutions that take budget constraints into account while still enabling audit readiness.
Research and academic institutions in the region drive innovation and serve as a talent pool. Collaborations between industry and research are a way to test AI security best practices without risking entire production environments.
Finally, the export orientation of many companies is a driver for compliance: security shortcomings can prevent market opportunities. Therefore: a traceable, technical and procedural securing of AI systems is a competitive advantage, not an additional burden.
In summary, Stuttgart is developing an ecosystem in which security, compliance and technical excellence must be interwoven — and this is precisely where our services come in to establish sustainable and auditable AI solutions.
Are your AI systems audit‑ready for TISAX and ISO 27001?
Let us assess together which measures are missing and how a lean PoC in Stuttgart can demonstrate your audit readiness.
Important players in Stuttgart
Mercedes‑Benz is not only a global automaker but also a central driver of digital transformation in the region. With projects for HR automation or digital services, Mercedes sets high expectations for data security and audit readiness — requirements that suppliers must also meet.
Porsche embodies top engineering and premium production. The brand advances data‑driven product features and connected services, with the protection of customer data and IP being an absolute priority. For suppliers, this means strict compliance and demonstrable security processes.
BOSCH operates as a technology and innovation engine in the region. BOSCH projects connect hardware and software to a high degree and require robust concepts for secure data storage, especially when production data and new display technologies are to be scaled.
Trumpf stands for precision machines and industrial laser technology. The combination of high‑end machines, proprietary processes and international customers makes cyber resilience and protection of design data a strategic topic.
STIHL is an example of a company that combines production, product digitization and training. Projects like saw training or ProTools show how digital offerings are integrated into manufacturing environments — and how important it is to make these offerings secure and compliance‑conformant.
Kärcher connects consumer and industrial customers with global service offerings. For such business models, secure customer data pipelines, clear retention policies and transparent audit logs are essential to build trust in digital services.
Festo is a cornerstone of industrial automation and education. Digital learning platforms and automated training systems require data protection‑compliant designs — especially when participant data and production data are linked.
Karl Storz, as a medical technology player, shows how tightly regulated industries structure AI adoption: traceability, data governance and validation are not optional here. Suppliers and partners must provide corresponding security and compliance evidence.
Ready for an AI security PoC in Stuttgart?
Book an AI PoC (9.900€) — we validate technical feasibility, security requirements and deliver a clear production plan on site.
Frequently Asked Questions
TISAX and ISO 27001 pursue similar goals — information security — but differ in focus and expectations. ISO 27001 is a generic management system for establishing, implementing and continuously improving information security. TISAX, on the other hand, is specifically tailored to the automotive industry and takes into account relevant requirements for confidential production data, testing and release processes along the supply chain.
In the AI context this means: ISO 27001 creates the management and process basis (risk management, ISMS, roles), while TISAX often specifies technical and procedural requirements that are particularly relevant for collaboration with OEMs, such as data segmentation, access controls and traceability of data flows.
Practically, we advise manufacturers in Stuttgart to combine both perspectives: an ISO‑27001‑compliant ISMS as a foundation and TISAX‑specific measures for working with automotive customers. This facilitates supplier contracts and reduces audit effort during OEM audits.
Concrete tip: start with a DPIA for your AI use cases and map the results against ISO‑27001 controls and TISAX requirements. This way you identify gaps early and prioritize measures that address both management requirements and industry‑specific audits.
The decision between local hosting (on‑premise) and cloud depends on data classification, legal requirements and business needs. For particularly sensitive design data or IP‑critical models, many manufacturers in Stuttgart tend toward self‑hosting to retain full control over data and minimize compliance risks.
Cloud providers now offer extensive security features, encryption and compliance certificates. In many cases, a private cloud or hybrid architectural approach is a practicable solution: training workloads can be processed in secured cloud environments, while production models that handle sensitive inference data run locally.
What matters is the implementation: regardless of platform, access controls, audit logging and data separation must be strictly enforced. Contract clauses on data sovereignty, subprocessors and incident response should also be clearly defined.
Our practical advice: run a proof of concept that includes security tests, privacy checks and a cost transparency comparison. In Stuttgart we support such PoCs on site to find the right balance between control, scalability and economic efficiency.
Data protection starts with data minimization: collect only the data necessary for the use case and anonymize or pseudonymize wherever possible. Spare parts forecasting often requires operational and machine data — here it's important to separate or anonymize personal data (e.g. employee identifiers) in advance.
For enterprise knowledge systems the challenge is to identify and protect confidential documents and personal information. Robust classification rules, data lineage and retention policies are central so that sensitive data does not flow into generative models or produce unintended outputs.
The GDPR also requires documentation: records of processing activities, legal bases and DPIAs for new technology deployments. For AI systems a DPIA is almost always advisable, especially when automated decisions or profiling play a role.
Implementation practices: deploy automated detection mechanisms for personal data, enforce strict role‑based access models and document data flows. These measures simplify audits and reduce legal risks.
The timeframe depends on use case complexity, data quality and internal maturity. A technical PoC to test feasibility can be realized in days to weeks — our AI PoC offering is precisely aimed at that: rapid validation, proof of value and a realistic roadmap.
For production readiness including security hardening, governance processes, audit evidence and integration into PLM/ERP you typically plan for 3–9 months. In highly regulated scenarios or large integration projects the cycle can be longer.
Key drivers of duration are: availability and preparation of training data, necessary infrastructure adjustments, coordination with OT teams and the length of internal decision‑making processes. Tight project management and clear responsibilities significantly shorten the time.
Our recommendation: start with a lean PoC, validate security assumptions early and plan compliance work (DPIA, ISMS mapping) in parallel. This yields early, reliable insights for scaling.
Red‑teaming simulates attacks and tests how AI systems behave under real threats. This is particularly important for industrial AI applications because a successful attack can impact production processes, jeopardize intellectual property or even cause safety risks.
An initial red team exercise should take place before production deployment to identify fundamental vulnerabilities. Afterwards we recommend regular tests — depending on the risk profile at least semi‑annually, for critical systems quarterly or after major changes to the model or data flow.
Red‑teaming should cover technical attacks (e.g. prompt‑injection, data poisoning) as well as organizational weaknesses (e.g. misconfigurations, inadequate access rights). Findings must be translated into concrete measures and tracked.
In practice we combine red‑teaming with automated security scans and continuous monitoring so that newly discovered risks are addressed quickly and evidence can be provided to auditors at any time.
Compliance automation aims to standardize recurring audit tasks and automatically collect evidence. Integrated into CI/CD pipelines, checks for data classification, model versioning, security scans and license checks can be executed before each release.
Start with risk prioritization: which controls are critical for audit readiness? Implement these prioritized controls first as automated tests. This creates quick wins and builds trust within the team.
Technically we use policy‑as‑code approaches that embed compliance rules into versioning systems, as well as automated evidence collectors that gather configuration and log data. These artifacts can be directly converted into audit reports.
Organizationally you need clear ownership: who is responsible for the compliance gate in the release process? Close alignment of DevOps, security and compliance teams is essential to avoid friction and maintain release velocity.
Contact Us!
Contact Directly
Philipp M. W. Hoffmann
Founder & Partner
Address
Reruption GmbH
Falkertstraße 2
70176 Stuttgart
Contact
Phone