How do manufacturing companies (metal, plastic, components) make their AI deployments secure, compliant and audit-ready?
Innovators at these companies trust us
The reality in manufacturing
Production facilities struggle with heterogeneous data landscapes from MES, PLCs, quality measurement data and supplier data — while pressure increases to use AI for workflow automation and quality control insights. Without a clear security and compliance strategy, data silos, unintended data exports and costly audit deficiencies are likely.
Why we have the industry expertise
Our consultants and engineers combine experience with industrial control environments (CNC, injection molding, press control) and information security. We design solutions that respect OT/IT boundaries while meeting ISO 27001, TISAX and GDPR requirements — pragmatically and without production risk.
When building audit-ready architectures we think in production cycles: data classification along the bill of materials (BOM), secure logistics and supplier workflows, as well as robust retention and deletion processes. Our team includes security and compliance engineers, data engineers and senior ML architects who deliver secure, auditable pipelines together.
We work as co-preneurs: not only recommendations but concrete implementations in your P&L. That means we build secure prototypes, carry out risk assessments and document everything audit-compliantly — from access controls to audit logging.
Our references in this industry
We have worked with STIHL on multiple manufacturing-relevant projects — from saw training and saw simulators to ProTools and ProSolutions. These projects demonstrate our ability to digitalize complex production processes while implementing security requirements in both training and production environments.
For Eberspächer we implemented AI-based approaches to noise reduction in manufacturing processes, including privacy-compliant data collection and analysis pipelines that support quality inspections and production adjustments. Both references demonstrate our understanding of industrial data, sensor technology and the balance between productivity gains and compliance.
About Reruption
Reruption not only builds strategies — we implement secure, production-ready systems. Our modules like Secure Self-Hosting & Data Separation, Model Access Controls & Audit Logging and Data Governance are specifically tailored to industrial requirements.
Our co-preneur philosophy means we take responsibility within your organization, deliver rapid prototypes and transition them into production with clear, auditable roadmaps. We ensure your AI initiatives not only work, but also stand up — before audits, partners and customers.
Ready to make your AI deployments secure and audit-ready?
Contact us for a short scoping call. We review your top risks and propose pragmatic next steps.
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 transformation in manufacturing
The manufacturing of metal, plastic and component products has reached a point where AI is no longer just an innovation project, but an integral part of production control and quality assurance. At the same time, AI systems introduce new attack surfaces, compliance requirements and responsibilities. Therefore, an AI transformation in manufacturing must combine technical excellence with strict governance.
Industry context
In regions like the Stuttgart area, known as an automotive and supplier hub, thousands of mid-sized companies work with sensitive product data, customer applications and tight supply chains. These companies operate production lines with PLC systems, central MES platforms and a variety of quality measurement systems (SPC, 3D measurement). The challenge is to connect AI models to these systems without violating OT security boundaries or exposing confidential design data contrary to compliance rules.
Product developments often contain intellectual property in the form of CAD data, process parameters and testing strategies. Unprotected use of such data in cloud models can pose irreversible risks to IP and competitiveness. Therefore, secure self-hosting and data separation are not only technical options but in many cases economic necessities.
Key use cases
Practical use cases in manufacturing include automated quality inspection using image processing, predictive maintenance for spindles and motors, procurement copilots for parts sourcing and automated production documentation. Each of these use cases has its own security and compliance requirements: image data can contain personal information, BOM and supplier data are confidential, and predictive maintenance models must be protected against model drift and manipulation.
For quality control (e.g., surface inspection of metal parts or injection-molded components) this means: models must be operated locally or processed encrypted, audit logs must record every inference run, and retention policies must handle raw images and models separately. For procurement copilots, access controls on supplier data, SLA-compliant data sharing and traceability of all recommendations are central.
Another important application is production documentation, where speech and text models are used to create inspection reports or maintenance instructions. Here, privacy impact assessments and safe prompting are crucial so that models do not inadvertently disclose sensitive design details or personal data.
Implementation approach
Our implementation begins with a use-case-driven risk analysis: we define inputs/outputs, assess data classification, and determine the minimal data volumes and processing locations. Based on this scope we propose an architecture — local or hybrid — that combines self-hosting and model access controls.
For production environments we recommend a strict separation of OT and IT access: gateways with controlled data flows, read-only views for production data, and secured inference paths. We implement audit logging at transaction and model levels so that every access, model version and decision is traceable.
In parallel, we automate compliance checks: templates for ISO 27001 and NIST, TISAX-readiness reports and standardized documentation for data protection authorities. Our compliance automation includes checks for data classification, retention and lineage — as well as scripts and dashboards for ongoing audit-readiness.
Evaluation, red-teaming and safety
Before production release we conduct comprehensive evaluations: performance tests, robustness checks, privacy impact assessments and red-teaming to uncover attack surfaces such as data inference or prompt injection. The evaluation includes metrics for robustness, cost per run and false-positive rates so that technical decision-makers receive transparent KPI bases.
Red-teaming simulates real attacks on models and data pipelines: manipulation of sensor data, adversarial images for visual inspection or unexpected user inputs for copilots. Resulting measures range from input sanitization and rate limiting to model ensemble defenses.
Operationalization and change management
Technical implementation is only part of it. We support operationalization with clear roles, incident response playbooks and training for operators. Production staff, quality engineers and IT security teams receive specific training on secure development processes, handling model updates and audit preparation.
It is important to establish a governance board with stakeholders from manufacturing, legal, IT and procurement. This board decides on data access, model approvals and escalation processes. This keeps the transformation controllable and trustworthy — also for OEM customers in sensitive supply chains.
ROI, timeline and team requirements
Typical pilot projects (e.g., automated optical inspection or procurement copilot) can be realized as a proof-of-concept within 6–12 weeks; production rollout, including compliance hardening and audit documentation, usually takes 3–6 months. Our AI PoC offering for €9,900 provides technical feasibility, performance metrics and a pragmatic production roadmap.
For successful scaling you need a small core team: a technical product owner, a data/ML engineer, a security/compliance lead and a manufacturing SME (e.g., process engineer). Reruption supplements this team with co-preneur engineering, compliance automation and audit documentation.
Long-term security architecture
In the long term we recommend a modular security architecture: isolated model farms for sensitive workloads, encrypted data sinks, fine-grained RBAC for models and dedicated lineage services. This architecture not only supports current use cases but also makes your organization resilient to regulatory changes and new audit requirements.
With clear processes for model updates, monitoring and regular red-teaming rounds, AI in manufacturing becomes not a black box but a controlled, measurable asset.
Would you like to start a technical proof-of-concept?
Book our AI PoC package for €9,900 and receive a working prototype, performance metrics and an actionable production plan within weeks.
Frequently Asked Questions
The first step is always a realistic inventory of the data landscape: which data sources exist (MES, SPC, CAD, sensor logs), where are they physically located and what sensitive information do they contain? In regions like Stuttgart, with many suppliers and OEM partnerships, the answer to this question is decisive because IP and customer requirements often need to be considered.
In parallel, an initial risk analysis should be performed to identify potential data exports, attack paths and compliance risks. Here we check whether certain data must remain local or whether encrypted, controlled cloud usage is possible.
On an organizational level we recommend establishing a governance board that brings together IT, production, legal and procurement. This board decides on data access, classifications and priorities — and ensures that security requirements do not remain isolated within IT.
Practically, a small, time-boxed proof-of-concept (e.g., image inspection or procurement copilot) is useful to quantify technical feasibility, costs and compliance effort. Our AI PoC offering provides exactly this assessment: a working prototype, performance metrics and an actionable production plan.
Self-hosting is recommended when models work with sensitive CAD, BOM or supplier data, or when legal/contractual requirements exclude cloud processing. For many mid-sized manufacturers, self-hosting is the economically sensible option because it returns control over data and models while reducing ongoing cloud costs.
Cost-effective implementation starts with clear prioritization of workloads: only latency- or data-sensitive models need to run locally; less critical analyses can be handled in a hybrid setup. We recommend containerized deployments on existing virtualized infrastructure or dedicated on-prem servers with GPU acceleration.
Automation and standardization are important: infrastructure-as-code for secure network policies, standardized images for models and template-based processes for access controls. These measures minimize operational effort and significantly reduce the entry costs for self-hosting.
Finally, plan audit-readiness from the start: automated audit logs, versioning of models and metrics, as well as backup and retention strategies. This makes self-hosting not only secure but also auditable.
TISAX and ISO 27001 require not only technical measures but documented processes, roles and responsibilities. In manufacturing it is important to think of these standards along production processes: access to PLCs, data flows from MES into analytics tools and interfaces to suppliers must be considered in risk analyses.
Integration begins with gap analyses: which of the required controls already exist, which processes are missing and where are responsibilities unclear? Based on this we create a roadmap with prioritized measures — for example network segmentation, encryption, access controls and audit logging.
Technically, we implement standardized templates for ISO/NIST-compliant policies, automated audit scripts to monitor configurations and reporting dashboards for auditors. For TISAX-relevant requirements we add supplier assessments and access restrictions to sensitive data.
The process is iterative: after implementation follow internal audits and adjustments before external certifications or assessments take place. We accompany this path with documentation templates, evidence collection and employee training.
Speech and text models carry specific risks: unintended disclosure of sensitive information, manufacturing instructions or IP through prompt leaks, as well as the generation of incorrect or non-auditable instructions. In a production context, faulty recommendations can have direct quality or safety consequences.
To mitigate these risks, safe prompting and output controls are crucial. Prompts and system policies must be designed so that models do not reproduce confidential design details. Additionally, models should only be trained on truncated, anonymized or tokenized data where possible.
We also recommend strict access controls and audit logs for all interactions with copilots. Every recommendation should include a source reference and a confidence value so engineers can trace decisions.
Finally, copilots should be operated in a secure environment and regularly evaluated: automated tests, human review processes and regular red-teaming exercises uncover risks before they enter daily production use.
The ROI of security and compliance investments can be measured across several levers: avoidance of production downtime, reduction of quality defects, fewer contractual penalties due to compliance breaches and faster time-to-market through audit-ready deployments. It is important to make these effects quantifiable.
In practice we assess KPIs before and after implementation such as downtime, scrap rates, mean time to root-cause analysis and audit findings. Example: if a robust quality inspection model reduces scrap by X%, the savings can be directly attributed to the security and governance measures that ensure the model's availability and reliability.
We also factor in risk costs: the probability and potential impact of a data leak or IP loss. Compliance measures significantly reduce this probability and can thus be valued as insurance-reducing investments.
We create concrete business cases with scenarios (best/likely/worst case) so decision-makers can make fact-based investment choices. Often the investment in audit-ready infrastructure pays off sooner than expected — especially in supplier chains with strict OEM requirements.
Red-teaming in production environments must be carefully planned to avoid disruptions. We strictly separate tests into development, staging and production environments. Attacks and manipulation scenarios are first executed in secure test environments that simulate real production data without affecting live processes.
Once a scenario is validated, a staged rollout follows with clear metrics and rollback plans. For production-near tests we use canary deployments or shadow execution where new models or defenses run in parallel but isolated from productive paths.
The organization also needs clear communication and escalation protocols: who is responsible, which KPIs are monitored and which measures are taken immediately when an issue is detected? Such playbooks prevent red-teaming from triggering uncontrolled reactions.
Regularity is important: plan semi-annual red-teaming rounds combined with monthly automated evaluations. This keeps models robust against drift and new attack patterns without endangering ongoing operations.
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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