Why do industrial automation and robotics in Berlin need strong AI Security & Compliance?
Innovators at these companies trust us
The local challenge
In Berlin manufacturing sites, robotics labs and automation startups, innovative hardware meets sensitive production data. Without a consistent AI Security & Compliance strategy, there is a risk of data leaks, faulty models in safety-critical environments and regulatory sanctions.
Why we have the local expertise
Our headquarters are in Stuttgart — we don't claim to have a Berlin office, but we travel to Berlin regularly and work on site with customers in factory halls, research labs and startups. This practice makes us agile: we understand the city's pace, the rapid iterations of product teams and the specific requirements of automation projects.
Through on-site work we learn not only processes but also the physical security requirements for robotic systems and production lines. The proximity to Berlin developer communities, hardware labs and universities enables us to design technical solutions that truly fit the local infrastructure.
Our references
In the manufacturing and mechanical engineering world we have worked with STIHL on a variety of projects — from saw training and ProTools to saw simulators — and supported processes that require high safety and compliance standards. This work has given us a deep understanding of product development and secure automation.
With Eberspächer we worked on AI-driven solutions for noise reduction in production and delivered concrete analyses that address both technical and data-protection questions. Projects like this demonstrate how closely technical performance and compliance are interlinked.
Furthermore, we have collaborated on technology and hardware projects with partners such as BOSCH and AMERIA — from go-to-market consulting to the development of contactless interaction concepts — repeatedly reshaping the interface between industrial engineering and secure AI systems.
About Reruption
Reruption builds AI products and AI-first capabilities directly inside organizations: we operate with a co-preneur mindset, take responsibility like co-founders and combine engineering power, speed and strategic clarity. For companies in Berlin this means: immediate outcomes instead of lengthy reports.
Our focus rests on four pillars — AI Strategy, AI Engineering, Security & Compliance and Enablement — which ensure that AI solutions not only work but are legally and operationally viable. We support pilot projects through to production maturity and are used to working on site with interdisciplinary teams.
Are your robotics projects in Berlin audit-ready?
We review your current security posture, perform a gap assessment and show concrete measures for TISAX/ISO compliance — on site in Berlin or remotely.
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 industrial automation and robotics in Berlin: A deep dive
Berlin is not just the startup capital — the city is an experimental ground where robotics labs, industrial automation projects and young hardware startups exist side by side. This heterogeneous landscape creates enormous innovation potential, but also specific security and compliance requirements: production data must not flow into the wrong network, models must not make safety-critical misdecisions, and traceability is mandatory for audits.
Market analysis & context
Demand for AI in industrial automation is exploding in Berlin: startups develop assistance systems for assembly, established manufacturers optimize production with predictive maintenance, and researchers combine robotics with multimodal sensors. This diversity leads to many data sources — image data, sensor data, logfiles — and therefore a complex security picture. An overarching goal is to design data flows so they are both efficient and auditable.
From a regulatory perspective, companies face requirements such as ISO 27001, industry-specific standards and, in some cases, TISAX compliance. Berlin-specific are also the expectations of investors and partners regarding data sovereignty and traceability: local tech ecosystems demand transparent governance policies as a precondition for collaboration and scaling.
Specific use cases in industrial automation & robotics
Concrete use cases we frequently see include: secure edge models for production robots; engineering copilots that assist maintenance personnel with diagnoses; automated quality inspections via computer vision; and predictive maintenance using sensitive machine data. Each use case brings its own compliance challenges: for example the need to operate models offline or to anonymize personal data in logfiles.
For every use case the architecture must be both secure and robust. This includes secure self-hosting options and data separation, strict model access controls, comprehensive audit logs and an integrated data governance framework that ensures classification, retention and lineage. Without these building blocks, production pilots are practically unauditable.
Implementation approach: From PoC to production
Our modular approach begins with a precise use-case definition: input, output, metrics and boundary conditions. This is followed by a technical feasibility check and a proof-of-concept, often within a few days, to demonstrate that models run reliably in the production environment. In this phase, security measures such as network segmentation, secure container deployments and encrypted data at rest are indispensable.
In the transition to production we conduct Privacy Impact Assessments, implement automated compliance checks and establish continuous evaluation processes including red-teaming. These steps reduce release risks and create audit-readiness — important when suppliers or OEMs require proof of compliance.
Technical building blocks & technology stack
A typical technology stack for secure AI in industrial environments includes: orchestrated edge workloads (Kubernetes/POC-edge setups), encrypted data pipelines, models in hardened runtime environments, identity and access management for models and datasets, as well as comprehensive logging. For certain scenarios Secure Self-Hosting & Data Separation is mandatory, for example when IP or personal data exclude the cloud.
Key modules we deploy are Model Access Controls & Audit Logging, Data Governance (Classification, Retention, Lineage) and Compliance Automation with ISO/NIST templates that bridge the gap between technical implementation and certification requirements. Safe prompting and output controls are especially relevant for generative models that are integrated into human-machine interfaces.
Organization, team & change management
Technology alone is not enough: successful projects need a cross-functional team with engineering, security, legal and production. In Berlin there are many interdisciplinary teams — but often the interface competence to securely integrate AI systems into production processes is missing. Therefore we operate as co-preneurs within the customer's P&L to anchor responsibilities clearly and speed up decisions.
Change management includes transparent communication plans, training for the safe operation of robotic systems and clear roles for incident response. Audit-readiness requires documented workflows, incident playbooks and regular internal audits. Only this way can certifications like ISO 27001 or TISAX be navigated efficiently.
Success factors, risks & common pitfalls
Success factors are clear governance, early threat modeling, and embedding security controls into CI/CD pipelines. Common pitfalls are unclear data usage rights, inadequate separation between development and production data, and missing logging of model decisions. These gaps often lead to retrofit costs or worse: production outages and reputational damage.
Another frequent mistake is treating compliance as an afterthought. It is better to integrate compliance requirements from the start into architecture and process design. That saves time and builds trust with partners, suppliers and authorities.
ROI, timeline & scaling expectations
A realistic timeline from PoC to production commonly ranges from 3–9 months, depending on data availability, security requirements and necessary certifications. ROI is measured not only in direct savings (fewer outages, more efficient maintenance) but also in secured business models: only with reliable compliance can larger customer and supplier networks be unlocked.
Investments in secure architectures pay off through lower risk premiums, faster approval processes and the ability to monetize more sensitive data-driven features. We help customers quantify these effects and plan a sustainable budget for security & compliance.
Final recommendations
Start with clear use-case priorities, run a quick technical proof-of-concept and plan compliance tasks in parallel with technical development. Use modular approaches — from Privacy Impact Assessments through AI Risk & Safety Frameworks to Evaluation & Red-Teaming — and embed responsibility in an interdisciplinary team.
If you're building in Berlin, remember: Reruption travels to you regularly, works on site with your teams and brings practical experience from industrial projects to make your AI solutions secure, vetted and production-ready.
Ready for an initial AI security PoC?
Start with a fast technical PoC: secure architecture, data governance and a production roadmap. We travel to Berlin regularly and work directly with your team.
Key industries in Berlin
Berlin has evolved from a post-industrial city into a vibrant hub for tech and creative entrepreneurship. The city attracts developers, researchers and founders, so innovations in robotics and industrial automation emerge alongside fintech, e-commerce and creative projects. This coexistence creates synergies: research projects can be quickly turned into prototypes, and startups find partners for industrial pilots.
The tech and startup scene is the backbone of innovation in Berlin. Many open-source projects, tools for machine learning operations and experimental labs that are relevant for robotics and automation projects originate here. Proximity to investor networks accelerates scaling — but it also increases pressure to demonstrate security and compliance early on.
In fintech, Berlin works intensively on secure, regulated infrastructures. The high standards in the financial sector shape expectations for data security and auditability, which also transfer into industrial automation: partnerships with fintech companies cause industrial data processing to meet increasingly strict governance requirements.
The e-commerce sector, driven by companies with large logistics and fulfillment operations, advances automated warehouse solutions, image analysis for quality control and the use of robotics in the supply chain. These commercial applications demand robust, scalable AI systems that are at the same time privacy-compliant and auditable.
The creative industries bring unconventional ideas to robotics and automation: collaborative robots for performances, interactive installations and media-production workflows that use generative AI. Such projects often push the boundaries of compliance and require flexible but secure architectures that allow creative experiments without neglecting regulatory risks.
Overall, Berlin faces the challenge of combining the speed of innovation with the care required for security. For industrial automation & robotics this means: technological agility must be accompanied by standardized security and compliance processes that enable both startup speed and industrial reliability.
Are your robotics projects in Berlin audit-ready?
We review your current security posture, perform a gap assessment and show concrete measures for TISAX/ISO compliance — on site in Berlin or remotely.
Key players in Berlin
Zalando has evolved from an online shoe retailer into a technology and logistics platform. Zalando invests heavily in data science and automation to optimize warehouse processes and personalization. The associated infrastructure and security requirements set benchmarks for other companies in the region.
Delivery Hero is an example of operational complexity at scale: supply chain optimization, route planning and dynamic logistics require robust, low-latency systems. The use of AI in operational systems raises industry awareness around resilience, fault tolerance and compliance.
N26 has set many standards for digital product security in Berlin. As a fintech that must comply with regulated processes, N26 shapes mutual expectations around auditability and data protection that also affect industrial IoT and automation projects when financial or billing data are involved.
HelloFresh links logistics, quality assurance and customer processes in a data-driven business model. Requirements for hygiene, traceability and quality inspection are high — parallels to industrial automation are clear, especially when it comes to quality control via image processing and robotics.
Trade Republic represents the young, regulated tech world of Berlin: lean products under strict compliance. Their approach shows how to integrate regulatory requirements into agile product cycles — a role model for robotics and automation companies operating in regulated contexts.
Alongside these major names there is a dense network of startups, research institutions and service providers that makes Berlin a unique place for connecting robotics innovation with industrial practice. These actors drive common standards and create demand for robust security and compliance solutions that we implement on site.
Ready for an initial AI security PoC?
Start with a fast technical PoC: secure architecture, data governance and a production roadmap. We travel to Berlin regularly and work directly with your team.
Frequently Asked Questions
TISAX is primarily established in the automotive and supplier industry, but it is also gaining relevance in adjacent industries. For robotics projects in Berlin, TISAX is relevant if you work with OEMs, suppliers or companies in the automotive chain. It is less about the certification itself and more about the established security processes partners expect — e.g. access controls, physical separation of sensitive systems and documented development processes.
For startups and SMEs, TISAX is often not a must from day one, but many business opportunities only arise once evidence of structured security processes can be provided. A pragmatic approach is to implement TISAX requirements as core measures early and pursue the formal certification process later.
Practically, we recommend performing threat modeling for specific robotics use cases: which data flows, where does model training occur, which interfaces could be attack vectors? This analysis helps prioritize and make TISAX-relevant controls implementable.
Our advice: start with a gap assessment for TISAX compliance, establish documented processes and implement technical measures such as network segmentation and access controls. We support designing these measures to address both TISAX and ISO 27001 requirements while remaining productivity-friendly.
The decision between cloud and local operation depends on several factors: latency requirements, data protection, IP protection and the ability to control models in safety-critical environments. In many production settings, Secure Self-Hosting & Data Separation is the preferred solution — local inference reduces latency and minimizes data exfiltration risks.
The cloud offers advantages like scalable training, easy updates and convenience, but it is not always permissible when sensitive production data or intellectual property are at stake. Hybrid architectures, where training and large batch jobs run in the cloud while inference occurs at the edge, are a commonly chosen compromise.
Technically, local operation requires strict configuration measures: hardened runtime environments, verified container images, encrypted persistence and robust access-control mechanisms. These elements are part of our standard for secure edge deployments in robotics projects.
We recommend validating the architecture decision within a PoC: measure latency, evaluation costs and security requirements. Based on these measurements, clear operating models can be defined that align compliance and operational needs.
Personal data in logfiles is a frequently underestimated risk. In industrial environments, logfiles can contain information about employees, access times or activities. First, data classification is necessary: which logs contain personal data, which only technical metadata?
Based on classification, retention policies and anonymization processes are defined. Unnecessary personal information should be removed or pseudonymized early. Traceability is important: for audits it must be documented which data was deleted or anonymized when and why.
Technically, we employ automated pipelines that classify data, mask personal fields and document lineage. Such measures help both with meeting data protection requirements and minimizing exposure risks.
Moreover, Privacy Impact Assessments are a central building block to identify risks and define appropriate technical and organizational countermeasures. In Berlin, collaborating with local data protection officers and legal counsel can be advisable, especially for cross-border data flows.
Red-teaming is essential to simulate real attack and failure scenarios. In industrial AI systems it's not enough to test for performance — you must also check how models react to manipulative inputs, whether sensor-fusion attacks are possible, or whether misclassifications could have safety-critical consequences.
A red team simulates attack vectors at both the data and model level: data poisoning, adversarial examples, manipulation of sensor data or exploitation of permission designs. These tests reveal gaps often hidden in normal QA cycles.
It is important to conduct red-teaming in a controlled and iterative manner. Tests must be documented and findings translated into technical measures — e.g. robust model architectures, input validation or stricter network restrictions. Without this feedback loop, insights often remain theoretical.
We recommend integrating red-teaming early into development cycles and converting results into a continuous security program. In Berlin we offer such assessments on site to account for real production conditions.
The timeframe varies widely: a technical proof-of-concept can be realized in days to a few weeks, depending on data access and use-case complexity. The transition to a production-ready, compliance-audited solution typically takes several months — commonly 3 to 9 months.
Main factors affecting duration are: data quality and access, the need for formal certifications (e.g. ISO 27001 or TISAX), infrastructural adjustments (edge vs. cloud) and organizational change measures. If audit evidence must be provided, the timeline extends because documentation, processes and technical controls need to be established.
An agile, modular approach speeds up the process: start with a small PoC that demonstrates feasibility, then scale step by step while implementing compliance controls in parallel. This approach minimizes risk and enables early business successes.
Our experience shows: clear goal definitions, a dedicated interdisciplinary team and regular milestones significantly reduce time-to-production. We support customers in Berlin on site to achieve milestones efficiently.
For industrial automation and robotics, ISO 27001 and industry-specific standards are often the most important references. ISO 27001 provides a solid information security management system that covers many compliance requirements. TISAX is relevant if you work with the automotive industry or its suppliers.
In addition to these standards, NIST frameworks and specific security audits for embedded systems may be relevant. The selection depends on your partners, customers and the industry in question. Startups often prioritize: ISO 27001 as a baseline, with additional audits as needed per partnership requirements.
It is important not to treat certifications as an end in themselves. More effective is to implement practical controls that can later be certified. Documentation, process embedding and technical implementation must come together to pass audits.
We accompany companies step by step: from gap analyses to implementation and audit-readiness. In Berlin we work on site with local teams to make certification processes efficient and align them with business-relevant priorities.
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Philipp M. W. Hoffmann
Founder & Partner
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Reruption GmbH
Falkertstraße 2
70176 Stuttgart
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