How do chemical, pharmaceutical and process companies in Frankfurt am Main secure their AI systems in a compliant and robust way?
Innovators at these companies trust us
The local challenge
Frankfurt is not only a financial metropolis but also a hub for companies with highly specialized processes and strict regulatory requirements. For the chemical, pharmaceutical and process industries, unsecured AI models pose a real risk: faulty recommendations in laboratory workflows, misclassified process data or uncontrolled data flows can jeopardize production, compliance and safety. Companies therefore need a clear, technically sound and auditable security strategy.
Why we have the local expertise
Our headquarters are in Stuttgart; nevertheless we are regularly on site in Frankfurt am Main and work directly with local decision‑making teams. We understand the particular proximity of Frankfurt industries to the financial world, the strict compliance expectations and the need to design technical solutions so that they withstand both internal and external audits. We travel regularly to Frankfurt am Main and collaborate with clients on site.
The requirements in Hesse are characterized by high audit frequency, demands for data isolation and frequent interfaces to external service providers. That is why we combine technical measures — secure self‑hosting architectures, strict access controls, audit logging — with organizational measures such as clear data governance processes and Privacy Impact Assessments.
Our work is aligned with industry standards like TISAX and ISO 27001 as well as sector‑specific requirements of the process and pharmaceutical industries. We develop auditable implementations and templates that integrate seamlessly into existing QM and compliance structures, so audit trails are clearly visible to regulators, customers and internal auditors.
Our references
For the process and manufacturing industries we have worked with companies like Eberspächer on solutions that analyze production and machine behavior while meeting data protection and security requirements. Projects analyzing acoustic and sensor data as well as optimizing production processes provided us with important insights for safety‑critical AI pipelines.
Our collaboration with STIHL spans years and includes product and training solutions in manufacturing and upskilling, where we had to ensure robust data flows, secure model deployments and logged user interactions — experiences directly transferable to compliance projects in chemical and pharma. In addition, the TDK project on PFAS removal brings technical chemistry knowledge and spin‑off advisory experience that gives us a solid perspective on regulatory and scientific requirements.
About Reruption
Reruption, as a co‑preneur team, not only develops strategies but delivers technical execution: we build prototypes, design secure architectures and implement compliance mechanisms into your operations. Our way of working is entrepreneurial, fast and outcome‑driven — we operate in the client's P&L, not in slide decks.
We combine engineering depth with clear governance frameworks so companies in Frankfurt can scale their AI initiatives without endangering the requirements of regulators, customers or internal quality systems. We are pragmatic, audit‑oriented and ready to take responsibility for real outcomes.
Interested in a security check for your AI systems in Frankfurt?
Book a short preliminary meeting: we travel regularly to Frankfurt and assess risks, architecture and compliance requirements on site.
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.
Comprehensive guide: AI Security & Compliance for chemical, pharma & process industries in Frankfurt am Main
This section should be a TRUE DEEP DIVE - 8-12 paragraphs minimum!
Security and compliance landscape: regulatory frameworks
The chemical, pharmaceutical and process industries operate under intense regulatory pressure: data protection laws, product‑specific requirements, regulatory supervisors and internal quality standards intersect. In Frankfurt, additional requirements arise from the proximity to the financial sector and its sensitive partners — exchanges with banks, insurers and logistics providers often demand extra safeguards and proof of data isolation.
A valid AI security strategy starts with alignment to standards such as ISO 27001 and sector‑specific control objectives. For companies with high confidentiality and security classification requirements, implementing controlled data handling and role‑based access control is also essential. Only when technical measures and organizational processes are synchronized will audits and certifications be sustainable.
Concrete use cases and their security requirements
Typical use cases in this sector — lab process documentation, safety copilots, high‑security knowledge search and internal models for process optimization — impose very different demands on security and compliance. Lab process documentation must be immutable and provably versioned, safety copilots require deterministic traceability for recommendations, and knowledge searches must integrate access restrictions, classification and data loss prevention (DLP).
For each of these scenarios we outline concrete security measures: data classification and tagging before model training, separate environments for training and inference, encrypted storage of sensitive measurement data, and complete audit logs for all model queries. Only in this way can sources of error be identified and responsibilities be demonstrated in the event of incidents.
Technical architecture: secure self‑hosting & data separation
Many companies require that critical models and data run on‑premise or in dedicated VPCs. Our modules "Secure Self-Hosting & Data Separation" and "Model Access Controls & Audit Logging" address exactly that: we design infrastructures that physically and logically separate data, enforce role‑based access, and fully log all accesses.
A secure architecture also includes hardware‑based security mechanisms (HSMs), encrypted communication channels, and differentiated deployment pipelines that separate training, testing and production. For labs and production facilities we also plan edge installations with local inference containers to minimize latency and data exfiltration.
Privacy Impact Assessments and data governance
A thorough Privacy Impact Assessment (PIA) is not a nice‑to‑have but a prerequisite for many regulatory approvals. Our module "Privacy Impact Assessments" guides the process from data inventory to risk assessment and derives appropriate controls. This includes minimal data collection, pseudonymization and data retention rules.
In parallel we build data governance frameworks with data classification, retention policies and lineage tracking. These measures are crucial to meet the expectations of internal auditors as well as external authorities and business partners in a traceable manner. Only with clear governance paths are AI models audit‑ready.
Operationalization: safe prompting, output controls and red‑teaming
The operational security of AI systems does not end after deployment. Our modules "Safe Prompting & Output Controls" and "Evaluation & Red‑Teaming of AI Systems" ensure models are continuously tested and protected against manipulation or misbehavior. Safe prompting reduces the risk that models generate unwanted or dangerous instructions.
Regular red‑teaming sprints and automated evaluations measure robustness, bias and attack scenarios. We simulate real attack vectors — from data poisoning to prompt injection — and build monitoring rules that make anomalies immediately visible. For safety copilots we develop explainer functions that provide traceable justifications for recommendations.
Compliance automation and audit‑readiness
Manuals and Excel sheets are not enough: compliance must be demonstrable through automation. With our module "Compliance Automation (ISO/NIST Templates)" we provide templates, continuous compliance checks and reporting that satisfy internal auditors and external examiners. Automated checks for configuration hygiene, access controls and encryption status reduce manual effort and improve traceability.
We produce audit packages containing all relevant artifacts: architecture diagrams, change logs, training dataset metadata, PIA reports and test results. These packages accelerate certification processes and minimize operational effort during audits by customers or authorities.
Integration into existing systems: MES, LIMS and ERP
In practice AI solutions integrate into existing management systems such as MES (Manufacturing Execution Systems), LIMS (Laboratory Information Management Systems) and ERP. Each interface is a potential risk: we design secure API gateways, role‑based access controls and message brokers with authentication and encryption mechanisms to secure data flows and accountability.
It is important that integrations do not dilute compliance boundaries: data classification must remain consistent across system borders, and logging standards must be unified. We provide integration patterns that ensure security and traceability without impairing the productivity of specialist departments.
Measuring success: ROI, timeline and team requirements
In the short term a PoC provides certainty about feasibility and risk; in the medium term automated checks reduce effort and audit costs. We set measurable KPIs: error rates in recommendations, time to audit approval, number of detected anomalies and cost per request. A typical PoC (like our offering) delivers reliable answers about architecture, costs and risks within weeks.
Organizationally, projects need a small cross‑functional core team: a security lead, a data engineer, a domain expert from lab/process and a compliance manager. We work in co‑preneur form with your team, bring engineering capacity and hand over an actionable roadmap that leads from prototype to productive, auditable operation.
Ready for an AI Security & Compliance proof‑of‑concept?
Start a €9,900 PoC: working prototype, security architecture, audit plan and live demo — tailored to chemical, pharma and process operations.
Key industries in Frankfurt am Main
Frankfurt has long been a hub of the financial world, but its economic significance extends far beyond that: logistics, insurance, life sciences and increasingly industrial service providers shape the region. Proximity to international transport routes and a dense network of specialized suppliers has made the city an attractive location for demanding, regulated industries.
The chemical and process industry in the Rhine‑Main region benefits from a dense infrastructure of research institutions, service providers and specialized machinery manufacturers. Historically, many medium‑sized companies have established themselves here as suppliers to international players; these companies now face the task of digitizing their production processes without endangering compliance or product safety.
The pharmaceutical sector in and around Frankfurt is more fragmented than in classic pharma hubs, but closely networked with the logistics and packaging industries. For pharmaceutical manufacturers, traceability, batch tracking and secure data handling are existential — requirements that AI solutions can both support and complicate if not implemented correctly.
Logistics and supply chain management are central themes in the region. A large portion of pharmaceutical and chemical products passes through complex supply chains where temperature, shelf life and legal documentation must be monitored. AI can simplify these processes, but successful solutions require robust data governance and secure integrations into TMS and WMS systems.
Another important area is the insurance and financial services industry, which acts as a customer for chemical and pharmaceutical companies. This leads to additional compliance requirements: partner due diligence, risk assessments and contractual evidence must also be considered in technical projects when sensitive data is exchanged between companies.
From a technical perspective there are great opportunities: predictive maintenance for process equipment, automated quality control, digital lab assistants and secure knowledge stores. Those who want to succeed in Frankfurt must combine technological innovation with demonstrable compliance — a balancing act that requires strategic planning and operational excellence.
Interested in a security check for your AI systems in Frankfurt?
Book a short preliminary meeting: we travel regularly to Frankfurt and assess risks, architecture and compliance requirements on site.
Important players in Frankfurt am Main
Deutsche Bank has established Frankfurt as a global hub. Its role as a major client and partner shapes the security requirements of many service providers in the region. The bank itself relies heavily on data‑driven processes and has internal compliance teams that demand demonstrable security measures from external technology partners. For AI projects in the supply chain or among service providers this means: a high degree of auditability and demonstrability must be planned from the start.
Commerzbank is another central financial actor working with strict requirements for information security and third‑party risk management. For chemical and pharmaceutical companies in the region, interfaces to financial services are often critical; therefore banks insist on clear data handling agreements and secure data transfers that are relevant for AI systems as well.
DZ Bank is strongly networked in the region’s trading and cooperation landscape, which influences audits and regulatory expectations. Projects that process market or partner data must be classified particularly cleanly here so that communication with cooperating financial institutions is legally secure.
Helaba, as a state bank, is not only a lender but also a driver for regional industrial projects. Investment decisions in technology and infrastructure projects often follow clear compliance criteria. Helaba requires solid evidence of risk and often also adherence to industrial security standards, which has direct implications for AI initiatives.
Deutsche Börse represents the interface to international capital markets; its demands for transparency and auditability are high. Companies in the region that operate capital‑market‑related data or models must maintain particularly strict controls — an environment where precision, traceability and security certifications make a difference.
Fraport, as the operator of Frankfurt Airport, is a logistics‑central actor whose infrastructure and service providers touch numerous supply chains. For chemical and pharmaceutical supply chains, stable, secure IT processes and traceable chain information are indispensable. Fraport‑adjacent ecosystems thus drive demand for secure, auditable data platforms and AI‑powered monitoring solutions.
Ready for an AI Security & Compliance proof‑of‑concept?
Start a €9,900 PoC: working prototype, security architecture, audit plan and live demo — tailored to chemical, pharma and process operations.
Frequently Asked Questions
The chemical and pharmaceutical industries have specific requirements that go beyond general IT security concerns. In addition to data protection, product safety, batch traceability and regulatory audit trails are central. While a financial service provider is primarily focused on confidentiality and integrity, pharmaceutical production additionally requires traceability for every process decision: which data was used, which model produced a recommendation, and who approved the decision?
Another distinguishing factor is the type of data: laboratory and process data can include measurements, time‑series recordings and sensor metadata that, if mishandled, can compromise process safety. In practice this means data classification, retention and lineage tracking must be enforced technically to achieve reliable and reproducible AI outcomes.
Regulatory aspects play a central role — authorities often demand traceable audits and validations. Models that affect product quality or safety must be validatable, versioned and tested, similar to regulated software in the pharmaceutical industry. Integrating PIAs, validation plans and documented test cases is therefore a normal part of such projects.
Practically, we advise companies in Frankfurt to embed compliance and security requirements already at the concept stage. This reduces later iterations and lowers the risk of delays during audits or the market launch of new solutions. Proximity to auditing institutions and capital‑market‑oriented partners only raises expectations for documentation and transparency.
Whether models must run on‑premise depends on several factors: data classification, contracts with partners, regulatory requirements and internal risk tolerance. For particularly sensitive process data or when legal regulations require local data residence, on‑premise operation is often the safest choice. It offers maximum control over data access and infrastructure.
However, hybrid approaches are often practical: training in secured cloud environments with strict tenant isolation and on‑premise inference for production systems combines cloud flexibility with control over local infrastructure. The decisive factor is the technical implementation: encryption, secure key management concepts and clear network boundaries must be in place.
For companies in Frankfurt working with banks or logistics partners, contractual aspects are also relevant — service providers often expect proof of data processing and location. We therefore recommend aligning architectural decisions early with compliance and legal teams and using proofs of concept to demonstrate technical and regulatory feasibility.
In short: on‑premise is not always mandatory, but in many security‑critical scenarios it is recommended. A solid risk analysis and a well thought‑out data flow design form the basis of a sound decision.
Preparing for an audit begins with mapping your existing security measures against the requirements of the respective standard. For ISO 27001 you need an information security management system (ISMS) with documented processes, responsibilities and demonstrable controls. For TISAX, which is common in the automotive and supplier industry, additional protection needs for prototype and development data are queried.
Concrete steps include an initial gap analysis, identification of critical assets and implementation of technical controls: encryption, access control, logging and change management. In parallel, organizational measures such as training, incident response plans and regular reviews must be established.
In addition to technical implementation, we provide preconfigured templates for policies, evidence reports and audit packages. These automate parts of the documentation and give auditors a clear, structured overview of security controls, responsibilities and test results.
It is important not to view audits as a one‑time goal but as a continuous process. After initial certification, continuous monitoring and regular internal audits are recommended to maintain compliance and close security gaps quickly.
Data governance is the backbone of any secure AI deployment in labs and production lines. It ensures data is consistently classified, versioned and traceable. Without clear governance, training data becomes inconsistent, access rights are insecure and responsibilities unclear — factors that can make models unreliable or audit‑prone.
In practice, governance initiatives start with a data inventory: which systems hold which data, who has access, and which legal requirements apply? Based on that we define classification rules (e.g. confidential, internal, public), retention policies and rules for data enrichment and cleansing.
Lineage tracking is particularly important for lab and process data: every transformation, annotation and aggregation must be documented so that the root cause can be traced in case of deviations. Technically we use metadata stores, automated pipelines and data‑level instrumentation for this purpose.
Governance is not purely technical: it requires committees, domain experts and clear escalation paths. Only then can rules be integrated into daily operations and sustained over time.
Safety copilots assist operators with safety‑critical decisions, so their error management is particularly sensitive. We build multiple lines of defense: preventive measures such as conservative model outputs, restrictive prompt guards and explicit warnings; detective measures like anomaly detection and monitoring; and reactive measures including rollback mechanisms and clearly defined escalation paths.
Before production deployment we conduct extensive test runs and red‑teaming scenarios that simulate potential misbehavior. These tests are documented and form a central part of validation for auditors. Models are assigned clear operational boundaries, and recommendations are presented with confidence values and stated error sources.
Operationally we define playbooks for incidents: who is notified, which systems are isolated and which steps are taken for forensic analysis. Playbooks are regularly tested so that rapid and coordinated measures are possible in an emergency.
Transparency towards users is also important: safety copilots must be able to explain why they make a recommendation. These explainable components help build trust and enable operators to responsibly use automated suggestions.
Our standardized AI PoC offering has a fixed price of €9,900 and is designed to deliver technical feasibility and initial security assessments in a short time. The PoC aims to produce a working prototype, performance metrics, a security architecture sketch and an actionable implementation plan. Typically we achieve these results within a few weeks, depending on data availability and access approvals.
The PoC usually includes: use‑case scoping, feasibility checks, rapid prototyping, security review (including a basic PIA) and a roadmap to production. For companies with higher isolation requirements or additional integrations we tailor the sizing, and the timeline may be extended accordingly.
Crucially, the PoC provides decision certainty. It shows whether a solution works technically, what data quality is required, which security measures are mandatory and what budgets to expect for a safe production rollout. This reduces financial risk compared to a direct full‑scale rollout.
If you are in Frankfurt, we are happy to come by: we regularly work on site with clients to clarify requirements and risks directly with your specialist departments and deliver fast, reliable results.
Contact Us!
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Philipp M. W. Hoffmann
Founder & Partner
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Reruption GmbH
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