Europe's AI future: Why independent security tests could become a competitive advantage

AI security as an economic factor: What Germany needs to build now

Check instead of blindly trusting: How Europe can become more independent in AI

Artificial intelligence is becoming an economic factor – but with more powerful models and autonomously acting AI agents, the risks also increase. When systems operate software, execute business processes, or influence technical systems, a faulty response can quickly lead to a consequential action. For Germany and Europe, this raises a strategic question: How can AI be reliably tested without stifling innovation through unnecessary bureaucracy?

Independent security audits could provide companies with greater planning certainty, facilitate investment, and strengthen Europe's negotiating position vis-à-vis large technology providers. This requires clear responsibilities, qualified auditors, and access to the information necessary for meaningful testing. At the same time, audit procedures must remain proportionate so that they do not primarily benefit large providers or duplicate existing regulations. Crucially, a security infrastructure must be created that identifies risks early, enables responsible applications, and demonstrates its own economic benefits.

Europe's AI future will be decided not only in the data center, but also on the test bench

Those who cannot independently control intelligent machines remain customers of foreign power

The economic significance of artificial intelligence depends not only on how powerful the models become. Equally crucial is the conditions under which companies, authorities, and societies can delegate responsibility to them. As long as AI primarily formulates texts, generates images, or summarizes information, many errors remain confined to individual work steps. However, as soon as autonomous AI agents operate software, execute business processes, prepare payments, or influence technical systems, the risk structure changes fundamentally. An incorrect response can lead to an incorrect action. A local problem can, through automated processing, result in damage affecting multiple companies or entire value chains.

This makes AI security an economic infrastructure issue. An economy needs not only power grids, transport routes, and digital connections, but also robust methods for assessing the reliability of particularly powerful systems. For Germany and Europe, this is about more than just threat prevention. It's about investment security, industrial competitiveness, negotiating power with technology providers, and the ability to base decisions on verifiable findings.

An independent testing architecture for frontier AI could therefore create a significant competitive advantage. However, it can also become a costly, bureaucratic parallel structure if responsibilities remain unclear, tests are not meaningful, or the same evidence is required multiple times. The economically sound perspective lies neither in blanket enthusiasm for technology nor in comprehensive pre-screening of every application. It lies in a risk-based security infrastructure that identifies dangerous capabilities early, accelerates responsible applications, and must justify its own costs.

Why particularly powerful AI constitutes a different risk class

Frontier AI refers to particularly powerful models at the current technological frontier. The term does not describe a permanently defined product group. What is exceptionally powerful today may become the widespread standard after a few development steps. Therefore, for economic and regulatory classification, it is insufficient to consider individual model names or company sizes. Crucial factors are capabilities, application possibilities, reach, and the extent of potential damage.

Of particular importance is the combination of general problem-solving skills with access to tools and independent action planning. A model, for example, can generate software code. An agentic system can additionally operate development environments, execute programs, check results, and initiate further work steps. This increases its economic value, but also the importance of access rights, security boundaries, and effective intervention options.

The European AI Regulation therefore distinguishes between general AI models and those posing systemic risk. A cumulative training effort exceeding 10^25 floating-point operations serves as a legal benchmark. This value is not a direct indicator of danger, but rather a sign of particularly high performance. Models below this threshold can also be classified accordingly if their capabilities or impacts are of comparable significance. Relevant criteria include, among others, access to tools, scalability, and prevalence.

This distinction is important from an economic perspective: A high-performance base model can become part of numerous applications. A flaw in such a shared foundation may then affect not just one provider, but many downstream companies. At the same time, different applications of the same model can exhibit completely different risks. A text assistant for internal drafts must be assessed differently than an agent that modifies production software or influences industrial control systems.

A sound security architecture must therefore distinguish between model risks and application risks without overlooking their interrelationships. Testing the base model does not replace evaluating the specific use case. Conversely, not every user can independently replicate the complete security research of a globally deployed base model.

The real market failure lies in the unequal knowledge

The primary economic reason for independent audits is information asymmetry. Developers have far greater knowledge of training procedures, internal tests, known weaknesses, and technical safeguards than customers, regulators, or external integrators. While buyers can monitor a model's output, this does not guarantee they can reliably detect rare malfunctions, targeted manipulation possibilities, or problematic capabilities.

This knowledge gap is particularly consequential for AI because convincing behavior under normal conditions says little about its behavior in exceptional situations. A system can correctly perform a thousand routine tasks and yet fail with an unusual combination of data, tool access, and instructions. A good demonstration is therefore not sufficient proof of safety.

Furthermore, there are differing economic incentives. A provider generates revenue from a rapid market launch and widespread adoption. Potential damages, on the other hand, may affect customers, business partners, or uninvolved third parties. Not all consequential costs can be contractually shifted back. Particularly in cases of serious, cross-border, or societal impacts, liability and subsequent compensation remain limited.

Independent audits can narrow this gap. Their benefit lies not in eliminating all uncertainty, but in providing verifiable information that enables more economically sound decisions. Companies can calculate risks more accurately, insurers can better justify their terms and conditions, and authorities can intervene more effectively.

Independence is not merely a label. A testing body can be formally separate from the development company and yet still be heavily dependent on its contracts. Therefore, rules regarding conflicts of interest, professional qualifications, and transparent testing methods are fundamental to a credible infrastructure. Without such rules, information control risks becoming a market for reassuring certificates.

What the available data actually show about demand

A data-driven analysis must distinguish between observed problems and uncertain future risks. The available figures do not prove that particularly powerful AI inevitably leads to uncontrollable damage. However, they do show that with increasing use, concrete requirements for transparency, validation, and operational reliability arise.

A British survey on AI use in the financial sector from October 2026 illustrates this trend. Among the companies surveyed, 86 percent were using AI, compared to 75 percent in the previous survey. This represents an increase of eleven percentage points within the sample. At the same time, 40 percent of respondents identified information security and operational resilience as a significant limitation when using third-party AI systems. A lack of transparency regarding such systems was cited by 34 percent. Around two-fifths saw challenges in independent validation, ongoing monitoring and change detection, or in version control and change management. Only 27 percent reported expanded operational resilience requirements, such as joint incident testing or exit strategies.

These results should not be generalized to the entire German or European economy. They originate from a specific sector and relate to the responding companies, not to a complete survey of all market participants. Nevertheless, they provide an insightful economic signal: even in a regulated environment, the use of such measures is increasing faster than the implementation of all control mechanisms.

It is also noteworthy that transparency issues and operational resilience occur together. Companies not only need to understand how a model works in principle, but they also need to clarify what happens in the event of an update, a failure, or an unexpected change in behavior. Security assessment is therefore not an isolated technical task, but rather an integral part of operational business management.

The German AI Security Institute needs a precise institutional role

A robust national security architecture requires a public body that consolidates scientific findings, builds technical expertise, and prepares government decisions. The German AI Safety and Security Institute (AISI) can fulfill such a function. A distinction must be made between its current state of development and its broader institutional objectives.

The institute commenced operations on August 31, 2026. According to the German government's statement published at the end of October, concrete long-term organizational, personnel, and financial expansion plans had not yet been established. The plan envisions a phased development of expertise, including the assessment of the capabilities, robustness, and potential for misuse of high-performance AI. At the same time, according to this statement, the AISI is neither a regulatory authority nor a supervisory body as defined by the European AI Regulation.

This distinction is crucial. Scientific assessment, technical review, and regulatory decision-making are interconnected but not identical. An institution can investigate risks and develop courses of action without itself imposing market bans or binding regulations. If these functions are indiscriminately conflated, legal uncertainty and institutional conflicts arise.

A clear division of labor would be economically sound. AISI could co-develop testing methods, pool research knowledge, support access to models, and contextualize results for the federal government and relevant authorities. Qualified independent testing organizations could conduct technical evaluations. State responsibility for binding decisions and oversight would remain.

Such a model combines public coordination with decentralized expertise. However, it only works if the public sector remains sufficiently competent to critically evaluate audit results. Those who merely receive technical reports without being able to understand their implications do not gain genuine decision-making authority.

Why lifecycle-accompanying inspections make more economic sense

The idea of ​​a one-time safety test immediately before market launch falls short when dealing with AI. It treats a dynamic system like a largely unchanging product. In reality, behavior and risks can shift significantly due to new model versions, additional tools, modified system instructions, and different operating conditions.

Therefore, economically sound testing begins during development. At this stage, fundamental weaknesses can often still be addressed in the architecture, training design, or authorization concepts. If problems are only discovered after widespread integration, additional costs arise due to adjustments, contract changes, shutdowns, and the revision of downstream applications.

Lifecycle-based control, however, does not mean completely re-examining every change. Such a procedure would unnecessarily increase the cost of ongoing development. What is needed is a tiered change management approach. A minor adjustment to the user interface has a different significance than a new model, expanded access rights, or the release of independent actions.

The test should focus on capabilities, the effectiveness of protective mechanisms, and behavior under realistic operating conditions. This includes not only whether a model responds to a specific problematic request, but also whether safety boundaries are maintained over extended chains of action and whether the system reliably hands over to humans in uncertain situations.

The economic advantage lies in a better distribution of security work over time. Companies avoid having critical issues arise only immediately before a planned implementation. At the same time, ongoing documentation can facilitate later audits, provided it contains technically usable information and not merely demonstrates formal completeness.

Access to models is a prerequisite for reliable findings

An independent review is only as good as its access to information. Someone who is only allowed to test a public user interface can examine the visible behavior. However, many questions about development decisions, internal safeguards, or known limitations remain unanswered.

Therefore, tiered access to models, training processes, and security-relevant development information is necessary. This does not mean disclosing all trade secrets or making complete training data publicly available. The crucial point is that qualified auditors receive only the information they actually need to address a specific security issue.

Several legitimate interests collide here. Developers want to protect intellectual property and technical details. Companies and authorities need meaningful security evidence. At the same time, testing procedures must not themselves create new attack vectors, for example through inadequately secured model copies or sensitive data collections.

The appropriate response is controlled access models. These can include protected test environments, tiered permissions, confidentiality rules, and logged evaluations. The most suitable form depends on the objective of the investigation. Testing observable behavior has different requirements than examining internal security mechanisms.

This approach is also crucial from an economic perspective. If public authorities and European companies only receive information from providers they voluntarily select, their negotiating position remains weak. Common requirements can prevent each user from having to negotiate individually for the same basic evidence. At the same time, they must remain proportionate so that necessary transparency does not become a blanket demand for all development details.

Autonomous agents shift the risk from content to action

In agent-based systems, risk assessment must focus more strongly on the available authority. A language model might make an unsuitable suggestion. An agent with the appropriate access rights could then implement this suggestion immediately. Therefore, the economic consequences depend not only on the model's quality but also on the entire technical and organizational environment.

An agent that searches internal documents requires different boundaries than a system that modifies orders or runs programs in a production environment. The combination of broad privileges, long unsupervised chains of action, and inadequate logging becomes particularly critical. In such cases, a single error can trigger further actions before human intervention.

The security assessment must therefore cover the entire chain of actions. Which tools is the system allowed to use? What data can it read? What changes is it allowed to make? Which actions require additional approval? Can transactions be limited or reversed?

This leads to an important economic conclusion: not every security gain requires a better base model. Risks can often be reduced through stricter authorization levels, limited budgets, independent oversight bodies, and clear lines of authority. A less autonomous system can, under certain conditions, deliver greater overall economic benefits if it operates reliably and requires less monitoring.

Conversely, additional controls must not be merely symbolic. Human approval is of little use if the responsible person is expected to confirm so many complex processes daily that no serious review is possible. Economically viable oversight must treat human attention as a scarce resource.

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In industry, the overall system determines safety

As soon as AI influences machines, systems, or physical processes, general AI expertise is no longer sufficient. Then, specialists in machine safety, control engineering, and the respective production process must be involved. The crucial question is not only whether a model generates reasonable costs, but whether its use alters existing safety mechanisms.

For example, AI can generate maintenance suggestions, recommend production parameters, or plan motion sequences. These functions differ significantly from directly controlling safety-relevant components. The closer AI gets to physical interventions, the more important independent limits and clearly defined permissible operating states become.

For industrial applications, it is therefore essential to examine whether AI errors are mitigated by other system layers. A high-performance planning function should not automatically be granted the authority to override technical safeguards. Timing behavior, communication failures, and the loss of external services must also be considered in the evaluation.

Comparing this with established testing structures for vehicles, industrial plants, and medical devices is helpful insofar as it highlights the importance of independent verification. However, it must not lead to existing procedures being transferred unchanged to learning and versatile models. Testing methods must reconcile the specific characteristics of AI with the requirements of the respective technical system.

This presents a particular economic opportunity for Germany. Industrial experience can contribute to the secure integration of intelligent systems. However, this will only translate into a competitive advantage if AI testing is combined with practical application expertise and not simply exists alongside industrial development as an additional documentation tool.

Security can enable investments instead of just causing costs

In public debate, security audits are often portrayed as an additional burden. From a business perspective, this view is incomplete. Companies do not forgo AI applications solely due to technological immaturity, but also because of unclear responsibilities, difficult-to-estimate follow-up costs, and a lack of evidence.

A credible evaluation can reduce such uncertainties. When a company better understands which tasks a system reliably performs and what its limitations are, it can plan investments more effectively. Instead of a blanket ban or uncontrolled implementation, a phased approach becomes possible.

The economic benefits of a security measure therefore result from several components: avoided damage, reduced internal control costs, accelerated decision-making processes, and additional productive applications. These are offset by testing fees, internal preparation work, potential delays, and ongoing monitoring.

A universally applicable return on investment cannot be derived from this. Reliable, industry-comparable data is lacking. Especially in the case of rare, serious events, the probability of occurrence and the extent of damage are uncertain. Sound analysis must identify these limitations instead of relying on seemingly precise promises of savings.

Nevertheless, the economic logic can be clearly formulated: An audit is valuable if it reduces decision-relevant uncertainty at a reasonable cost. A comprehensive report without practical consequences does not meet this criterion. In contrast, a targeted test that identifies risky tool access and enables simple limitations can generate significant benefits.

Poor testing regulations can increase the market power of large providers

Safety regulations are not competition-neutral, even if they are formulated identically for everyone. Large companies can spread fixed documentation and testing costs across numerous customers. Small providers may have to finance similar basic expenses from significantly lower revenues.

This can lead to high fixed requirements exacerbating existing market concentration. A system requiring the same proof for narrowly defined, specialized models as for particularly powerful, broadly applicable models with extensive operational possibilities would be especially problematic.

The answer is not to exempt smaller providers from safety requirements altogether. Even a small company can develop or operate a dangerous system. Capabilities, reach, and operating conditions must remain the decisive factors. However, proportionality requires that evidence be tiered according to actual risk relevance.

Equally important is their reusability. If a baseline model has already been independently examined, downstream companies should be able to use these results, provided they are relevant to their specific application. They should then primarily assess additional risks associated with their integration, rather than repeating identical baseline assessments.

Open models also require a nuanced approach. Accessibility can facilitate research, oversight, and competition. However, it does not automatically answer all security questions. Conversely, a closed model does not justify an assumption of higher reliability. The crucial factor is which evidence can be verified under which conditions.

A testing market needs effective self-regulation

Binding safety requirements create a market for evaluation, technical consulting, and conformity assessment. This market can build expertise and mobilize additional resources. However, it also has its own inherent perverse incentives that must be considered from the outset.

When developers choose and pay their own testing agency, an incentive can arise to favor particularly cheap or lenient providers. Conversely, testing agencies may try to increase their profits through unnecessarily complex procedures. Neither competition alone nor government accreditation completely eliminates these risks.

Therefore, transparent quality requirements, oversight of testing activities, and effective management of conflicts of interest are necessary. Particularly sensitive evaluations could be further validated through comparative tests or independent cross-audits. The results from different testing bodies should also be methodologically comparable.

The interests of the auditing industry deserve a dispassionate examination. Organizations that advocate for additional auditing requirements can simultaneously benefit economically from them. This does not mean that their proposals are factually incorrect. It simply means that the benefits from the perspective of the overall economy must be assessed independently.

The quality of an audit architecture is therefore not measured by the number of certificates issued. What matters is whether it identifies relevant risks, triggers effective improvements, and handles comparable cases consistently. Increasing audit volume is not sufficient proof of a gain in societal safety.

Europe's institutional diversity must not lead to double-checking

A national security infrastructure must be embedded within the European framework. Otherwise, conflicting requirements, multiple submissions of documents, and uncertainty regarding the responsibilities of individual bodies are likely. Such fragmentation would be particularly costly for internationally operating companies.

The European AI Regulation already includes obligations for providers of general AI models with systemic risk. These include model assessments, the evaluation and mitigation of systemic risks, the reporting of serious incidents, and adequate cybersecurity. An additional independent auditing architecture should specify these obligations and improve their verifiability, not simply exist alongside them in a disorganized manner.

A model of division of labor therefore speaks in favor of Europe. Common methods and safety requirements can be combined with specialized national expertise. Not every country needs all technical capabilities to the same extent. At the same time, each participating country must possess sufficient expertise to make effective use of shared results.

Mutual recognition of reliable assessments is particularly important. This must not mean that the weakest assessment automatically becomes binding for everyone. Common quality standards and procedures for dealing with justified doubts are needed.

Economically, such a structure would be comparable to a European knowledge network. It distributes costs, reduces duplication of effort, and strengthens collective negotiating power. A clear separation between scientific expertise, technical evaluation, and legally responsible decision-making remains a prerequisite.

International cooperation is a security factor and a negotiating tool

The development of high-performance AI is internationally organized. Models can be trained outside Europe and still be used in European companies. Purely national regulations therefore quickly reach their practical limits, especially when information and technical access depend on foreign developers.

Coordination with partner countries such as Great Britain, Canada, and Japan can promote common testing methods and facilitate access to security-relevant information. The economic advantage lies not only in cost sharing. Jointly formulated requirements carry more weight with global suppliers than numerous conflicting individual requests.

A long-term international agreement could further stipulate reviews during development and before deployment, limits on unmanageable risks, and an international supervisory structure. Limited suspensions of development or deployment could be considered if risk thresholds are exceeded or safety evidence is insufficient. Additionally, a global reporting system for serious safety incidents would be beneficial. These elements are part of the proposed international target framework, not an existing, fully-fledged system.

However, its political feasibility remains uncertain. States have differing assessments of technological leadership, security interests, and economic openness. Furthermore, the willingness to share sensitive information with international bodies is limited.

Therefore, a phased strategy is more realistic than expecting an immediate, comprehensive global regime. Joint testing protocols, coordinated incident reporting, and mutual access to selected findings can already have practical benefits. A later agreement would then have a technical and institutional foundation, rather than consisting solely of abstract commitments.

Risk thresholds must be decisive, not merely superficially precise

The demand to suspend development or deployment in the event of unmanageable risks sounds clear. Its practical implementation, however, is not. First, it must be clarified which capabilities are considered particularly critical, which damages are to be considered, and what evidence is deemed sufficient.

A single performance metric cannot answer these questions. Training computing power is measurable, but it is not synonymous with dangerousness. Even good results in standardized tests do not prove comprehensive safety. Systems can react differently under real-world operating conditions than in known test scenarios.

A more sensible approach is a combination of capability assessments, concrete threat scenarios, review of protective mechanisms, and analysis of the planned deployment. It must be clear on what assumptions each assessment is based. A system may be acceptable with restricted rights but require a different assessment under expanded operational capabilities.

The proportionality of any intervention is also economically important. Several options exist between unrestricted release and a complete halt to development. These include limiting user groups, blocking tools, additional monitoring, or suspending certain uses.

A sustainable system requires clear procedures for reassessment. Suppliers must know what improvements are necessary and how they can demonstrate their effectiveness. Without a verifiable path back to the market, preliminary safety decisions would lead to investment risks that are difficult to calculate.

A global incident reporting system can accelerate collaborative learning

Safety knowledge isn't solely generated in the laboratory. Real-world incidents reveal which assumptions don't hold up in practical operation. If such insights remain confined to individual companies, other organizations can repeat the same mistakes.

An international reporting system could systematically compile information on serious AI security incidents. This would require common categories to ensure that comparable events are indeed recorded as comparable. Reporting deadlines, confidentiality, and access rights would also need to be regulated.

Economically, this results in a shared learning benefit. The costs of a single study can benefit many other users. At the same time, the likelihood decreases that known vulnerabilities will be exploited again due to a lack of awareness, or that the same organizational errors will occur repeatedly.

However, the design must avoid undesirable side effects. Overly broad reporting requirements can generate a flood of meaningless information. Excessive public detail can help attackers or discourage companies from reporting early.

A phased approach to information sharing is therefore necessary. Authorities and qualified experts may require different details than the general public. The crucial point is to enable security learning without unnecessarily weakening confidentiality and protection against misuse.

The shortage of skilled workers also limits the security architecture

A new institution and additional testing requirements do not automatically create technical expertise. What is needed are people who can analyze powerful models, develop attack strategies, statistically interpret results, and understand industrial implications.

These skills are distributed across different disciplines. AI research, cybersecurity, software engineering, statistics, and functional safety must collaborate. Legal and economic expertise is also required if proportionate decisions are to be derived from technical findings.

This creates competition for scarce personnel in public institutions. Therefore, sustainable funding must consider not only the number of positions, but also computing resources, secure testing environments, and professional development. A nominally large institute can remain practically weak if it lacks the necessary technical resources.

At the same time, it would be inefficient to develop all expertise centrally. Collaborations with universities, research institutions, and qualified testing organizations can unlock existing knowledge. However, the public authority must retain sufficient in-house expertise to professionally manage external services.

Continuity is crucial here as well. Short-term projects can test methods. However, a permanently operational security infrastructure requires long-term staff development and reliable working conditions. Otherwise, painstakingly acquired knowledge is regularly lost.

How to measure the economic success of a testing architecture

A security infrastructure should not only evaluate other systems, but also have its own verifiable performance targets. The mere number of tests performed or reports published says little about its effectiveness.

More meaningful metrics would be processing times, the reusability of evidence, and the number of duplicate checks avoided. Technical criteria should also be considered: Are critical weaknesses reliably identified? Can recommended countermeasures be implemented in practice? Do the assessments of different testing bodies show sufficient agreement in comparable cases?

Economic impacts also warrant systematic observation. How significantly do the burdens differ for small and large providers? Do robust audits facilitate their use in demanding applications? Do they reduce internal validation costs, or do they primarily create new reporting obligations?

Proving avoided damage remains particularly difficult. An event that does not occur cannot be directly observed. Therefore, success should not be judged solely on the basis of retrospective damage figures. Methodological quality, realistic tests, and verifiable improvements are also relevant indicators.

Such an evaluation also protects against institutional self-reinforcement. Audit requirements must not increase simply because corresponding organizations and responsibilities have been established. New obligations must plausibly demonstrate an additional gain in knowledge or safety.

Germany's opportunity lies in verifiable capacity to act

The crucial economic policy question is not whether Europe prioritizes innovation or security. This juxtaposition underestimates how strongly these two conditions influence each other. Insufficient security can stifle investment, damage trust, and delay productive applications. Inadequate regulation can have the same consequences.

A convincing strategy therefore combines three levels: high-performance research and development, broad economic application, and independent assessment of particularly relevant risks. None of these levels replaces the others. Testing expertise alone does not create competitive models. Proprietary models alone do not guarantee safety. Widespread application without robust control can create new dependencies and consequential costs.

True sovereignty lies in being able to base decisions on independent insights. Germany and Europe do not need to develop every model they implement themselves. However, they should be able to assess which capabilities they need to acquire, which risks they need to assume, and which limits they need to enforce.

An independent audit architecture is a plausible building block for this. Its success depends on clear responsibilities, qualified personnel, appropriate access to models, and internationally compatible methods. Equally important are proportionality, competitive openness, and the willingness to modify ineffective procedures.

The most economically viable approach is therefore neither a blanket safety reservation nor the fastest possible market access at any cost. It is a system that accelerates the development of high-performance AI where its risks are demonstrably limited, and intervenes effectively where this limitation cannot be achieved. Only under these conditions will technological capability become a reliably usable economic resource.

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