1. Home
  2. Cybersecurity & Risk Management

Thales in CyberNEMO

Thales, present in 68 countries and employing more than 81,000 people, is a global leader in advanced technologies across aerospace, aeronautics, defence, digital security, cybersecurity, and biometrics. Thales has become a key multinational player in protecting citizens, strengthening state resilience, and enabling business success. Its main areas of expertise include:

  • Aerospace / Aeronautics: Thales provides advanced avionics solutions, as well as in-flight entertainment and connectivity systems for both civil and military aviation—enhancing safety, efficiency, and the onboard experience.
  • Defense: Thales is a major industry player, delivering radars, sonars, and optronics for land, air, and naval forces, along with secure communications, cybersecurity, and ISR capabilities (intelligence, surveillance, and reconnaissance).
  • Digital Security and Identity: Thales is a pioneer in digital identity and security, offering solutions for secure transactions, identity management, and data protection. Its technologies are used in finance, healthcare, and public administration to ensure the integrity and confidentiality of sensitive data.

CortAIx Labs is a cutting-edge R&D organization dedicated to advancing AI, machine learning, and data science technologies. Its expertise includes AI for defence and security, large-scale data processing, cybersecurity, robotics, and autonomous systems. The CyberNemo project brings together contributors from two of its laboratories.

The first is the VAR laboratory – Vision, Autonomy and Robotics, which develops AI algorithms and multi-agent simulation capabilities for enhanced perception and the autonomy of robotic systems. The second is the cybersecurity laboratory at CortAIx Labs, which focuses on AI security. AI Friendly Hacker is a program dedicated to AI security, covering both offensive and defensive aspects, including robustness, privacy, and intellectual property protection.

Our major contribution to the CyberNemo project is the integration of our open-source component Pybiscus, which enables secure collaborative learning. Thanks to the collaboration with our various partners, we use this component to train AI models to detect anomalies in cybersecurity tasks across the different scenarios addressed by the project.

Read More

Reading the Hacker’s Mind: How Math Can Predict the Next Step of a Cyberattack

Imagine a burglar trying to break into a high-tech smart home. If you knew exactly which window or door they were going to try next, you could stand right behind it and catch them in the act. In the digital world, this is the ultimate goal of cybersecurity.

Cyberattacks rarely happen completely at random. Hackers usually follow specific, structured paths and methodologies to reach their targets. Today, security experts are developing a groundbreaking tool to exploit this predictability: a Probabilistic Graph. This is an intelligent mathematical model capable of analyzing an ongoing cyberattack in real time and predicting—with high statistical confidence—what the hackers will do next. Building this predictive crystal ball relies on a sophisticated four-stage pipeline that turns messy, scattered internet data into an active defense shield.

Stage 1: Gathering the Digital Clues (Cyber Threat Intelligence)

The first step is all about data collection. Every single day, cybersecurity analysts around the globe publish detailed post-mortem reports on recent cyberattacks. These documents are known as Cyber Threat Intelligence (CTI) reports.

  • The Challenge: The internet is flooded with information, but not all of it is accurate, high-quality, or relevant.
  • The Solution: The pipeline automatically scans the web to harvest these CTI documents. It then puts them through a strict filtering process. Only reports containing concrete, valuable blueprints of actual cyberattacks are kept, while the digital “noise” is discarded.
Stage 2: Decoding the Hacker’s Playbook

Once the high-quality reports are selected, the system dives deep into the text to extract the underlying strategy of the attack.

  • Identifying Techniques: The system automatically extracts the specific technical methods the attackers used.
  • The Importance of Timing: In cybersecurity, order matters. The system tracks the exact chronological sequence of events—noting, for example, that the hackers first sent a phishing email, then stole user credentials, and finally encrypted the database.
  • Standardizing with MITRE ATT&CK: To ensure everyone speaks the same language, each extracted technique is linked to the MITRE ATT&CK framework—a globally recognized dictionary of hacker behaviors.

By the end of this stage, the raw text is transformed into a clean, chronological sequence of standardized technical steps.

Stage 3: Connecting the Dots

A single attack report shows you how one specific hacker thought on a Tuesday. However, when you look at hundreds of attack reports together, you begin to see how the entire hacking ecosystem operates.

In the third stage, the pipeline takes the individual attack sequences extracted from various reports and merges them into a single, unified dataset. This massive library of combined historical data serves as the foundation for the predictive model, teaching it the common habits and behavioral patterns of cybercriminals.

Stage 4: Creating the Predictive Graph

In the final stage, all the combined historical data is transformed into a Probabilistic Graph. Think of this graph as a digital roadmap with multiple interconnected intersections. Each “stop” on the map represents a specific attack technique, and the roads connecting them are weighted with mathematical probabilities.

When a company detects a new cyberattack in progress, their security team inputs the malicious activities they have observed so far. The graph instantly analyzes the data to:

  1. Map out the most common paths historically taken from that specific starting point.
  2. Calculate the statistically most likely next step the attacker will take.
Why This Changes the Game

Historically, cybersecurity has been reactive: organizations waited for an alarm to trip and then scrambled to fix the damage. By utilizing probabilistic graphs, the industry shifts toward predictive defense. Security teams can now anticipate their opponent’s next move, fortify the exact vulnerability about to be targeted, and stop a cyberattack before the worst damage is ever done.

Read More

Beyond the Product: Managing Cyber Risk Across Organisations and Value Chains
Cyber risk rarely stops at the boundary of a system

Traditional cybersecurity assessments often begin with a product, application or infrastructure component. The objective is to identify vulnerabilities, understand relevant threats and determine whether available controls reduce the risk to an acceptable level.

That remains essential, but it is no longer sufficient. Modern critical infrastructures depend on interconnected software, cloud services, edge platforms, operational technology, data providers and external suppliers. A vulnerability originating in one product may affect an organisation’s operations, spread through a supply chain and ultimately influence services delivered to society.

A major development in CyberNEMO D1.3 is the introduction of a structured hierarchy connecting these different risk perspectives.

Three connected views of risk

Product and system risk concerns an individual product, service or system of systems. The assessment examines how vulnerabilities, misconfigurations, design weaknesses or foreseeable misuse could affect intended operation. Its result is an evidence-based description of how a product could contribute to broader operational consequences.

Product-level assessments then become inputs to organisational risk management. At this level, organisations determine what a technical failure means for business processes, service continuity, compliance obligations, financial exposure and operational resilience. Multiple product risks may need to be combined because organisational impact rarely depends on one component alone.

The broadest level concerns value-chain and societal risk. Critical infrastructures rely on shared technologies and services, so a cyber incident in one organisation can trigger disruption elsewhere. Organisational risk postures from federated nodes can be correlated against cross-sector dependencies to detect coordinated threats, cascading effects and supply-chain exposure.

From isolated risks to cascading consequences

A technical vulnerability may initially appear limited to a single asset. Its significance changes when dependencies are considered. A compromised service may affect the availability of a business function, the integrity of information exchanged with partners, the operation of connected infrastructures or another organisation’s ability to deliver an essential service.

The key question therefore changes from how severe a vulnerability is to where the resulting disruption can propagate. This dependency-aware perspective reveals risks that cannot be understood by analysing individual components in isolation.

Why aggregation is difficult

Risk aggregation is not simply the addition of multiple scores. Product assessments may use different scales, assumptions, data sources and levels of confidence. Some describe technical severity; others measure business impact or operational disruption. Combining them without preserving this context may produce a misleading organisational view.

A defensible process must retain the original evidence, assumptions, uncertainty and data-quality information, dependencies among assets and organisations, the rationale for escalation and the controls operating at different levels. Traceability is therefore a prerequisite for moving from product risk to organisational and value-chain risk.

Federated awareness without centralising everything

Cross-organisational risk management requires useful intelligence to be shared without unnecessarily exposing sensitive operational information. In a federated model, each organisation can maintain control of local data and decisions while contributing selected information to a collaborative layer.

The shared layer can identify related attacks, repeated vulnerabilities, shared dependencies and potential cascading consequences that would remain invisible to an isolated participant. The objective is not central control of every local decision, but a common situational picture built from distributed evidence.

Risk management across the lifecycle

D1.3 explicitly connects product risk management with system use, DevSecOps, incident response, certification, governance and pre-market and post-market monitoring. Risk begins before deployment and continues after a system enters operation.

Design decisions, third-party components, deployment conditions, operational changes and newly discovered vulnerabilities may all change the risk posture. Assessment must therefore accompany conception, design, development, verification, deployment, operation, continuous validation, re-evaluation and retirement.

The key lesson

Cybersecurity risk management can no longer stop at the product boundary. Product risks must be translated into organisational consequences and examined against the dependencies connecting suppliers, operators and critical services.

TAKEAWAY A mature cyber-risk model preserves traceability while moving from component-level evidence to organisational decisions and cross-sector situational awareness.

Editorial basis: CyberNEMO D1.3. Public-facing version with no sensitive operational details.

Read More

From AI-Assisted Assessment to Continuous Assurance: The New Risk Management Model
AI support is not enough on its own

Artificial intelligence can analyse logs, correlate vulnerability information, identify anomalies and support mitigation decisions at a scale that would be difficult to achieve manually.

D1.2 already established AI as both an object of risk management and a tool supporting risk processes. D1.3 advances this model by introducing a more explicit framework for AI-empowered risk automation, including assurance of the AI systems performing risk-management activities.

The emergence of the assessor AI

An AI system may appear in risk management in two distinct roles. The assessed AI is the operational system whose security, behaviour and potential consequences must be evaluated. The assessor AI is the tool that supports or automates the assessment itself.

This distinction is fundamental. If an AI model determines asset criticality, classifies threats, prioritises vulnerabilities or recommends controls, errors in that model can directly affect risk decisions. The assessor must therefore be governed with the same discipline expected of other decision-support systems.

What AI can support

AI-relevant capabilities span the risk-management lifecycle: extracting information from structured and unstructured sources, discovering assets and dependencies, identifying missing records, correlating vulnerabilities with threat intelligence, recognising abnormal behaviour, estimating risk, supporting treatment selection, generating reports and monitoring changes in risks, controls and assumptions.

The potential benefit is a transition from periodic assessment to continuous risk awareness. Automation is valuable, however, only if the result remains trustworthy.

Risk information is a governed product

The updated methodology places greater emphasis on the risk management file as an organised collection of evidence, records and references. It may include asset descriptions, assumptions, vulnerability information, threat intelligence, logs, incidents, risk evaluations, treatment decisions, control-effectiveness evidence, uncertainty measurements, data-quality indicators and review records.

Assumptions, uncertainty, quality measurements and metadata are often not represented explicitly in cybersecurity information models. Their inclusion is necessary because an AI-generated result is only as defensible as the evidence and conditions behind it.

Quality becomes part of the risk calculation

A risk assessment can fail even when the calculation itself is correct. An accurate model working on incomplete asset data may miss critical exposure. Current vulnerability intelligence linked to an outdated topology may produce incorrect priorities. A recommendation generated from an invalid assumption may appear credible while being operationally unsuitable.

Quality characteristics such as completeness, accuracy, relevance, freshness, latency, consistency, provenance, traceability and fitness for the intended decision therefore become part of the evidence used to determine whether an assessment is fit for use.

Managing assumptions and uncertainty

Continuous risk management operates under incomplete knowledge. Threat actors adapt, systems change, models drift and dependency information is rarely perfect. Assumptions concerning system boundaries, threat relevance, control effectiveness, data completeness and human oversight must be explicit and monitored.

Uncertainty must also be connected to decisions. A risk that appears acceptable as a single point estimate may cross an unacceptable threshold when uncertainty in probability and consequences is considered. Treatment may therefore address the underlying risk or reduce uncertainty by improving data and system knowledge.

Assurance of the automation layer

The more responsibility assigned to an assessor AI, the stronger its assurance requirements become. An organisation must establish which data and model version produced an output, what assumptions and thresholds applied, whether the output was reviewed, how uncertainty was represented and what decision followed.

Operational logging connects model versions, inputs, outputs, errors, decisions and resulting actions. It supports monitoring, post-event investigation, auditability and re-evaluation.

Human oversight as decision control

Human involvement is part of the risk-control architecture. AI may prepare information, recommend an action or execute a pre-approved low-impact response, while a human validates actions with significant operational, legal or reputational consequences.

The central question is not whether a process is manual or automated. It is whether decision authority, escalation criteria and accountability remain explicit.

From assessment to continuous assurance

A risk assessment is no longer treated as a document produced at a single point in time. It becomes a continuously maintained body of evidence linked to system evolution, operational monitoring, incident response and control validation.

The resulting feedback loop is observe, assess, decide, treat, verify, learn and reassess. AI can accelerate each stage; governance, evidence quality, validation and human oversight determine whether that acceleration can be trusted.

The key lesson

AI can make risk management more current, scalable and responsive, but it also creates a second layer of risk: the reliability of the system performing the assessment.

TAKEAWAY Continuous assurance requires organisations to govern not only the systems being assessed, but also the data, models, assumptions and automated decisions used to assess them.

Editorial basis:  CyberNEMO D1.3. Public-facing version with no sensitive operational details.

Read More

SAAM: Turning Isolated Cyber Incident Reports Into Pan-European, Coordinated Defense

Modern cyberattacks don’t respect organizational boundaries, neither organizations are operating in isolation from each other. A breach at an energy provider can ripple into telecoms, finance or healthcare within hours. Yet, most organizations still defend themselves in isolation, filing incident reports through manual, paper-based processes that arrive too late to help anyone else.

SAAM was built to change that. SAAM is a collaborative platform for sharing cyber threat intelligence, assessing systemic risk, and coordinating incident response across Critical Infrastructures (CIs) throughout Europe, realizing in practice the share-to-benefit paradigm.

Instead of static, form-based notifications, SAAM enables continuous, machine-readable and explainable threat intelligence exchange, aligned with the EU’s NIS2 Directive, the Cyber Resilience Act (CRA), the AI Act, and the Critical Entities Resilience (CER) Directive.

How the Platform Works

At its core, SAAM is a centralized but distributed, multi-tenant, event-driven platform built around a simple principle: organizations get more value out of the system the more they contribute to it, forming a pan-European “knowledge sharing” ecosystem.

Here’s the flow:

  1. Ingestion: Distributed Critical Infrastructure operated CyberNEMO installations generate threat intelligence structured to SAAM’s own STIX 2.1 data model and send it through TAXII 2.1 to SAAM.
  2. Analysis: Everything moves through an Apache Kafka event-streaming backbone into the Systemic Risk Analysis Engine (SRAE), which enriches each event using severity, confidence, and mappings to the MITRE ATT&CK and D3FEND frameworks, identifying cascading effects and potential coordinated attacks.
  3. Governed sharing: The Sharing Engine applies policy- and Traffic-Light-Protocol-driven distribution rules based on impact scope, sector, geography, and membership in “Trusted Circles” before pushing enriched alerts out to the right stakeholders, organized by sector, country, cross-border region, or the whole of Europe.
  4. Knowledge retention: SAAM insights and findings are stored in a pan-European Knowledge Base and made accessible to its user base through a web-based Visualization Environment.

Security is baked in throughout as SAAM adopts Zero-Trust principles via a Keycloak-based Identity Management System (IMS), enforcing OAuth 2.0, OIDC, and SAML 2.0 across every component.

Built for Different Users, Different Needs

SAAM is designed around four distinct roles:

  • Cybersecurity Experts get a personalised view of their organisation’s threat exposure alerts, a submission tracker, a STIX viewer, and access to the shared knowledge base.
  • CTI & Risk Analysts work across sectors and borders to spot threat trends invisible at the level of a single organisation, mapping activity to MITRE ATT&CK and producing human-readable risk assessments.
  • Cybersecurity Authorities — national CSIRTs and sector ISACs — get a bird’s-eye view across sectors for cross-border situational awareness.
  • System Administrators manage the platform itself: infrastructure health, identity and access management, onboarding new partners, sharing policies, and audit logs.

Explainable AI at the Center

Predicting how a threat might arise or an attack cascade from one Critical Infrastructure to another is only useful if humans can understand why the system reached that conclusion.

SAAM’s Systemic Risk Analysis Engine combines several modelling approaches: LSTM networks for tracking long, multi-stage attack sequences, Transformer variants (Informer/Autoformer) for spotting complex patterns in parallel, and an optional Graph Neural Network layer that models how risk propagates across interconnected infrastructures.

Every prediction is made explainable using SHAP and LIME, with a large language model translating the technical output into plain-language narratives for human operators.

This isn’t just a nice-to-have; it directly supports the AI Act’s requirements for transparency and human oversight, while an anonymization layer keeps the system aligned with GDPR data-minimization principles.

Built With Regulation in Mind, Not as an Afterthought

SAAM’s cloud-native and modular architecture is designed to evolve alongside the harmonized regulatory objectives still being developed for the AI Act and CRA, while already supporting NIS2 incident reporting, CER risk assessment, and GDPR data-protection obligations.

More than that, SAAM follows the CyberNEMO Risk Methodology to work at the broadest possible tier of risk propagation from individual products and organisations, up through value chains, to society-level risk, closing the loop on how risk moves between interdependent organizations linked by sector or geography.

That’s the core reason CyberNEMO chose a federated design over a centralized one: risk doesn’t respect a single organization’s walls, so the platform that tracks it can’t either.

What’s Next

By pairing standardized, explainable threat-intelligence exchange with a trust-based sharing model, SAAM offers a concrete path away from isolated incident notification and toward proactive, coordinated cyber-defense across Europe.

Future work includes real-world pilot validation across energy, water, agriculture and healthcare Critical Infrastructures, along with continuous alignment as harmonized EU standards mature.

Read More

Zero Trust, Edge AI, and Confidential Computing — The Technologies Redefining Edge Security

The security architecture being built for the edge is fundamentally different from what came before. Perimeter defence — the logic of a hard external wall and a trusted interior — does not work when the “perimeter” is a sensor on a wind turbine, a camera on a factory floor, or a controller on a substation. These devices sit in physically uncontrolled environments, often connected via public networks, and there are too many of them to manage individually. The industry is converging on a new model built around three core technology trends.

Zero Trust is the foundational shift. In a Zero Trust architecture, no interaction between an edge device and its gateway is assumed safe: every request must be authenticated and authorised, regardless of where it originates or what it claims to be. For edge environments with hundreds or thousands of endpoints, this is architecturally demanding — but it is increasingly the baseline expectation set by both regulators and enterprise customers. NIS2 and the CRA effectively mandate Zero Trust principles without using the term.

Edge AI is making Zero Trust operationally viable at scale. The ENISA Threat Landscape 2024 documents that edge devices such as routers and IoT hardware are prime targets precisely because of outdated firmware and limited local monitoring capabilities. Running AI-native threat detection models directly on the edge node — rather than routing raw telemetry to a central Security Operations Centre — addresses this structural weakness head-on: a smart meter or industrial gateway can apply lightweight anomaly detection locally, flagging suspicious behaviour in milliseconds without transmitting sensitive operational data to the cloud.

In many industrial and healthcare contexts, local inference is the only architecture that simultaneously meets latency, bandwidth, and data sovereignty requirements.

Confidential Computing addresses a different but equally critical problem: what happens when sensitive workloads must run on third-party infrastructure? Hardware-based Trusted Execution Environments (TEEs) — such as Intel SGX — process data inside an encrypted enclave, meaning the infrastructure provider physically cannot access the raw data being computed. This allows organisations to use shared or commercial edge infrastructure without surrendering data confidentiality — a capability that is increasingly essential as edge deployments scale beyond what any single organisation can own outright.

Two further developments are reshaping the threat landscape itself. Private 5G Networks combined with Multi-access Edge Computing (MEC) enable compute to be placed at mobile base stations, offering high security through physical isolation of industrial traffic from public networks. ModelOps Security (AI TRiSM) is emerging as a response to adversarial attacks that target not the network infrastructure, but the integrity of the AI model itself. Recent incident analysis of cloud-edge deployments documents cases where attackers manipulated communication links between edge and cloud nodes to modify sensor data — underscoring that in environments where AI drives automated decisions, securing the model pipeline is as critical as securing the network.

These technologies are not on the horizon. They are being deployed now, in real industrial environments, by the same organisations that CyberNEMO works with.

Read More

Who Secures Europe’s Edge? A Map of the Key Players

The European edge security market is not dominated by a single category of player. It is a fragmented, competitive landscape where global hyperscalers, European industrial giants, telecom operators, and specialist security firms are all competing — and sometimes partnering — to own different layers of the stack. Understanding who does what, and where the tensions lie, is essential for anyone operating in or procuring from this market.

Hyperscalers — Microsoft (Azure IoT Edge), AWS (Greengrass), and Google Cloud (Distributed Cloud) — dominate the software stack and developer ecosystems. Their “cloud-to-edge” integration is technically seamless and benefits from enormous R&D investment. But they carry a structural liability in the European context: exposure to the US Cloud Act creates trust and sovereignty friction that no product feature can fully resolve, particularly for government, defence, and critical infrastructure customers.

The counterweight is a group of European Industrial and Security Sovereigns. Siemens Industrial Edge offers measured boot and digital signatures to ensure only authorised software runs on edge devices. Bosch IoT Suite provides secure Over-the-Air (OTA) updates. Thales leverages its defence background for high-grade encryption and identity management. Eviden (Atos) focuses on cybersecurity, encryption technologies, and trusted digital infrastructures aligned with European security requirements. These companies hold a structural “home court advantage”: deep OT expertise, long-standing relationships with EU institutions, and a trusted status that no marketing spend can replicate.

Telecommunications providers — Deutsche Telekom, Orange Business, and Telefónica — occupy a distinct strategic position: they own the network (5G/fibre) and the physical edge locations. They are moving up the value chain toward “Managed Edge Services,” bundling connectivity with security. Orange Business combines its network with Orange Cyberdefense to offer SASE (Secure Access Service Edge) and Virtual Network Edge solutions. Deutsche Telekom promotes Campus Networks for Industry 4.0, packaging connectivity and security together for industrial customers.

Providers like OVHcloud, T-Systems, and CloudFerro are carving out a distinct niche, capitalising on enterprise distrust of hyperscalers by offering sovereign cloud and edge infrastructure with contractual guarantees of data residency. This segment is growing directly in proportion to regulatory pressure — every new NIS2 enforcement action is, indirectly, a sales event for sovereign infrastructure providers.

Finally, as the talent shortage bites across the board, Managed Security Service Providers (MSSPs) are becoming increasingly critical. The European managed security services market is valued at over $11 billion in 2025 and is projected to grow at a CAGR of approximately 10% through 2033, reflecting the reality that most organisations — and virtually all SMEs — cannot build in-house edge security expertise. MSSPs are, in practice, becoming the primary delivery mechanism for edge security for a large portion of the market.

Read More

Europe’s Edge Computing Boom and the Security Gap

Europe’s digital infrastructure is being rewired. Data processing is migrating away from centralised hyperscale data centres toward the logical and physical periphery of networks — the “Edge.” This shift, often described as the evolution toward a Cloud-Edge Continuum, is no longer a future scenario: it is the present reality of Industry 4.0, the Internet of Things, and autonomous systems that simply cannot tolerate the latency of a round-trip to the cloud.

The numbers confirm the scale of what is happening. The European edge computing market is projected to grow from approximately €4.5 billion in 2024 to over €56.6 billion by 2033, driven by a Compound Annual Growth Rate exceeding 31%.To put that in perspective, this is one of the fastest-growing technology segments on the continent, outpacing most of the broader digital economy.

Yet behind these extraordinary growth figures, a structural problem is forming — one that CyberNEMO was built to address. While infrastructure investment accelerates, cybersecurity spending is not keeping pace. Security budgets are actually forecast to drop to 10.9% of overall IT spend in 2025 (Figure 1), even as the threat landscape intensifies.


Figure 1. The average security budget as a percentage of IT spending had been growing steadily until this year. Chart: CFO.com Source: IANS Research and Artico Search

ENISA’s own reporting confirms the picture: EU organisations spent an average of €1.5 million on cybersecurity in 2024, representing roughly 9% of their total IT allocations — and even that figure is under pressure from ongoing budget cuts across the continent.The result is a widening “vulnerability deficit”: European enterprises are deploying more distributed, more exposed infrastructure with proportionally fewer resources dedicated to defending it.

This is not a marginal risk. The broader European cybersecurity market — covering cloud, endpoint, and network security — is valued at approximately €53 billion in 2024 and is expected to reach €100 billion by 2030 at a CAGR of roughly 11.2%.The gap between edge deployment speed and security investment speed is, in other words, a gap between two very large numbers — and it is growing in the wrong direction.

There is also a currency dimension that often goes unnoticed. A significant share of edge hardware and software licences is priced in US dollars, sourced from American hyperscalers (AWS, Microsoft, Google). Euro-denominated European firms are therefore exposed to exchange rate volatility on top of their infrastructure costs. This is one of the quiet drivers behind the “Sovereign Cloud” movement, pushing enterprises toward local providers like Deutsche Telekom or Orange Business whose cost bases sit in euros — and whose legal obligations sit within EU jurisdiction.

The edge is where Europe’s industrial future will be computed. CyberNEMO’s mission is to ensure it is also where it will be secured.

Read More