Keeping the Continuum Safe: Introducing CASB, CyberNEMO’s Security “Dispatcher”

As critical infrastructures increasingly rely on interconnected devices, cloud platforms, and edge computing, one question keeps coming up: when something suspicious happens somewhere in that chain, who decides what to do about it and how fast?

Inside the CyberNEMO project, that job belongs to the Continuum Access Security Broker (CASB), a component designed and developed by SPACE Hellas.

What CASB Actually Does

Think of CASB as a dispatcher for security incidents, the same role a control room plays at a power plant or a water treatment facility when something goes wrong, just applied to cybersecurity instead of physical operations. Across a modern IT environment spanning cloud, edge devices, and everything in between many different tools are constantly watching for suspicious behavior: unusual logins, strange network traffic, unexpected system activity. On their own, these are just incoming signals; someone, or something, still has to decide what to actually do about each one.

That’s where CASB comes in and, like any good dispatcher, it can respond in two ways.

Most of the time, CASB reacts automatically: it takes incoming alerts, matches them against known attack patterns, and immediately dispatches the appropriate response for example, restricting network access to an affected device or service. A set of guardrail policies acts as a safety net on this automatic path, stopping any action that could affect sensitive, core parts of the underlying infrastructure itself.

Alongside that, a security operator can also step in directly and dispatch a mitigation themselves through the CASB dashboard (based on Airflow software) useful when a situation calls for human judgement rather than an automatic reaction. Whichever path is used, every action is logged and visible to the team in real time, so nothing happens without a clear, traceable record of what was done and why.

This combination is deliberate. In an active incident, low-risk, well-understood responses can be dispatched instantly without waiting on a person while operators retain the ability to step in and dispatch a response themselves whenever a situation calls for their judgement, with full visibility into everything that happens either way.

Why This Matters for Critical Infrastructure

CyberNEMO’s mission is to strengthen cybersecurity across sectors like energy, water, healthcare, media, and finance sectors where a slow or wrong response to a cyberattack can have real-world consequences. CASB doesn’t work alone: it’s one piece of a larger CyberNEMO security framework, coordinating closely with other components that detect threats, enforce network policies, and maintain an overall picture of the system’s security posture.

By centralizing how response decisions are made rather than leaving each tool to act independently CASB aims to make security responses more consistent, auditable, and easier to manage across large, distributed environments.

Where Things Stand Today

CASB’s architecture is fully designed, and the core building blocks automatic detection-to-response processing, safety guardrails, and the operator dashboard have been implemented and are running in CyberNEMO’s experimental lab infrastructure. The team is now working on broadening guardrail coverage and hardening the component ahead of the next stage: validation within CyberNEMO’s real-world pilot sites.

As with any research project, this is progress-in-motion rather than a finished product and we’ll share concrete results from pilot testing as they become available, rather than before.

What’s Next

Over the coming months, the CASB team at SPACE Hellas will focus on integrating the component more tightly with the rest of the CyberNEMO platform and preparing it for pilot deployment. We’ll follow up with a more detailed look at how CASB performs once it’s tested in a live environment.

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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.

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CyberNEMO and Open Source best practices

The open-source strategy for CyberNEMO is a core pillar of its mission to establish a secure, transparent, and resilient IoT-Edge-Cloud-Data computing continuum. By building upon the open-source foundations of the NEMO project (Next Generation Meta OS), CyberNEMO leverages the Eclipse Foundation ecosystem to ensure professional governance, intellectual property management, and long-term sustainability.

The strategy is designed to accelerate the adoption of its cybersecurity innovations, ranging from Zero-Trust Network Access to AI-driven threat detection, by making them accessible to a broad community of developers and stakeholders.

This commitment to an open-source model is driven by following open-source best practices, establishing clear licensing, open sourcing core components, maintaining thorough documentation, actively contributing to community building, participating in industry events, and hosting dedicated community events.

The application of the open-source best practices has been translated into statements which have been put into practice in CyberNEMO, among which the following ones can be highlighted:

  • All the CyberNEMO repositories in Research Labs should be public. No work in private is allowed in the public repository, as well as all the issues and discussions should be public.
  • All the CyberNEMO repositories should have a README file, facilitating the engagement and adoption of the CyberNEMO code by external developers.
  • All the CyberNEMO source code files (when the programming language allows it) will have a copyright header, following the defined structure.
  • All the CyberNEMO components will use open-source libraries, and third-party library conflicts or issues will be solved by the developers.
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Secure Remote Production with CyberNEMO: The Future of Smart Media

The Media and Broadcasting Industry: New Opportunities, New Cybersecurity Challenges

The media and broadcasting industry is undergoing an unprecedented transformation. The need to produce and distribute high-quality multimedia content with massive bandwidth and minimal latency has driven the adoption of distributed infrastructures across the Edge-Cloud continuum.

However, this technological evolution brings a critical challenge: a significant increase in the surface area exposed to cyberattacks.

What is the CyberNEMO Project?

CyberNEMO (End-to-end CYBERsecurity to NEMO meta-OS) is an innovative initiative funded by the European Union’s Horizon Europe programme.

Its main objective is to add a transversal layer of comprehensive cybersecurity and trust to the IoT-Edge-Cloud computing ecosystem. To achieve massive adoption and technological maturity, CyberNEMO builds upon Zero-Trust cybersecurity systems, privacy-by-design protection, innovation, and collaboration, introducing new methods, tools, and threat analysis platforms.

The Smart Media Pilot: A Critical Environment

One of the key validation environments within the project is Pilot 3: Secure and Intelligent Media Content Supply Chain Ecosystems.

This pilot focuses on validating end-to-end cybersecurity measures throughout the entire lifecycle of professional media workflows.

The pilot addresses the inherent conflict in the media industry: the operational demand for high speed and low latency versus the critical need for robust, multi-layered security.

It focuses on mitigating sophisticated threats that can compromise the supply chain at any point—from the initial content capture (contribution phase), through intermediate processing stages (production phase), to final delivery to users (distribution phase). Protecting this ecosystem requires guaranteeing data sovereignty and system resilience without degrading media quality or introducing unacceptable delays.

The pilot is divided into two main use cases:

  • Secure and Collaborative Multimedia Content Production: Focused on secure content contribution, access control, content integrity verification, and anomaly detection during remote production.
  • Efficient and Secure Distribution across Multi-domain Edge-Cloud: Focused on secure distribution to authorized users, malicious traffic detection, and the implementation of countermeasure actions during content delivery.

How Does CyberNEMO Support the Smart Media Pilot?

The integration of CyberNEMO technologies transforms this pilot into a highly secure environment. By deploying advanced cybersecurity components, CyberNEMO provides fundamental capabilities such as:

  • Zero-Trust Architecture: Implementing strict security policies where no device or user is trusted by default, ensuring that production tools and media streams communicate only through authenticated and authorized channels.
  • Intelligent Anomaly Detection: Using behavioural analysis and security logs (network logs, video quality metrics, and access logs) to identify unusual activities that may indicate cyberattacks, credential theft, or malicious actions during live media transmission.
  • Proactive Mitigation: Detecting malicious traffic or content integrity violations in real time and automatically isolating compromised components without disrupting live production or media distribution workflows.

Building Secure Media Ecosystems

CyberNEMO provides the Smart Media sector with the cybersecurity framework and intelligent tools required to operate decentralised remote production infrastructures with confidence, resilience, and operational continuity.

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ASM Trial #2: Strengthening Cybersecurity for Smart Energy and Smart Water Infrastructures

As part of the CyberNEMO project, ASM Terni contributes as the pilot partner for Trial #2, focused on Smart Energy and Smart Water Critical Infrastructures.

ASM operates essential urban services in Terni, including electricity distribution, water services, and related digital monitoring systems. These infrastructures are increasingly connected through sensors, smart meters, SCADA systems, communication networks, and data platforms. While this digital transformation improves efficiency and service quality, it also introduces new cybersecurity challenges. Within CyberNEMO, ASM provides a real pilot environment where innovative cybersecurity solutions can be validated in practical conditions.

The trial focuses on understanding how cyber threats may affect interconnected energy and water assets. It also supports the analysis of anonymized operational data, network events, and security-related information.

The objective is not only to detect possible cyber incidents, but also to improve preparedness, resilience, and response capabilities. CyberNEMO technologies will help explore advanced monitoring, risk analysis, and mitigation approaches across the cloud-edge-IoT continuum.

For ASM, this represents an important opportunity to connect research outcomes with the needs of real critical infrastructure operators.

The pilot also highlights the importance of protecting citizen services, operational continuity, and data privacy. By participating in CyberNEMO, ASM contributes to building more secure, resilient, and trustworthy smart utility systems.

Trial #2 demonstrates how collaboration between technology providers, researchers, and infrastructure operators can support the future of European critical infrastructure protection.

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From Cyber Threat Reports to Action with Generative AI

Cyber threat intelligence is often hidden inside unstructured sources such as security blogs, advisories, and open-source reports. Although these sources contain valuable information, manually turning them into actionable intelligence is slow, difficult, and not scalable.

STS, within CyberNEMO, proposes an automated pipeline that transforms open-source cyber threat intelligence into structured and usable knowledge. The pipeline combines deterministic methods with Generative AI to support the full process: scraping threat reports, extracting relevant information, converting it into STIX 2.1 objects, storing it in OpenCTI, and using it for threat hunting and response.

The system uses a modular Python architecture and Docker-based deployment to connect different tools in a reproducible way. Stixify is used to convert raw text into structured STIX objects, while OpenCTI currently acts as the central knowledge base for visualization, sharing, and standardization.

The main contribution is showing that Generative AI can strengthen cyber defense when combined with existing standards and tools. Deterministic methods remain useful for clear indicators of compromise, while AI helps extract context, relationships, and more complex threat patterns.

Overall, STS’ initial work shows a practical path toward proactive cyber defense: transforming unstructured threat information into standardized intelligence that can support faster analysis, hunting, and response.

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Protecting People While Sharing Cyber Data: Why Anonymisation Matters in CyberNEMO

Every day, critical infrastructures generate enormous amounts of cyber data, from hospitals and smart energy networks to drones, media platforms, and logistics systems. This information is essential for developing better cybersecurity tools, but it also raises an important question:

How can we share valuable cyber data without exposing people’s privacy?

At CyberNEMO, the answer is anonymisation.

Why Technical Data Isn’t as Anonymous as It Looks

Many people assume that if a dataset doesn’t contain names, it’s already anonymous. Unfortunately, that’s not always true.

Technical information such as IP addresses, timestamps, GPS locations, or device identifiers can often be linked back to individuals, organisations, or specific systems. Even cybersecurity logs can reveal working patterns, locations, or sensitive infrastructure details.

That’s why every dataset collected across CyberNEMO’s pilots is treated as potentially identifying, even when no personal names are present.

Turning Sensitive Data into Safe, Reusable Datasets

CyberNEMO applies several anonymisation techniques that protect privacy while preserving the information researchers need.

Some examples include:

  • Replacing real identities with anonymous labels so users and devices can still be tracked within a dataset without revealing who they are.
  • Masking IP addresses to hide the exact location of computers while keeping network traffic patterns intact.
  • Generalising timestamps, for example recording activity by hour instead of by the exact second.
  • Reducing GPS precision so drone flights and smart infrastructure can be analysed without revealing precise locations.
  • Hiding sensitive server names and service details while maintaining realistic communication patterns.
  • Generalising medical information so healthcare datasets remain useful for cybersecurity research without exposing patient information.

The goal is simple: preserve the value of the data while removing the details that could identify people or critical systems.

Why This Matters

Effective anonymisation allows CyberNEMO to balance two equally important goals:

  • Protect citizens’ privacy and comply with GDPR.
  • Enable researchers, innovators, and cybersecurity experts to work with realistic datasets.

This supports the European vision of privacy by design, helping organisations collaborate without exposing sensitive information.

From Protected Data to Shared Knowledge

Once anonymised and validated, CyberNEMO datasets can be securely shared across the consortium to support the development and validation of cybersecurity technologies.

Following the FAIR principles (Findable, Accessible, Interoperable and Reusable), selected anonymised datasets are planned to be published through trusted open-data repositories such as Zenodo, subject to consortium approval and the necessary legal and ethical clearances.

Publishing datasets through Zenodo provides long-term preservation, persistent Digital Object Identifiers (DOIs), and enables researchers worldwide to discover, cite, and reuse CyberNEMO research outputs.

This approach allows researchers to evaluate new cybersecurity techniques using realistic operational data while fully respecting privacy and data protection requirements.

The benefits extend well beyond the CyberNEMO project:

  • Researchers can develop, benchmark, and validate new cybersecurity detection algorithms using realistic datasets.
  • SMEs and technology providers can accelerate the development of innovative cybersecurity products and services.
  • Public authorities and critical infrastructure operators can promote secure, trustworthy, and responsible data sharing across Europe.
  • The wider research community benefits from reusable datasets that support reproducible research and future innovation in cybersecurity and critical infrastructure protection.

Privacy Enables Innovation

Anonymisation is often viewed as a compliance requirement, but in CyberNEMO it is much more than that.

It is the foundation that transforms sensitive operational data into trusted, reusable knowledge. Instead of keeping valuable cybersecurity information locked away, anonymisation allows Europe to share what matters while protecting the people behind the data.

That’s how CyberNEMO helps build a stronger, more collaborative, and more privacy-conscious cybersecurity ecosystem.

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Gaps and opportunities in Europe’s Edge Cybersecurity Market: what remains unsolved

Understanding a market means understanding not just where money is flowing, but where genuine needs are going unmet. In European edge security, the gaps are structural and well-documented — and they represent the exact space where projects like CyberNEMO operate.

The SME security divide is perhaps the most acute. Small and Medium Enterprises face a compound problem: the upfront cost of edge infrastructure is already a stretch, and layering credible security on top of it is frequently prohibitive. Survey data identifies “high costs” and “unclear ROI” as the primary barriers to adoption. The consequence is a two-tier market where large industrial players deploy sophisticated edge security, while SMEs — which represent over 99% of European businesses and the backbone of manufacturing — remain largely exposed. ENISA’s Threat Landscape reports that nearly 40% of cyberattacks in Europe already target SMEs, making this divide a systemic risk, not just a commercial gap.

The skills shortage compounds every other problem. According to the 2024 ISC2 Cybersecurity Workforce Study, Europe faces a shortage of approximately 424,000 skilled cybersecurity workers — a gap that widened by nearly 10% in a single year. Within the EU specifically, 65% of organisations reported a cybersecurity staffing shortage in 2024. Edge security demands an especially rare profile: practitioners must simultaneously understand Kubernetes (IT), SCADA systems (OT), and 5G protocols — a combination that barely exists in the current talent pool. This scarcity is a hard ceiling on how fast the market can actually deploy the solutions it needs.

Against these structural challenges, several clear opportunities are emerging for vendors and integrators willing to address root causes rather than symptoms.

Compliance as a Service is the most immediately actionable. NIS2 and the CRA create mandatory audit, documentation, and reporting obligations that are genuinely complex to satisfy at scale. Vendors offering edge platforms that are “pre-certified” — automatically generating required audit logs and compliance artefacts — will face strong demand from organisations that lack the internal capacity to manage this themselves.

Privacy-Enhancing Technologies (PETs) — including Federated Learning and Homomorphic Encryption — are moving from academic research into commercial deployment, enabling “Privacy-by-Design” architectures where data can be processed without being exposed. For healthcare and finance in particular, this is not a differentiator: it is a prerequisite for using edge infrastructure at all.

The Sovereign Stack gap remains largely unaddressed. There is unmet demand for a seamless European alternative that combines the developer experience and scalability of a hyperscaler with the legal certainty of a local provider. And OT/IoT Managed Detection and Response (MDR) — specialist services that understand industrial protocols well enough to distinguish a cyberattack from a mechanical fault — are in high demand and chronically short supply. With the European managed security services market set to grow at roughly 10% CAGR through 2033, the structural shortage of OT-literate security specialists means that supply will struggle to keep pace with demand for years to come.

These are not niche edge cases. They are the dominant unmet needs of the market CyberNEMO is designed to serve.

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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.

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MoniKube: Security-Aware Infrastructure Discovery for Cloud-Native Environments

As organizations continue to adopt Kubernetes and cloud-native technologies, their infrastructures become increasingly complex and difficult to manage. Distributed clusters, virtual machines, containers, and interconnected services provide scalability and flexibility, but they also create significant challenges in maintaining visibility, understanding asset relationships, and identifying security risks.

MoniKube is a distributed security-aware monitoring and intelligence platform designed to address these challenges. By continuously monitoring Kubernetes and cloud-native environments, collecting telemetry data, and performing vulnerability assessments, it automatically discovers infrastructure components and builds a comprehensive representation of the operational environment. The platform correlates infrastructure, monitoring, and security information to provide organizations with a deeper understanding of their assets, dependencies, and overall security posture.

At the core of MoniKube is a security-aware knowledge graph that transforms distributed infrastructure data into a centralized and interactive model. By mapping assets and their relationships, the platform enables operators and security teams to explore infrastructure topology, understand dependencies between systems, identify exposed components, and gain valuable insights into potential risk and exposure pathways.

MoniKube discovers Kubernetes resources through the Kubernetes API and can optionally enrich the model with host-level Docker workloads. The platform integrates Trivy-based vulnerability and misconfiguration scanning, allowing assets to be continuously assessed for security weaknesses. Vulnerability information, exposure indicators, runtime metrics, and security scores are incorporated directly into the graph, enabling users to filter, compare, and prioritize risks from a single dashboard.

Beyond infrastructure discovery, MoniKube can ingest information from external security and monitoring solutions, including IDS, SIEM, and IDMEF-compatible sources. This allows the knowledge graph to remain synchronized with operational reality while providing a unified view across cloud-native and traditional systems.

MoniKube combines vulnerability information, runtime monitoring metrics, and exposure indicators into a unified security-scoring framework. It can integrate information from both cloud-native and traditional systems, creating a unified view of infrastructure regardless of underlying technology. Beyond infrastructure monitoring and security assessment, MoniKube introduces the ability to generate exportable infrastructure models that can serve as the foundation for digital twins, automating much of this process by capturing the security characteristics of operational environments and transforming them into reusable digital representations. The result is a comprehensive solution that helps organizations gain visibility into complex environments, strengthen their security posture, and transform operational infrastructure data into actionable security intelligence.

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