This systematic literature review analyzes the current state of compliance with Regulation (EU) 2024/1689 in autonomous robotic systems, focusing on cybersecurity frameworks and methodologies. Using the PRISMA protocol, 22 studies were selected from 243 initial records across IEEE Xplore, ACM DL, Scopus, and Web of Science. Findings reveal partial regulatory alignment: while progress has been made in risk management and encrypted communications, significant gaps persist in explainability modules, real-time human oversight, and knowledge base traceability. Only 40% of reviewed solutions explicitly address transparency requirements, and 30% implement failure intervention mechanisms. The study concludes that modular approaches integrating risk, supervision, and continuous auditing are essential to meet the AI Act mandates in autonomous robotics.

Smart homes that integrate Internet of Things (IoT) devices face increasing cybersecurity risks, posing significant challenges to these environments. The study explores security threats in smart homes ecosystems, categorizing them into vulnerabilities at the network layer, device level, and those from cloud-based and AI-driven systems. Research findings indicate that post-quantum encryption, coupled with AI-driven anomaly detection, is highly effective in enhancing security; however, computational resource demands present significant challenges. Blockchain authentication together with zero-trust structures builds security resilience, although they need changes to existing infrastructure. The specific security strategies show their effectiveness through ANOVA, Chi-square tests, and Monte Carlo simulations yet lack sufficient scalability according to the results. The research demonstrates the requirement for improvement in cryptographic techniques, alongside AI-enhanced threat detection and adaptive security models which must achieve a balance between performance and efficiency and real-time applicability within smart home ecosystems.

The overall security framework (see Figure 2) provides a high-level overview of the security framework. A. MCP Server-Side Mitigations 1) Network Segmentation and Microsegmentation: Network segmentation is a fundamental security strategy that goes beyond traditional perimeter-based defenses. In MCP environments, this approach is exponentially more critical due to the protocol's dynamic nature of tool interactions. Dedicated MCP Security Zones: Isolate MCP servers and critical components within dedicated network segments (e.g., Virtual Local Area Networks (VLANs), Virtual Private Cloud (VPC) subnets) with strict ingress/egress filtering rules based on the principle of least privilege. Use Security groups as well in Cloud Environments like A WS. Service Mesh Implementation: Employ a service mesh (e.g., Istio) to enforce fine-grained, identity-based traffic control (mutual Transport Layer Security - mTLS) between MCP microservices and connected tools, independent of network topology when using Kubernetes architecture. Application-Layer Filtering Gateways: Deploy gateways (e.g., Web Application Firewalls (W AFs), API Gateways) capable of deep packet inspection (DPI) for MCP traffic, configured with rules to detect protocol anomalies, malicious payloads in tool descriptions/parameters, and known attack signatures.

Digital twins have recently gained significant interest in simulation, optimization, and predictive maintenance of Industrial Control Systems (ICS). Recent studies discuss the possibility of using digital twins for intrusion detection in industrial systems. Accordingly, this study contributes to a digital twin-based security framework for industrial control systems, extending its capabilities for simulation of attacks and defense mechanisms. Four types of process-aware attack scenarios are implemented on a standalone open-source digital twin of an industrial filling plant: command injection, network Denial of Service (DoS), calculated measurement modification, and naive measurement modification. A stacked ensemble classifier is proposed as the real-time intrusion detection, based on the offline evaluation of eight supervised machine learning algorithms. The designed stacked model outperforms previous methods in terms of F1-Score and accuracy, by combining the predictions of various algorithms, while it can detect and classify intrusions in near real-time (0.1 seconds). This study also discusses the practicality and benefits of the proposed digital twin-based security framework.

Trust is vital to economics, society, and sustainable development. That's equally true when it comes to artificial intelligence. To develop trusted AI, security should be an integral part of your AI development lifecycle. With every technology paradigm change, attackers are there to exploit capabilities. In response, cyber team defense patterns have also evolved.

The shift to cloud networks and a wider attack surface brought about by new working practices during the COVID-19 pandemic have made traditional security strategies unfit for purpose, according to Steven Tee, principal solutions architect at Infoblox, speaking during a session at the Infosecurity Online event. He made the case that there needs to be much greater use of automated tools such as machine learning to effectively detect and combat cyber-attacks in the current age. Tee began by outlining the alarming increase and impact of cybercrime over recent years. "Cybercrime is a problem that either directly or indirectly affects everyone," he said. He noted that the average cost of a data breach in 2019 was almost $4m.

Artificial intelligence has been called today's new electricity, a potential fundamental building block for our society. In the same way, data is the fuel that powers the artificial intelligence (AI) engine. The process of training the AI model -- whether supervised or unsupervised -- requires feeding the algorithms with substantial amounts of data in order to learn and process information for real-time decision making. The more relevant input datasets result in better AI algorithms. In many use cases, these better AI algorithms require the trade-off of collecting private and personal data.

In Part 1 of this series, we reviewed the continued disconnect between corporate IT security spending and the cause of most security incidents. Most breaches are known to be caused by the misuse or takeover of user-access authorizations. In this blog, we suggest some machine-learning-based approaches to user access that will help improve organizational security. Machine learning uses constraint-based and pattern-matching algorithms. These techniques are ideal for analyzing behavioral patterns of people signing in to systems that contain sensitive information.