Most organizations feel the imperative to keep pace with continuing advances in AI capabilities, as highlighted in a recent MIT Technology Review Insights report . That clearly has security implications, particularly as organizations navigate a surge in the volume, velocity, and variety of security data. This explosion of data, coupled with fragmented toolchains, is making it increasingly difficult for security and data teams to maintain a proactive and unified security posture. Data and AI teams must move rapidly to deliver the desired business results, but they must do so without compromising security and governance. As they deploy more intelligent and powerful AI capabilities, proactive threat detection and response against the expanded attack surface, insider threats, and supply chain vulnerabilities must remain paramount. "I'm passionate about cybersecurity not slowing us down," says Melody Hildebrandt, chief technology officer at Fox Corporation, "but I also own cybersecurity strategy.

The Model Context Protocol (MCP) has been proposed as a unifying standard for connecting large language models (LLMs) with external tools and resources, promising the same role for AI integration that HTTP and USB played for the Web and peripherals. Yet, despite rapid adoption and hype, its trajectory remains uncertain. Are MCP marketplaces truly growing, or merely inflated by placeholders and abandoned prototypes? Are servers secure and privacy-preserving, or do they expose users to systemic risks? And do clients converge on standardized protocols, or remain fragmented across competing designs? In this paper, we present the first large-scale empirical study of the MCP ecosystem. We design and implement MCPCrawler, a systematic measurement framework that collects and normalizes data from six major markets. Over a 14-day campaign, MCPCrawler aggregated 17,630 raw entries, of which 8,401 valid projects (8,060 servers and 341 clients) were analyzed. Our results reveal that more than half of listed projects are invalid or low-value, that servers face structural risks including dependency monocultures and uneven maintenance, and that clients exhibit a transitional phase in protocol and connection patterns. Together, these findings provide the first evidence-based view of the MCP ecosystem, its risks, and its future trajectory.

Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and neglected other interfering aspects such as the social media dimension. To fill this gap, we propose STRisk which is a predictive system where we expand the scope of the prediction task by bringing into play the social media dimension. We study over 3800 US organizations including both victim and non-victim organizations. For each organization, we design a profile composed of a variety of externally measured technical indicators and social factors. In addition, to account for unreported incidents, we consider the non-victim sample to be noisy and propose a noise correction approach to correct mislabeled organizations. We then build several machine learning models to predict whether an organization is exposed to experience a hacking breach. By exploiting both technical and social features, we achieve a Area Under Curve (AUC) score exceeding 98%, which is 12% higher than the AUC achieved using only technical features. Furthermore, our feature importance analysis reveals that open ports and expired certificates are the best technical predictors, while spreadability and agreeability are the best social predictors.

XDR, or Extended Detection and Response, is an emerging security technology that is rapidly gaining popularity in the cybersecurity industry. It is a comprehensive security solution that offers a unified approach to threat detection, investigation, and response across multiple endpoints, networks, and cloud environments. In today's digital age, cyber threats are becoming increasingly sophisticated and diverse, making it difficult for organizations to detect and respond to them in a timely and effective manner. Traditional security solutions, such as antivirus software, firewalls, and intrusion detection systems, are no longer sufficient to protect against the complex and evolving threat landscape. It collects and correlates data from various sources, including endpoints, network devices, and cloud platforms, and applies advanced analytics and machine learning algorithms to identify suspicious activity and potential threats.

The emergence of effective natural language processing tools such as ChatGPT means it's time to begin understanding how to harden against AI-enabled cyberattacks. The natural language generation capabilities of large language models (LLMs) are a natural fit for one of cybercrime's most important attack vectors: phishing. Phishing relies on fooling people and the ability to generate effective language and other content at scale is a major tool in the hacker's kit. Fortunately, there are several good ways to mitigate this growing threat. A leader tasked with cybersecurity can get ahead of the game by understanding where we are in the story of machine learning (ML) as a hacking tool.

Artificial intelligence when combined with machine learning becomes a potent source of security. Our future depends on the professional manoeuvre of handling data online and protection from cyber hackers. Both AI and machine learning are changing the way we perceived technology earlier. They both can augment the safety measures of various applications that become a soft target for cybercriminals. Let us delve deeper into the concept with some basic notions of artificial intelligence and machine learning.

Cybersecurity is the security cornerstone of digital transformation of the power grid and construction of new power systems. The traditional network security situation quantification method only analyzes from the perspective of network performance, ignoring the impact of various power application services on the security situation, so the quantification results cannot fully reflect the power information network risk state. This study proposes a method for quantifying security situation of the power information network based on the evolutionary neural network. First, the security posture system architecture is designed by analyzing the business characteristics of power information network applications. Second, combining the importance of power application business, the spatial element index system of coupled interconnection is established from three dimensions of network reliability, threat, and vulnerability. Then, the BP neural network optimized by the genetic evolutionary algorithm is incorporated into the element index calculation process, and the quantitative model of security posture of the power information network based on the evolutionary neural network is constructed. Finally, a simulation experiment environment is built according to a power sector network topology, and the effectiveness and robustness of the method proposed in the study are verified.

In Cybersecurity, systems that can learn and adapt to evolving threats are much more competent than " fine-tuned " systems to identify these threats. Consequently, using artificial intelligence (AI) for cybersecurity provides a compelling use case. Typical security systems like firewalls, antivirus or intrusion detection systems (IDS) can only detect threats they are already familiar with. This leaves enough room for zero-day attacks and other matured forms of malware. A security system that's powered by AI can identify evolving threats and even adjusts its defences to restrict the danger until it can be effectively neutralized.

NetApp a global, cloud-led, data-centric software company, announced the general availability of Spot Security. Built for the cloud, Spot Security delivers a solution for continuous assessment and analysis of cloud security posture. Spot Security enables DevOps and SecOps teams to easily collaborate to identify misconfigurations, reduce their potential attack surface, and ensure compliance. Spot Security's agentless technology analyzes cloud resource relationships to provide clear visibility and prioritized actions, automatically determining the prospective exposure of each cloud resource and surfacing critical security threats based on their potential impact to the organization. These automated actions mitigate alert fatigue and keep cloud infrastructure secure and operations teams efficient.

With global cybercrime costs expected to reach $10.5 trillion annually by 2025, according to Cybersecurity Ventures, it comes as little surprise that the risk of attack is companies' biggest concern globally. To help businesses uncover and fix the vulnerabilities and misconfigurations affecting their systems, there is an (over)abundance of solutions available. But beware, they may not give you a full and continuous view of your weaknesses if used in isolation. With huge financial gains to be had from each successful breach, hackers do not rest in their hunt for flaws and use a wide range of tools and scanners to help them in their search. Beating these criminals means staying one step ahead and using the most comprehensive and responsive vulnerability detection support you can.