Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Today’s Security Operations Centers (SOCs) have plenty of data but are overwhelmed. A single attacker can trigger alerts across various tools, each showing only part of the attack. While visibility is essential, unmanaged alert volume creates a different risk: distraction, delay, and potentially, missed threats. Fidelis Elevate solves this problem with Alert Noise Cancellation, a built-in feature that filters, verifies, and adds context to security alerts across the entire system.

Financial institutions sit at the core of the global economy and are at the top of the list for insider threat actors. Whether it’s a rogue employee leaking customer information or a third-party contractor with unchecked access, insider incidents in the financial sector can be devastating. As a result, employee monitoring in financial services has become an essential strategy for reducing risk and maintaining oversight in high-stakes environments.

Cyber teams are busy. Tools are deployed. Alerts are flowing. Dashboards light up with scores, heatmaps, and recommendations. But when I ask one simple question — “What does this mean for the business?” – I often get technical jargon or vague reassurances. That’s a problem. When cyber risk isn’t expressed in terms the business understands — continuity, customer trust, regulatory exposure, and revenue impact — it becomes abstract.

In many organisations, the security operations centre (SOC) is overwhelmed. The volume of alerts coming from tools like Sentinel, Defender for Endpoint, and Cloud Apps is high—and growing. Spending more time triaging noise than they are stopping real threats, does this sound familiar? This isn’t about analyst headcount or tool choice. It’s about architecture.

For many network and security teams, operational efficiency isn’t just desirable—it’s essential. Unfortunately, outdated CLI-based workflows often hinder teams from achieving agility, scalability, and accuracy. Enter Forward Networks’ NQE. This powerful tool enables operators to automate routine checks, streamline data extraction, and access actionable insights across the entire network. It’s not just a time-saver—it’s a transformation.

In today’s threat landscape, the market for stealer logs—collections of credentials, browser data, and session cookies harvested through infostealers—continues to evolve. While many threat actors have come and gone, others have adapted and built significant operations around data resale, log aggregation, and credential-based exploitation. We spoke directly with MoonCloud, one of the more active stealer log aggregators operating in 2025.

At Tines, our mission is to power the world’s most important workflows. That extends to how you implement and control AI across your business. We're excited to introduce the ability to build Model Context Protocol (MCP) servers directly on your Tines storyboard.

Vibe coding is a new development paradigm where large language models (LLMs) generate code from natural language prompts. The developer’s role shifts from writing every line to guiding the model toward a desired output—iterating through prompts, feedback, and high-level direction.

The recent revelation of a critical vulnerability in Base44, a prominent vibe coding platform, has spotlighted the intricate relationship between innovation and security in AI-assisted development. Researchers at Wiz uncovered a flaw in the platform that allowed unauthorized access to private enterprise applications, exposing sensitive data and raising urgent questions about the security of vibe coding practices.

In recent days, cyber threats have grown and evolved almost too rapidly for most organizations to internally keep up. Cyberattacks now cost businesses trillions annually. According to Cybersecurity consulting firms, global cybercrime damage is projected to reach USD 10.5 trillion by 2025. Such disconcerting statistics make it apparent: cybersecurity consulting companies are not merely important; they are necessities as strategic partners.