Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

PwC recently did an AI agent survey where they found the following: This all sounds great, right? For many reasons it is, but agentic AI creates a challenge of visibility for organizations into how AI agents are communicating with each other and external third-party vendors. Imagine a multitude of AI agents autonomously exchanging data across a complex mesh of third-party vendors and applications.

Malware, as one of many cyber threats, is not some random annoyance. Yet, there is nothing polite about it. It bypasses your firewall and establishes itself in your system. Then, escalated privileges are granted, and processes are killed. If you are particularly unlucky, malware encrypts your core and sticks around like a parasite in the CI/CD. So, it’s not about chaos but orchestration. That means you’re forgetting about something.

A newly surfaced set of vulnerabilities in the SonicWall SMA100 series appliances has captured the attention of cybersecurity professionals. While SonicWall has released patches for CVE-2025-40596 through CVE-2025-40599, and media reports point to a surge in Akira ransomware attacks targeting SonicWall SSL VPN infrastructure, CISA has not formally confirmed exploitation of these specific vulnerabilities by Akira at this time.

For years, security frameworks have served as essential tools for aligning cybersecurity practices, but they’ve also come with limitations. Designed primarily for compliance, many frameworks are rigid by nature, sometimes to the extent of being a checklist, making them ill-suited for today’s dynamic risk environments. But the threat landscape has evolved, and so too must our approach.

Every demo promises that the AI is smarter, faster, and more reliable than humans.

You’re an IT professional whose teams manage network and security. They face millions of alerts each day, from threat detections to access anomalies and from connection failures to latency spikes. Your costs are high: it’s been hard to scale your team to cope with the growing volume of events. But the stakes are also high. Miss the wrong operational alert and your network team gets swamped with tickets. Miss the wrong security alert and your whole business is exposed.

AI agents are being rapidly embedded into the SaaS ecosystem to streamline operations, trigger complex workflows, and interact with sensitive data and systems. From automating calendar updates to executing code and accessing cloud data stores, they are becoming integral to business processes. But with this integration comes risk. AI agents are often quickly deployed across SaaS environments by employees, without centralized tools to govern them.