Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

John, a Cyber Threat Intelligence (CTI) analyst, turns to look at his CISO. He seems a bit rattled. John responds, “Yeah. Huge story.” “Massive. The board is worried and wants to know if this puts us at risk. We’re secure, right?” John hesitates.“Let me get back to you on that.” The CISO walks away. John races to his desk.

Humans have always sought to streamline productivity through the most convenient solutions available, prioritizing speed to stay ahead and gain an edge over the competition. From the assembly line to the cloud, the goal remains the same: do more with less friction. Today, that convenience is synonymous with AI. While these tools have revolutionized how we work, the reality remains that rapid innovation always comes with a hidden cost.

When vulnerability remediation succeeds at enterprise scale, it’s very rarely because the vulnerability management team is finding more vulnerabilities. It’s because the program was built around the idea of turning messy findings into steady, measurable risk reduction. That’s not an easy task. It’s easier to make it a numbers game, pointing to vulnerability volumes and how many findings were addressed, rather than accurately depicting how much real risk was eliminated.

When threat actors target your vendors, they’re not just looking to exploit a system for a single attack. They’re looking for every opportunity to scale up their operations. This means seeking ways to push their compromises as far downstream into the supply chain as they can go.

‍ ‍Artificial intelligence (AI) systems and GenAI tools are no longer merely being experimented with in the market. Instead, they are being embedded into the organizational infrastructure at large, shaping how enterprises process data, automate decisions, and provide core services to customers. Unfortunately, while this integration increases efficiency, it simultaneously increases exposure to a dramatic extent.