Why Pausing to Understand Context is Better than Racing to Respond to CVEs

Security still labors under a big misassumption; if we can stay on top of every alert, we’ll be safe. Unfortunately, things are no longer that simple.

Managing Common Vulnerabilities and Exposures (CVEs) can feel like one big game of Whack-a-Mole: as soon as you’ve got one, another pops up. Even when you do catch up, asset sprawl, dark web threats, and a lack of visibility on misconfigurations can lead to breaches, regardless of having addressed every alert from your vulnerability scanner.

The reality is that risky exposures come in many different forms; CVEs are just one of them. If modern enterprises are to keep pace, understanding context is key. That way, they can make decisions based on the threat that presents the greatest immediate danger to the business—not just the one that’s next on the list.

And that’s the basis of exposure management in cybersecurity.

CVEs: The Least of Our Worries

For companies with a growing attack surface, CVEs give only a limited view of a much wider threat picture.

Thanks to the cloud, distributed workforces, virtual assets, software supply chains, and endless IoT, APIs, and BYODs, companies can be exposed to digital threats from a plethora of different vectors. Those can include:

• Misconfigurations in SaaS API security controls
• Connected IoT devices on unsecure networks
• Stolen company credential lists for sale on the dark web
• Zero-days in executive assets
• Former employee accounts with Domain Administrator privileges

And so on. Not all of these problems turn up in a penetration test, and not all will have a neatly-packaged CVSS score assigned to them. These “off the grid” risk factors are impacting companies nonetheless and leading to breaches, at a time when most SOCs are just trying to get through their vulnerability backlog.

The result is enterprises experiencing a false sense of security when, in reality, they are exposed to potentially critical threats that could result in a serious loss of data.

CISOs Need More than a CVE View

We all know the CISO is responsible for establishing priorities from the top down. Patching every CVE to come up is a worthy goal; but as CISOs are increasingly being seen in their rightful context as business-drivers, this goal can prove narrow in vision and simply not enough.

But how does a CISO get a handle on the full security picture? Risk-based vulnerability management (RBVM) is a step in the right direction, but even that falls short of completely aligning with business goals—which, let’s face it, has become a major part of the job.

Today’s attackers aren’t limiting themselves to vulnerabilities alone, and today’s CISOs can’t afford to, either.

To fully grasp the impact of today’s threats, security leaders need to understand more than what needs to be patched; they need the context of additional factors like active ransomware groups, deep and dark web forums, concurrent industry attacks, emerging malware, asset criticality and more.

This is far more data than can be communicated by a CVSS score.

What Is Exposure Management?

Exposure management leverages AI to help security leaders get a comprehensive view of their environments and the threats they face. 

As opposed to a siloed, alert-based approach, exposure management “covers your entire attack surface, including all digital assets and identities, and all forms of preventable risk like common vulnerabilities, misconfigurations and excessive permissions.”

Fundamentally, threat exposure management is based on a shift towards business-centric security. In this approach, the assets that provide the highest business value get top billing when it comes to security concern. This is the ideal state: security serving business directly, rather than existing as an end unto itself.

Why CISOs’ Careers May Depend on Adopting an Exposure Management Approach

Threat exposure management not only gives CISOs the “big picture view,” but draws a line to the most business-critical assets at risk at any given time.

As the World Economic Forum states, “Without a way to clearly map risks to value-creating assets or processes … it is hard to quantify and justify the resources that should be allocated to mitigating them.”

Gartner puts a finer point on the matter: “A security strategy based on defense is hard to validate and fund...CISOs must flip the narrative away from defense to active measures to gain executive funding and buy-in and improve security performance.”

Nothing says “stuck in a defensive rut” like a CISO whose team is operating off quarterly vulnerability reports, especially given the complexity of today’s attack surface and the multi-vector angles of attacks. By the same token, nothing spells “proactive, future-proof security” like going into the game with insight into the full deck of cards.

How AI Enables the Exposure Management at Scale

As security leaders become business enablers, threat exposure management is having its moment in cybersecurity. CISOs will always need more information about their environments, and they need that information presented with maximum cohesion. Which is why AI-driven exposure management might be more than a moment.

AI is at the forefront of the modern exposure management approach. Here’s how it’s being used.

• Unifying attack surface telemetry—including cloud and Active Directory.
• Automatically applying context and enrichment to threats.
• Prioritizing risk based on business impact.
• Allowing Natural Language Queries (NLQ) so practitioners can ask complex threat questions in plain English.
• Providing advanced analytics and reporting, saving teams hours of manual effort.

Gartner again solidifies the trend in their recent June 2025 research: “To combat the speed at which attackers exploit vulnerabilities, there will be a shift toward end-to-end automation of exposure management, including: continuous discovery, assessment, prioritization, validation and remediation of exposures.”

AI is central to this process.

AI-Powered Exposure Management in the Real-World

We’ve talked theory. Now let’s take AI-powered exposure management for a spin. Here’s what it looks like in a real-world scenario or two.

Threat Investigation

With an advanced exposure management platform, teams can type in a question like, “Which assets are exposed to Log4j?” and get a response simplified in a concise narrative, complete with digestible visuals and as-needed details.

Risk Prioritization

AI models are used to simultaneously analyze diverse datasets. This allows them to process information like:

• Attack path criticality
• Threat intelligence
• Attack path modelling
• External exposure

And present a final answer that puts top business-centric risks at the forefront.

Dynamic Risk Scores

Unlike static CVSS scores, AI produces dynamic risk scores that may assign a higher figure to medium-severity flaw in a payments API than it would to a high-severity flaw in a decommissioned test server.

This is the precise information security leaders need to strategically allocate resources.

CISOs: Staying in the Know

Exposure management is keeping pace with security threats and allowing CISOs to do the same. By delivering a comprehensive view of business-centric risks, it transcends the siloed insights of “here are this month’s vulnerabilities” or other reactive approaches.

Instead, it puts top security leaders directly in the driver’s seat. AI-enabled threat exposure management gives CISOs the time, context and resources to make the best decisions under pressure—and know that the best ones are not just the next on the list.

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