Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The platform determines what is known. AI helps determine what it means. Technology solution providers across virtually every industry are racing to add AI, in some fashion, to their offerings. Vulnerability management providers aren’t immune, either. They’re racing just as fast as the rest of them, sometimes faster. These vendors are embedding copilots, assistants, and agents powered by technologies like Anthropic Claude, OpenAI GPT, and Google Gemini.

The first Aviation ISAC summit, a little over a decade ago, was about forty people in a room in Miami, hosted by the Health ISAC. This year’s conference in Vancouver hosted more than five hundred: airlines, airports, OEMs, suppliers, and government, all in one place for three days. Many in the audience were new to the conference. One of the opening speakers asked first-timers to raise their hands, and a lot of hands went up around the room.

AI adoption is making everyone faster, including the attackers we as cybersecurity practitioners are competing with. While the attackers are getting faster thanks to AI, it’s not changing why most preventable breaches happen. That part is remaining consistent, for better or worse.

Anyone who has participated in a cybersecurity audit knows the drill and has likely asked the same question. “Is there a way to automate any of this?” When an auditor requests evidence that vulnerabilities are being identified, prioritized, remediated, and tracked according to policy, the automation question is a fair one.

Every security team knows the feeling. The quarterly vulnerability scan completes. The report lands, with thousands of findings, color-coded by CVSS severity, neatly timestamped. And the moment it’s printed, it’s already out of date. That is the fundamental flaw at the heart of traditional exposure management: it is built around a point in time.

Nucleus Insights flagged 14 vulnerabilities with real-world exploitation activity that looked risky before CISA added them to KEV. The key takeaway: all 14 were later listed in KEV. Acting on those signals would have been the right call every time, just earlier.

Let me be direct about something the industry keeps dancing around: the vulnerability problem isn’t getting better. It’s getting structurally worse. The wave of AI-generated code and compression of time to exploit thanks to frontier AI models like Mythos is about to make “worse” look quaint. If your vulnerability and exposure management program is still built around scanner cycles, ticket queues, and CVSS scores, you’re not running a security program.

Security teams didn’t need a headline to tell them that vulnerability volumes continue to be problematic. The CVE database now contains over 354,000 records. Annual disclosure rates have climbed steadily for more than a decade. And remediation backlogs have long been recognized not as an aberration, but as a fixture of the job.

Security teams can’t remediate every vulnerability the moment it appears, so prioritization must separate urgent, business-critical risks from noise. This is complicated by the fact that traditional scoring methods like CVSS often lack the context needed to decide what should be fixed first. The Stakeholder-Specific Vulnerability Categorization (SSVC) framework is one option many organizations use to fill this gap as part of automating vulnerability prioritization.