Aqsa Taylor is Chief Security Evangelist at Exaforce, an AI SOC company.
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On March 31, 2026, one of the most popular open-source packages, Axios, which is downloaded millions of times per week, was compromised. Malicious versions of the package were briefly distributed through the npm registry after a maintainer account was hijacked. Because Axios is embedded in countless web applications, mobile apps and back-end services, the exposure had a potentially broad reach.
Security researchers believe the malicious packages attempted to steal credentials, cloud keys, API tokens and other secrets before deploying a cross-platform remote access trojan (RAT) for persistent access. The compromised versions remained available for roughly three hours before removal.
Any security leader who reads that number knows what it means in practice. Three hours is shorter than most companies take even to review an alert. But this is the new reality of modern cybersecurity. The gap between how fast attackers are moving and how fast defenders respond is widening.
Developers got vibe coding. They stopped managing syntax and started expressing intent. The AI handles the mechanics, the developer handles the thinking and suddenly, the best engineers in the room got dramatically more effective. This is because the infrastructure was finally able to support them to the point that they didn’t need to focus on the heavy lifting, and they could spend that energy instead on creativity and execution.
Security operations teams need a parallel movement: vibe hunting.
The definition of vibe hunting is using AI agents to assist with the threat hunting process in the security operations center (SOC). So let’s say a security analyst reads a blog post documenting a widespread attack, which includes information about the compromised versions, the malicious dependency, the known bad domains and the credential theft behavior.
Instead of opening a legacy tool (like a SIEM) and crafting queries for each threat indicator one by one, they point an AI agent at the blog. The agent reads it, understands all of the indications of a breach and builds a plan to look for these indicators, and then it executes that plan.
The time it took from reading threat intelligence to having a running detection is minutes. The analyst still read the intelligence, made the judgment call about version pinning and validated the AI agent’s findings and escalations. Vibe hunting is a prime example of how security experts and AI can work together in 2026.
The first wave of AI in security operations was real but narrow: a language model in a sidebar helping write search queries faster, or a chatbot explaining what an alert meant. These are genuine improvements at the margin. They did not change the underlying architecture of the problem. Most of what gets demoed at conferences like RSA, where you see "AI-powered," is an LLM with a clean UI sitting on top of the same legacy data architecture.
That LLM agent on your traditional security platform isn’t really vibing, sorry.
Here's why. An LLM agent dropped on top of fragmented data is still working with fragmented data. It can answer questions faster, but it cannot reason across things it doesn't actually understand in context. When identity events live in one system, endpoint telemetry in another, cloud control plane logs somewhere else and SaaS activity in a fourth, and none of these share a schema or a common understanding of what entities mean across systems, adding a chatbot on top doesn't fix that. You've just given your analyst a faster way to do the same manual pivoting.
The technical reality is that a chatbot answers questions about data that it is fed. What a SOC actually needs is a system that reasons about it. That requires a different foundation entirely. A unified semantic layer. A knowledge graph that gives AI real structured context about your environment. Alerts, events, identities, configurations, relationships—all of it, understood together.
Attackers are already "vibing." They’re using AI without hesitation to exploit faster and hit harder. Reconnaissance that used to take days takes minutes. Phishing campaigns that used to require a skilled social engineer now get generated at scale, personalized and launched automatically.
What this means in practice is that attacks have grown faster than most organizations have adjusted for. The Axios window was just three hours. It was fast, targeted and designed to be in and out before a traditional SOC workflow has even fully processed the first alert.
The speed asymmetry is real, and it is getting worse. Every quarter that a security team operates on a manual hunting cycle while adversaries run automated campaigns, the gap widens. You can't hire your way out of it. You can't patch your way out of it. The only answer is meeting AI-powered offense with AI-powered defense, built on a foundation that can actually reason at the speed the threat requires.
The developers had their moment. The best engineers didn't disappear. They became dramatically more effective because the mechanical layer finally caught up to their capabilities. The skill was always there; the constraint was the tooling. SOC teams have been carrying that same constraint for years while the threat landscape moved in the other direction. The best threat hunters in the world have been spending the majority of their day in query syntax and console pivoting, work that is necessary but not where their value actually lives. Meanwhile, the other side automated that work a long time ago.
That constraint is lifting.
The SOC's vibe moment is here, finally giving them the infrastructure that matches the pace of what they're up against. This is where the tools become partners and not a burden to work with.
But vibe moments come with hype, and hype is where bad decisions get made. The teams that get this right will be the ones who ask harder questions about what's actually underneath the platform. What does the data foundation look like? How does the AI reason, and in what context? Is this a wrapper on the same broken architecture or something genuinely different?
That's the question worth asking before the next board slide.
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