By: Kevin Gonzalez, VP of Security, Operations, and Data at Anvilogic

Everywhere I look, there's a new "AI-first" company or individual proudly branding themselves as pioneers of a new technological frontier. Frankly, it's beginning to boggle my brain. But before I lose myself to an AI-induced existential crisis, let’s set the record straight on what "AI-first" actually means, both for people and for companies.

AI-First People: Natural Intelligence Must Precede Artificial

To claim you are an "AI-first individual" is, to put it gently, missing the mark entirely. At the core, humans possess actual, natural intelligence (well, most of us do). This natural intelligence is critical—it’s foundational, forming initial thoughts and coherent reasoning. We rely on our intrinsic understanding, knowledge, and intuition to frame problems, even before we consider engaging an LLM (large language model).

Even when faced with a challenge where the final answer isn’t immediately clear, our internal reasoning shapes how we interact with AI. This makes us engineer-first, specialist-first, researcher-first—AI second. It is precisely these humans who leverage AI as a tool rather than as a crutch who will drive genuine innovation.

In contrast, self-proclaimed "AI-first" individuals often risk becoming overly reliant on models prone to hallucinations and biases. They are the very candidates likely to be replaced by the agentic systems they so enthusiastically champion. I fear for these individuals who abandon perfectly good foundational knowledge in favor of blind faith in AI. The true strength lies in being a specialist empowered by AI supplements—these people are the unstoppable forces of innovation.

AI-First Companies: Building Castles in the Sky

Turning the lens toward organizations, the "AI-first" label takes on a different meaning. Unlike people, companies don't possess thoughts—at least, not yet. A product is purpose-built, designed to perform a specific function and interact with users to the degree users engage with it. But in today’s rapidly evolving tech environment, systems now interact proactively, staying one step ahead of the user.

Imagine walking into work each day to find your AI-powered team member has already accomplished significant tasks: identifying new detection opportunities and actually constructing detection logic, triaging alerts with enriched context, or proactively aligning product features to your organization’s risk posture. This isn't simple analysis—these are agentic systems delivering actionable insights and tangible outcomes.

However, this vision is only achievable if there's a solid foundation in place. Unfortunately, too many "AI-first" startups rush headlong into the future without first building a robust architectural foundation. These companies create teams of AI agents with nowhere to operate—no clearly defined structure, no foundational architecture. The results? Heavy hallucinations, biased outputs, and ultimately, products that fail to deliver on their grand promises.

Such AI-first companies, fueled by enormous seed rounds and ambitious visions, are often doomed to one of two fates: rapid demise or swift acquisition. The latter fate at least places them within a structured environment they so desperately need, integrating them into established products with solid foundational architectures.

Purpose-Built AI: The Winning Strategy

The smarter approach—the strategy that truly capitalizes on AI’s transformative potential—is building systems with foundational product architecture at their core. This "foundation-first" approach is what separates successful organizations from fleeting startups. It involves:

• Detection Engineering: Building atomic-level, context-rich detections informed by real-world threat intelligence.
• Structured Data Enrichment: Enhancing alerts and detections with relevant context and intelligence, significantly boosting signal clarity.
• Automated Alert Triage: Leveraging advanced data science techniques like NLP, learning-to-rank, and clustering to streamline and prioritize analyst workloads.
• Continuous Improvement: Using data-driven feedback loops to refine AI outputs, ensuring systems evolve in step with emerging threats and challenges.

When executed properly, this framework transforms Security Operations Centers (SOCs) from reactive alert-triaging centers into proactive, intelligence-driven hubs—exactly the type of "AI-first" organizations we should all aspire to be.

Final Thoughts

So, whether you're an individual professional or a burgeoning startup, rethink what "AI-first" truly means. Realize that natural intelligence and foundational architecture must precede artificial intelligence. AI should always serve as an enhancement to deep, domain-specific expertise—not replace it.

After all, without strong foundations, AI-first is just an illusion.

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