COMMENTARY: Over the past year, cybersecurity vendors have increasingly introduced AI-powered products. Yet, beneath the marketing, the reality has been far less transformative. Most so-called “AI-security enhancements” boil down to cosmetic upgrades on legacy platforms, such as giving a vintage car a digital dashboard and calling it a Tesla.For today’s security professionals, the distinction between AI-native and AI-powered has become more than technical. It’s the difference between mounting a real defense against threats and falling for a promise that leaves critical risks unaddressed.[SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Read more Perspectives here.]AI-native simply means a product built from the ground up for AI, using AI. The AI was not added as an afterthought to an existing tool. AI- native systems are designed to make sure that AI reaches its full potential. Just as cloud-native software needed to be designed from the ground up, the same thing is true for AI-native.In contrast, typically when a legacy security tool has been enhanced by AI, the AI gets added to the input layer, which means the tool now will have some sort of chat interface that will translate free text to the schema that the tool can operate on. Most of the time, the transformation will stop there, it doesn't change the way the tool works. It doesn't integrate AI capabilities deep into the system. And by AI capabilities, most of the time we mean the reasoning capabilities of AI, which sets AI-native tools apart from regular software.Raz Rotenberg, co-founder and CEO, Fabrix Security
The essential ingredient: reasoning
AI-native tools have the ability to reason, which separates them from AI-enhanced security tools. To date, machines have been very good at automating processes programmed by humans. Whatever the human wanted the machine to do, the human had to precisely tell the machine to do it. The human would have to think about all the potential scenarios and precisely describe what should happen if anything comes up. However, reasoning with AI allows for two radical advances:- Policy as intent, not code: Security teams can state high-level objectives in natural language, such as: "No engineer should have access to customer data after leaving a project." AI-native systems then interpret, operationalize, and enforce those goals—across disparate platforms and ever-changing contexts.
- Threading the needle of context: AI can synthesize relationships across identities, resources, and events; connecting dots that manually-tuned systems never could. It learns from history, adapts to emerging risks, and autonomously optimizes permissions, all without endless human review cycles.
Don’t fall for AI theater
The future of identity security requires tools that can reason, decide, and act—not just pose as “AI-powered.” Before signing on to yet another “AI” tool, consider the following:- Ask yourself: Am I buying comfort and a shinier UI, or a substantive improvement in risk posture?
- Evaluate intelligent defense capabilities: For example, an AI-native IAM tool offers a proactive, intelligent defense that can predict threats, remediate excessive permissions, and dynamically adjust access.
- Follow the innovators: History rewards those who bet on the true pioneers. Seek out vendors (think Cursor, Decagon) prioritizing AI-native architectures—not those content to follow the latest trend.
Most important, recognize the costs of waiting. The market changes fast, and every quarter spent with bolt-on AI tools is time not spent eliminating real identity threats.
