Before Security Can Use More AI, It Needs a Model of Reality

As the industry rapidly advances toward AI, there is a pervasive focus on buzzwords like context, tools, and harness. However, the real challenge lies in the lack of a universally accepted understanding of these terms, which impedes the effective integration of AI in security. True context should be a live model of the environment, not just enriched data, allowing for real-time understanding and decision-making. This concept is exemplified by CloudTwin and StreamForce, which emphasize a unified model of the environment where tools are not isolated features but expressions of the system's state, leading to seamless and meaningful actions. Instead of relying on external control layers or harnesses that compensate for fragmented data, these models propose that AI should operate on a shared reality. This approach facilitates predictive simulation rather than mere visibility or understanding, allowing agents to execute within constraints set by humans, shifting the role of AI from being an assistant to an operator. The paradigm shift proposed is to prioritize understanding the system's reality before increasing AI usage, suggesting that better security arises from a comprehensive model of the environment rather than merely adding more AI.