Contrary to common fear, the article argues that artificial intelligence hasn’t “broken” security systems — instead, it has revealed weaknesses in assumptions that organizations already had. Traditional security models were built around predictable, deterministic workflows where humans click buttons, APIs receive structured inputs, and authorization boundaries are explicit. When AI enters those systems, it changes how inputs and actions are interpreted, exposing gaps that were previously hidden rather than creating completely new vulnerabilities.
One major assumption that AI shatters is that inputs are predictable and structured. Traditional systems validate inputs based on known shapes — like numbers, fields, and formats — but AI models accept natural language, multi-step instructions, and contextual cues that aren’t captured by classic validation rules. This shift means even “safe” input can lead to unintended actions because AI doesn’t treat validation the same way legacy systems do.
Another assumption under scrutiny is the idea of static authorization boundaries. Security models typically check permissions at defined endpoints and roles, but AI doesn’t inherently understand what a user is allowed to do versus what a user asks it to do. This can result in cross-role data exposure or actions executed on behalf of users without traditional privilege escalation, essentially blurring the lines that were once clear in enterprise access controls.
Finally, the article explains that security logic’s dependence on determinism is undermined by AI’s inherent unpredictability. The same prompt can produce different outputs based on context, breaking regression assumptions and invalidating many traditional threat models. What looks secure at the code level may still behave insecurely in practice because AI introduces ambiguity into decision logic and context handling — not because of a flaw in security tools, but because the systems they protect were never designed for this new mode of operation.
