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Differences Between Traditional Software Security and AI Model Security
1:59.5
AI security is different because AI systems do not behave like ordinary deterministic software. Traditional software is usually written as a set of instructions. If a condition occurs, the program follows a defined path. When a bug appears, engineers can often trace the problem to a line of code, a failed dependency, or an incorrect configuration. AI models are different. Their behavior emerges from training on large data sets and adjusting millions, billions, or even trillions of internal parameters. These parameters do not operate like easily readable rules. They encode statistical relationships that allow the model to classify images, generate text, summarize documents, recommend actions, or write code. This creates a security challenge because the model may respond unpredictably to inputs that are unusual, ambiguous, adversarial, or outside its training distribution. Two prompts that look similar to a human may produce very different model behavior. A slight change in phrasing can bypass a safety rule. A hidden pattern in an image can alter classification. A misleading document can reshape the model's response during retrieval. Explainability is also difficult. Even when a model gives a confident answer, it may not be able to reliably explain why it produced that answer or which sources shaped it. Security teams must therefore defend not only software code, servers, and ARPIS, but also model behavior itself. That means testing for robustness, monitoring outputs, controlling access to sensitive data, validating tool use, and designing systems that assume the model may be wrong or manipulated. AI security expands cyber security from protecting execution paths to protecting learned behavior.
AI Under Attack: Adversarial Inputs, Prompt Injection, and Model Security
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