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Securing AI: Addressing the OWASP Top 10 for Large Language Model Applications


May 13, 2025

AI Is Just Software—But It Is Not Just Software

Artificial Intelligence (AI) is frequently portrayed as a disruptive force with the potential to revolutionize industries, optimize workflows, and enhance decision-making in ways that were often seen as unattainable. While this perspective highlights AI’s impact, it overlooks a fundamental reality. AI is still software at its core—it runs on code, processes data, and operates within an infrastructure like other enterprise systems. Yet, despite these similarities, AI introduces critical differences that cannot be overlooked.

 

Unlike traditional software, which follows predefined logic and executes tasks based on explicit

programming, AI systems—particularly large language models (LLMs) are designed to operate probabilistically. These models do not adhere to fixed decision trees or structured workflows. Instead, they generate responses dynamically, relying on statistical relationships between words, phrases, and concepts. This distinction is not merely a technical detail; it has significant implications for security, governance, and

risk management.

 

Organizations that deploy AI cannot rely solely on the security frameworks and best practices traditionally applied to conventional IT systems. For example, a firewall will not prevent an AI model from inadvertently leaking sensitive information if prompted in a specific way. Similarly, a traditional endpoint detection system will not stop an attacker from poisoning a model’s training data or manipulating embeddings to alter the responses generated by the AI. Therefore, AI security requires an expanded approach that considers the unique ways AI models process input, generate output, and interact with enterprise systems.









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