The Evolution of AI Security: Why Secure by Design Matters

The AI Security Landscape

The age of generative artificial intelligence (GenAI) has arrived, and businesses are adopting the technology at bullet train speed. Protecting AI systems requires a fundamental shift in security thinking.

Understanding the Unique AI Attack Surface

Unlike traditional cybersecurity, AI systems face unique vulnerabilities:

• Data Poisoning Attacks: Attackers secretly insert harmful inputs into training data, compromising AI systems before they're deployed.
• Prompt Injection Attacks: Carefully worded inputs that override safety measures.
• Model Deserialization Attacks: When AI models are packaged (like Pickle or PyTorch files), attackers embed malicious code within them causing system compromise upon loading.

Defense in Depth

A robust defense in depth (DiD) strategy includes data validation, model monitoring, runtime protection, and incident response layers specifically customized for AI.