Executive Summary Integrating Artificial Intelligence (AI) into enterprise operations drives productivity and unlocks new business capabilities. However, these systems also introduce complex, multi-dimensional risks. Unlike traditional software, which fails in predictable, binary ways, AI systems degrade gracefully or fail catastrophically through hallucinations, model drift, and security exploits. AI Risk Management is the practice of systematically…
Executive Summary Traditional software vulnerability scanners search for known bugs in dependencies, memory leaks, and weak authorization policies. However, deep learning models are opaque, meaning standard security scans cannot identify logic flaws or safety failures in a model’s weights. To uncover vulnerabilities in an AI system before hackers do, organizations must employ AI Red Teaming.…
Executive Summary Deploying traditional software involves pushing containers, managing databases, and securing APIs. Deploying AI applications—particularly those containing Large Language Models (LLMs) and autonomous agents—introduces a unique infrastructure risk. AI applications are non-deterministic, generate code on the fly, and execute actions (tool calling) in real-time. If an agent is hijacked via prompt injection, it could…