Executive Summary In traditional DevOps, continuous integration and continuous deployment (CI/CD) pipelines compile code, run unit tests, and deploy software artifacts when all automated checks pass. This model assumes that code behavior is deterministic. In the world of generative AI and autonomous agents, this assumption breaks. A minor tweak to a system prompt or a…
Executive Summary The ultimate goal of artificial intelligence has often been framed as complete automation. However, in high-stakes enterprise applications—such as medical diagnostics, financial loan approvals, judicial reviews, and industrial automation—relying solely on autonomous AI agents introduces severe operational and ethical risks. AI systems lack human judgment, common sense, and the ability to understand context…
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…