The integration of AI across Health Care, Energy and Manufacturing has moved past the pilot phase and into mission-critical infrastructure.While high - level benefits are heavily celebrated they often obscure severe operational risks. Traditional software engineering treats code as a static,deterministic asset. Machine learning systems,however,are probabilistic,volatile and highly sensitive to environmental context. This presentation pulls back the curtain on hidden technical dangers of deployed AI systems that sit outside the awareness of typical non-specialist stakeholders. The mechanics of 'data drift' - the process by which live AI models silently degrade over time without triggering system alerts and the optimization hazards of 'reward hacking' will be examined. Also covered will be the security vulnerabilities of poisoned data pipelines,human-factor risks of automation bias and the compliance challenges posed by 'black-box' uninterpretability.