What is AI Safety?

AI safety is the field focused on ensuring artificial intelligence systems operate safely, reliably, and beneficially. It encompasses technical research (alignment, robustness, interpretability), policy frameworks (regulation, standards, certification), and organizational practices (audits, red-teaming, incident response).

In 2026, AI safety has moved from an academic concern to a regulatory requirement. The EU AI Act classifies AI systems by risk level and mandates safety assessments for high-risk applications. Company boards are expected to understand and govern AI safety at a strategic level.

Key AI safety concerns for enterprise applications: bias and fairness (AI systems reproducing or amplifying societal biases), robustness (AI behaving unpredictably with novel inputs), transparency (inability to explain AI decisions), and security (adversarial attacks that manipulate AI behavior).

Practical AI safety measures include: bias testing across demographic groups, adversarial testing (red-teaming), output monitoring and filtering, human-in-the-loop oversight, and incident response plans for AI failures.