In recent years, generative AI has spread through society at extraordinary speed, rapidly expanding from everyday use to healthcare, education, and public administration. As AI capabilities advance, however, a diverse range of unprecedented challenges has emerged.

AI discussions often focus on “safety” and “security,” but the challenges facing contemporary AI extend far beyond these areas.

Safety: Risks with direct real-world consequences, including hallucinations, autonomous agents, critical infrastructure, robotics, medical AI, and physical harm. High levels of safety are essential where human life or physical actions are involved.

Security: Threats to AI systems, including cyberattacks, prompt injection, model theft, data poisoning, supply-chain attacks, and identity security. As AI becomes part of societal infrastructure, effective defense becomes increasingly important.

Governance: Institutional and operational challenges concerning accountability, human oversight, compliance, auditability, transparency, and the allocation of responsibility.

Privacy & Rights: Impacts on individual and organizational rights, including privacy, personal data, copyright, intellectual property, and fundamental human rights. Technological performance must coexist with rights protection.

Society: Broader social impacts, including dependency, manipulation, political influence, misinformation, bias and fairness, and employment effects. These challenges require responses at the societal level.

Global: Cross-border AI, AI sovereignty, regulatory fragmentation, national security, and international cooperation. Because AI inherently operates across borders, many challenges cannot be resolved by a single country.

In practice, AI systems face all six categories simultaneously. Autonomous agents, for example, raise not only safety and security concerns, but also questions of accountability, personal data protection, societal impact, and compliance across jurisdictions.

The challenge facing contemporary AI is therefore not a collection of isolated technical problems, but the simultaneous application of multiple, distinct requirements to the decisions and executions of a single AI system. This structural complexity explains why conventional issue-specific measures are insufficient and provides the basis for this book’s proposal of an integrated architecture: the Governance OS.