Never compromise with vulnerabilities: a comprehensive survey on AI governance

AI vulnerabilities require integrated governance, not siloed fixes: The paper identifies three interconnected pillars—Intrinsic Security (system reliability), Derivative Security (real-world harm mitigation), and Social Ethics (value alignment)—showing that technical safeguards alone are insufficient without addressing societal impacts and accountability mechanisms.

Current defenses are failing against evolving threats: The research identifies a critical "generalization gap" where existing security measures fail when AI systems encounter new or slightly modified attacks, meaning today's protections may not protect against tomorrow's threats.

Evaluation methods miss real-world harms: The paper finds that inadequate evaluation protocols overlook actual risks people face, such as misinformation, inequity, security breaches, and physical harm—suggesting that current testing standards don't adequately measure what matters most to society.

Fragmented regulations create inconsistent oversight: Different jurisdictions and sectors apply different rules to AI systems, resulting in gaps where some AI applications face minimal scrutiny while others are heavily regulated, undermining consistent protection of the public.

Governance must be built in from the start, not added later: The paper emphasizes that treating AI governance as an afterthought leads to reactive, ineffective responses; instead, transparency, accountability, and trust should be foundational design principles integrated throughout AI development.