AI security risks are also cultural and developmental
Security teams spend much of their time tracking vulnerabilities, abuse patterns, and system failures. A new study argues that many AI risks sit deeper than technical flaws. Cultural assumptions, uneven development, and data gaps shape how AI systems behave, where they fail, and who absorbs the harm.
The research was produced by a large international group of scholars from universities, ethics institutes, and policy bodies, including Ludwig Maximilian University of Munich, the Technical University of Munich, and the African Union. It examines AI through international human rights law, with direct relevance to security leaders responsible for AI deployment across regions and populations.
AI systems carry hidden assumptions
The study finds that AI systems embed cultural and developmental assumptions at every stage of their lifecycle. Training data reflects dominant languages, economic conditions, social norms, and historical records. Design choices encode expectations about infrastructure, behavior, and values.
These assumptions affect system accuracy and safety. Language models perform best in widely represented languages and lose reliability in under-resourced ones. Vision and decision systems trained in industrialized environments misread behavior in regions with different traffic patterns, social customs, or public infrastructure. These gaps increase error rates and create uneven exposure to harm.
From a cybersecurity perspective, these weaknesses resemble systemic vulnerabilities. They widen the attack surface by producing predictable failure modes across regions and user groups.
Cultural misrepresentation creates security exposure
The research shows that AI systems increasingly shape cultural expression, religious understanding, and historical narratives. Generative tools summarize belief systems, reproduce artistic styles, and simulate cultural symbols at scale.
Errors in these representations influence trust and behavior. Communities misrepresented by AI outputs disengage from digital systems or challenge their legitimacy. In political or conflict settings, distorted cultural narratives contribute to disinformation, polarization, and identity-based targeting.
Security teams working on information integrity and influence operations encounter these risks directly. The study positions cultural misrepresentation as a structural condition that adversaries exploit rather than an abstract ethics issue.
Development gaps magnify AI risk
The right to development plays a central role in the findings. AI infrastructure relies on compute access, stable power, data availability, and skilled labor. These resources remain unevenly distributed worldwide.
Systems designed with assumptions of reliable connectivity or standardized data pipelines fail in regions where those conditions do not hold. Healthcare, education, and public service applications show measurable performance drops when deployed outside their original development context.
These failures expose organizations to cascading risks. Decision support tools generate flawed outputs. Automated services exclude segments of the population. Security monitoring systems miss signals embedded in local language or behavior.
The study treats these outcomes as predictable consequences of uneven development.
AI governance overlooks cultural and developmental risk
Existing AI governance frameworks address bias, privacy, and safety but often miss cultural and developmental dimensions. Risk categories rely on generalized assumptions about users and environments. Accountability structures fragment responsibility across global supply chains.
The research identifies this gap as a governance blind spot. Cultural and developmental harms accumulate across systems, vendors, and deployments. No single actor owns the resulting risk.
For cybersecurity leaders, this resembles third-party and systemic risk. Individual controls do not eliminate exposure when the broader ecosystem reinforces the same assumptions.
Epistemic limits affect detection and response
The study also highlights epistemic limits in AI systems. Models operate on statistical patterns and lack awareness of missing data. Cultural knowledge, minority histories, and local practices often remain absent from training sets.
This limitation affects detection accuracy. Threat signals expressed through local idioms, cultural references, or non-dominant languages receive weaker model responses. Automated moderation and monitoring tools suppress legitimate expression while missing coordinated abuse.
Security teams relying on AI-driven detection inherit these blind spots. The research frames epistemic limits as structural constraints that shape incident response quality across regions.
Cultural rights intersect with security outcomes
The authors connect cultural rights with system integrity and resilience. Communities retain interests in how their data, traditions, and identities appear in AI systems. Exclusion from these decisions reduces trust and cooperation.
Low trust weakens reporting, compliance, and adoption of security controls. Systems perceived as culturally alien or extractive encounter resistance that undermines security goals.
Cultural rights and development conditions shape how AI systems perform, where they break, and who experiences harm.