Trusting AI Models Isn’t a Geography Problem
In an age where artificial intelligence is becoming an integral part of our daily lives, the question of trust in AI models is more pertinent than ever. The focus often shifts toward the origins of these models, with particular emphasis on Chinese AI models versus those developed by Western companies such as OpenAI, Anthropic, or Google. Yet when we peel back the layers of geography and corporate branding, we find that the potential risks of model origin, especially concerning model poisoning, are remarkably universal.
Understanding the Risk of Model Poisoning
Model poisoning is a subtle but increasingly significant threat in the AI landscape. It refers to the injection of harmful data, often unknown to the user, into a model’s training process or operational workflows, thereby skewing the model’s outputs, undermining its reliability, or creating new security threats. This risk is not bound by borders. Whether an AI model is developed in Hangzhou or Silicon Valley, the potential for poisoning exists during both the training phase and operational deployment.
Everyone should operate large AI models with the assumption that it may be poisoned. This risk increases over time as hackers, organized crime, and state actors invest heavily in leveraging AI as a new attack vector.
During training, models may encounter tainted data that is intentionally inserted or inadvertently included, which can distort their understanding and outputs. AI lab employees or external bad actors can also reach in and taint models at various points throughout their creation and deployment cycles. This risk is not unique to Chinese models. OpenAI, Google, and Anthropic must also navigate the murky waters of data integrity, ensuring that their unfathomably large training datasets, sourced from vast portions of the internet and from other AI models trained on similar data pools, remain as clean as possible.
These organizations must also make appropriate investments in staffing and processes to protect against insider threats. All major AI labs employ individuals who may be subject to immense pressure from state actors and organized crime to compromise models, as well as from hackers seeking access. No one today can credibly claim that their AI models are completely safe from poisoning.
The Human Element: A Universal Vulnerability
Beyond the data itself lies the human component. Employees of any nationality, whether working for a Chinese tech giant, a French AI lab, or a Silicon Valley behemoth, could potentially influence a model’s training.
Within these organizations, the presence of foreign nationals or employees with close family members abroad adds an extra layer of complexity. This reality raises questions about state actor influence and the potential for coercion or subversion. The risk is not theoretical. Pressure applied to family members abroad can become leverage.
However, these concerns should not be confined to any single nation or company. The AI industry is a global, interconnected field where talent crosses borders easily, and the risk of compromise exists everywhere. The fact that much of the most critical AI work is performed or led by academics who often lack experience with strong enterprise security culture further increases this risk. The high-flying academic labs have also only recently hired experienced cybersecurity leadership, now working fervently to remediate years of operating without appropriate enterprise-level security safeguards.
The Internet: A Double-Edged Sword
The internet is a vital resource for model training, but it is also a persistent source of poisoning risk in day-to-day model operation. AI models, when tied into agents, regardless of origin, are exposed to harmful or misleading content during normal operations such as tool calls, data retrieval, employee supplied data and other external data ingestion.
This means that the decentralized and open nature of the internet creates a reality where no model is inherently protected. Models developed in China and the United States alike can unknowingly ingest damaging or adversarial information as part of routine operation.
Mitigating Risks Through Control
When it comes to deploying AI models, the environment in which they operate plays a critical role in mitigating risk. Running models in controlled, in-house environments, regardless of their origin, provides a level of governance and visibility that external model provider APIs cannot match.
By self-managing execution environments, restricting network and agent access, and tightly controlling the data models can access, organizations can significantly reduce model poisoning risk and better ensure that AI systems behave as intended.
Control over where models run, what agents can access, data input sources, tools used by agents, and data output monitoring is a practical and effective defense.
A Call for Nuanced Trust
The risk of model poisoning is a universal challenge that transcends national borders and corporate identities. Focusing solely on where a model was developed misses the real issue. What matters is how models are trained, deployed, monitored, and constrained in production.
Trust in AI should be built on rigorous data scrutiny, strong security practices, and well-governed execution environments. By shifting the conversation away from model nationality and toward model operational safeguards, we can take a more realistic and effective approach to trusting AI in an increasingly interconnected world.
