AI isn’t just a buzzword anymore—it’s woven into the fabric of our daily lives. From chatbots handling customer service to self-driving cars and AI-generated content, this technology fuels the modern economy. In fact, 73% of people worldwide now claim to understand what AI is. However, as AI’s reach grows, so does the risk of misuse, and trust in these systems is more critical than ever. If mistrust seeps into AI, the repercussions could ripple through both our economy and society at large.

Despite the current AI boom, seasoned experts in the field, who have seen the hype cycle come and go, remain skeptical about whether this time represents AI’s true moment of takeoff. In the U.S., growing concerns reflect this caution. In 2023, more than half of adults—52%—said they were more worried than excited about the growing presence of AI in their lives, a significant jump from 37% in 2021. [1]

While AI continues to evolve at breakneck speed, cybercriminals are already adapting to its advancements, searching for new vulnerabilities to exploit. One emerging threat is data poisoning.

What is Data Poisoning? Data Poisoning is a covert attack where hackers deliberately sabotage AI training datasets. The goal? To degrade a model’s performance or inject specific weaknesses that can be exploited later. These attacks can lead to AI systems making faulty decisions, displaying bias, or failing completely.

We’ve already seen how dangerous data poisoning can be in real-world scenarios:

In one large-scale attack, cybercriminals bombarded a popular email provider’s spam filter with millions of manipulated emails. By distorting the AI’s spam detection algorithm, they allowed malicious emails to bypass security, delivering malware and other threats right to users’ inboxes.

Another case involved a well-known social media chatbot. Users launched a coordinated effort to flood the bot with offensive and biased content, causing it to generate inappropriate and harmful responses.

Data poisoning attacks range from simple to highly sophisticated. In our latest report, Building Trustworthy AI: Contending with Data Poisoning, we identified nine distinct types of attacks and offered actionable steps to safeguard against them.

Today’s AI models are trained on vast datasets, often automatically pulled from the internet. This data is crucial for advancing AI, but it’s also a vulnerability. Poisoning as little as 0.001% of an AI dataset can lead to significant, targeted errors in a model’s behavior.

As AI development accelerates, shortening training cycles, these poisoning attempts become easier to execute. The risks are real: from sabotaging autonomous vehicle safety systems to manipulating financial algorithms, successful attacks could result in anything from financial losses to threats to human life.

As AI integrates more deeply into our everyday existence and critical infrastructure, the stakes only grow higher. And as the industry shifts toward smaller, more specialized models, the attack surface expands.

For AI organizations pushing the limits of innovation, success means not just advancing technology but also safeguarding it. They must navigate a constantly evolving regulatory landscape, combat platform misuse, maintain user trust, and defend against cyber threats. Nisos empowers leading AI innovators by unmasking threats and exposing the ecosystems that seek to undermine their work. As an extension of your team, we help ensure that your groundbreaking innovations remain secure in an increasingly competitive and dangerous market.