Data poisoning in the age of AI

Educating personnel about the risks of data poisoning and fostering collaboration with industry partners and cybersecurity experts can enhance an organization's ability to respond to and mitigate these threats.

- KHUSHBU JAIN

Data poisoning involves intentionally manipulating the training data used by machine learning models to influence their behaviour in malicious ways.

MUMBAI 'n the modern era of artificial intelligence, I data has become the backbone of countless applications, ranging from language models and image recognition systems to autonomous vehicles and healthcare diagnostics. As AI systems increasingly rely on vast amounts of data to learn and make decisions, data poisoning has emerged as a significant threat that can undermine the integrity, reliability and security of these systems.

WHAT IS DATA POISONING? Data poisoning involves intentionally manipulating the training data used by machine learning models to influence their behaviour in malicious ways. By inserting carefully crafted data points into the training dataset, a person can aim to compromise the model's performance, cause it to make incorrect predictions or introduce vulnerabilities that can be exploited later.

In the age of AI, several factors have amplified the risks associated with data poisoning: 1. MASSIVE SCALE OF DATA COLLECTION: 1.1. Public Data Sources: Modern AI models, especially large language models like GPT, are trained on enormous datasets scraped from the internet. This vast and often uncurated data opens avenues for attackers to insert poisoned data subtly.

1.2. Automated Data Pipelines: The automation of data collection and processing can overlook anomalies or malicious inputs, making the system more susceptible to poisoning.