Data exfiltration
Data exfiltration is the unauthorized transfer of information from computers or devices. Two types of data exfiltration related to AI are:
- Exfiltration of the AI model
- Exfiltration of training data
Data Exfiltration of the AI Model: The unauthorized siphoning of information from an AI model. It involves stealing the model's architecture, weights, or other proprietary components. Attackers can exploit this to replicate or misuse the model for their purposes, potentially compromising its integrity and intellectual property.
Exfiltration of Training Data: Training data used to build an AI model is illicitly transferred or leaked. It involves unauthorized access to sensitive datasets, which can lead to privacy breaches, bias amplification, or even adversarial attacks. Protecting training data is crucial to prevent such exfiltration.
AI plays a pivotal role in both preventing and enabling data exfiltration. While AI can help detect and mitigate data breaches, it also provides attackers with advanced tools to steal sensitive information. This dual influence of AI creates a complex challenge for organizations aiming to protect their valuable data.
Data exfiltration can be mitigated by using good security hygiene: principle of these privilege, patching systems and keeping them up to date, labeling, and classifying data and adopting a zero trust architecture.