As cyber threat actors increasingly target AI systems, the data pipelines used to train these systems will similarly come under attack as a potentially easier or more covert route to poisoning and altering the operation of these systems. This excerpt highlights technical methods for implementing stealthful training data manipulation through targeted adjustments in pipeline automation.

Rapid industry adoption of chatbot technology is driving efficiency and improving customer service but also expanding cyber risk. Implementation of conversational AI solutions have outpaced many cybersecurity controls and may leave organizations vulnerable to cyber-attack. This series will improve awareness of potential chatbot exploitation methods, map the expanded attack surface, and provide guidelines for improving the technical and procedural controls required to address these threats.

Attacks against AI-based systems have only begun, but these systems increasingly have the attributes that attract cyber threat actors. Business criticality, processing of sensitive information, control over financial transactions and the power to disrupt are key elements that motivate attackers. As attackers pivot their focus to intelligent systems, how well we have anticipated methods of attack and corresponding defense techniques will be the difference between a narrow miss and a significant compromise.