Why Model Poisoning Requires A New Approach To AI Security

Model poisoning is a growing AI security threat in which attackers manipulate training data, model updates or other parts of the AI supply chain to subtly alter a model’s behavior while allowing it to appear trustworthy. Unlike traditional cyberattacks that focus on gaining access, these attacks undermine the reliability of AI systems and can lead to inaccurate, biased or harmful outputs. Learn how organizations can reduce this risk through stronger governance, continuous monitoring and layered security controls throughout the AI lifecycle.

What this resource covers:

• How model poisoning attacks target AI systems
• Risks associated with manipulated training data and model updates
• The impact of compromised AI reliability and trust
• Governance and monitoring strategies for AI security
• Layered security controls across the AI lifecycle