What is Adversarial Machine Learning?
Adversarial machine learning is the practice of designing, testing, or exploiting AI models so they perform incorrectly through malicious inputs, poisoned data, or other manipulation. It matters for compliance because it directly affects model security, safety, and the evidentiary basis for claims that an AI system has been tested and controlled.
In Depth
In practice, adversarial machine learning covers attacks such as prompt injection, evasion, data poisoning, model extraction, and membership inference, depending on the model type and deployment context. Compliance teams need to understand these risks because they can undermine output integrity, expose personal or confidential data, and create failures in systems used for regulated decisions, customer-facing services, or safety-critical operations.
This term is relevant to security and governance controls that require organizations to assess abuse cases, test model robustness, and implement monitoring, incident response, and access restrictions. It is commonly addressed through ISO 27001, ISO/IEC 42001, NIST AI RMF, SOC 2 + AI, and AI governance expectations in the EU AI Act, especially where organizations must demonstrate secure design, risk management, and post-deployment oversight.
Related Frameworks
Related Topics
Related Terms
Weekly digest — coming soon
Leave your email to get the first issue when it ships. Free, no account required.
We use your email only for the digest. Privacy policy