The report also defines three broad classes of adversarial AI: adversarial machine learning attacks, generative AI system attacks, and MLOps and software supply chain attacks. It suggests four ways to defend against an adversarial AI attack: making red teaming and risk assessment part of the organization’s DNA, staying current and adopting the defensive framework for AI that works best for the organization, reducing the threat of synthetic data-based attacks by integrating biometric modalities and passwordless authentication techniques into every identity access management system, and auditing verification systems randomly and often, keeping access privileges current.
Key takeaways:
- Despite 97% of IT leaders acknowledging the importance of securing AI and safeguarding systems, only 61% are confident they’ll get the necessary funding.
- Adversarial AI aims to mislead AI and machine learning systems, making them ineffective for their intended use cases. There are three broad classes of adversarial AI: adversarial machine learning attacks, generative AI system attacks, and MLOps and software supply chain attacks.
- Organizations can defend against adversarial AI attacks by making red teaming and risk assessment part of their routine, staying current with defensive frameworks, integrating biometric modalities and passwordless authentication techniques, and frequently auditing verification systems.
- Synthetic data is increasingly being used to impersonate identities and gain access to source code and model repositories, making it one of the most challenging threats to contain.