AI Radar tracks publicly disclosed AI incidents, investigations, enforcement actions, and material failures connected with cybersecurity, fraud, financial crime, privacy, and governance. Its purpose is to provide a clear, practical view of how AI-related risk manifests in real cases, from deepfake-enabled impersonation and synthetic identity abuse to data leakage, malicious model use, and failures in oversight.

The radar brings together key information on each case, including the date, the entity involved, the core issue, the main public findings, the cause of the failure or violation, and the event narrative. Where relevant, it also captures the operational impact, regulatory dimension, and source material. By presenting these cases in one place, AI Radar helps legal, compliance, AML, fraud, privacy, security, and risk teams understand which control gaps most often lead to public exposure, regulatory scrutiny, customer harm, financial loss, or reputational damage.

More than a list of incidents, AI Radar is designed as a working governance and risk resource. It shows how organizations and regulators respond to issues such as deepfake fraud, phishing, AI-assisted social engineering, synthetic identity abuse, model misuse, insecure deployment, data leakage, inadequate monitoring, poor human oversight, and third-party failures. This makes it easier to translate public incidents into practical lessons for internal controls, AI governance, fraud prevention, AML monitoring, vendor management, and enterprise risk management.