Overview of Security Risks Associated with AI Systems
General map of AI security risks
AI systems introduce a combination of risks ranging from the purely technical to the organizational: attacks aimed at the model, such as input manipulation or theft of its internal logic; data-derived risks, such as poisoning or sensitive information leakage; and governance risks, such as using AI without adequate oversight or without traceability of decisions. Added to these are third-party risks, since many organizations don't train their own models but integrate services from external providers whose behavior and security controls aren't always transparent. Understanding this landscape holistically, instead of treating it only as a technical problem for data teams, is what makes it possible to prioritize investments and prevent the accelerated adoption of AI across different business areas from creating security blind spots that go unidentified until an incident occurs.