A responsibility gap is the point where no one can clearly be held accountable for an automated outcome, even though many people helped create it. In AI and other complex systems, the model builder, data provider, platform operator, and deploying organization may all influence the result. When something goes wrong, blame can become fragmented, and the affected user may have no clear path to challenge the decision.
In cyber security, this matters because automated tools increasingly make or shape security decisions, from alerts and triage to access control and fraud checks. Attackers can exploit weak ownership by hiding behind opaque pipelines, while defenders can miss errors if no team owns logging, review, and override rights. Good practice closes the gap with clear accountability, audit logs, human oversight, escalation procedures, and contractual responsibility for vendors and operators.