<p>This article develops a context-sensitive multi-level harm–threat ethical framework for assessing the permissibility of artificial intelligence (AI) in intelligence operations, with particular focus on harms to privacy and autonomy. AI offers unprecedented capabilities in data collection, analysis, and predictive modeling, enabling more efficient and effective detection and prevention of threats. At the same time, its use in intelligence contexts intensifies the scale, depth, and persistence of intrusions, while reshaping how intelligence operatives understand their roles, judgements, and reliance on algorithmically generated information. The ethical challenge, therefore, is not whether intelligence-AI causes harm, but how such harm can be morally assessed, constrained, and justified throughout the process. This paper addresses this challenge by developing a multi-level harm–threat framework that evaluates ethical permissibility by systematically linking the degree of harm imposed to the severity, proximity, evidentiary strength, and target liability of the anticipated threat. Embedded within the intelligence cycle, the framework captures how AI-driven collection, processing, analysis, and implementation progressively intensify intrusion through profiling and prediction. The paper’s contribution lies in translating normative ethical reasoning into an operational justificatory logic that guides intelligence decision-making without normalizing ambient or disproportionate surveillance.</p>