Human-Centred Risk Mitigation for AI-Mediated Information Manipulation: A SOCMINT Framework Based on Information Manipulation Sets

• URL: http://arxiv.org/abs/2606.09754v1
• Date: Mon, 08 Jun 2026 17:12:11 GMT
• Title: Human-Centred Risk Mitigation for AI-Mediated Information Manipulation: A SOCMINT Framework Based on Information Manipulation Sets
• Abstract summary: This paper proposes a SOCMINT framework based on Information Manipulation Sets (IMS)<n>Building on the VIGINUM/EEAS use of IMS in counter-FIMI analysis, the framework treats manipulation as a coherent process involving narratives, accounts, infrastructures, temporal patterns, cross-platform migration, synthetic amplification, and cognitive targeting.<n>The paper also proposes a tabletop evaluation protocol to assess decision quality, confidence calibration, and mitigation proportionality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-mediated information manipulation increasingly takes the form of social cyber attacks that target trust, attention, credibility, reputation, and decision-making rather than only technical infrastructures or isolated false contents. Existing defensive approaches often oscillate between incident-level analysis, which fragments campaigns into weak signals, and attribution-first analysis, which may delay mitigation until responsibility is established. This paper proposes a SOCMINT framework based on Information Manipulation Sets (IMS) as an intermediate operational unit between individual incidents and strategic attribution. Building on the VIGINUM/EEAS use of IMS in counter-FIMI analysis, the framework treats manipulation as a coherent process involving narratives, accounts, infrastructures, temporal patterns, cross-platform migration, synthetic amplification, and cognitive targeting. The proposed pipeline moves from signal detection and diagnostic triage to IMS hypothesis construction, confidence/severity assessment, mitigation selection, and iterative update. A compact scenario illustrates how IMS-based analysis captures what content-level and attribution-first approaches miss. The paper also proposes a tabletop evaluation protocol to assess decision quality, confidence calibration, and mitigation proportionality. The main implication is that human-centred risk mitigation requires not only better detection, but also structured reasoning under uncertainty, auditable decision-making, and safeguards against over-securitising legitimate dissent.

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