Human-Centred Risk Mitigation for AI-Mediated Information Manipulation: A SOCMINT Framework Based on Information Manipulation Sets
2026-06-08 • Computers and Society
Computers and SocietyCryptography and SecuritySocial and Information Networks
AI summaryⓘ
The authors explain that AI-driven attacks now focus on manipulating people's trust and decision-making rather than just hacking systems or spreading false information. They propose a new way to analyze these attacks using something called Information Manipulation Sets (IMS), which look at patterns and behaviors across different platforms over time. This method helps spot attacks earlier and respond better than current approaches that either look at isolated incidents or wait to find who's responsible. The authors also suggest a way to test how well these responses work, emphasizing the need for careful, human-focused decision-making that balances security with protecting free speech.
AI-mediated information manipulationSocial cyber attacksInformation Manipulation Sets (IMS)SOCMINTCounter-FIMINarratives and cognitive targetingSignal detectionMitigation strategiesAttributionDecision-making under uncertainty
Authors
Antonio Scala
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.