Artificial intelligence governance is often reduced to technical compliance, obscuring its core function: the governance of decisions that carry institutional, legal, and social consequences. This article argues that effective AI governance is defined not by conformity to technical requirements, but by the legitimacy of AI-assisted decisions within public and organisational frameworks.
By integrating insights from AI governance, digital forensics, and decision accountability, the analysis shows how compliance-driven approaches can coexist with governance failure when responsibility becomes diffused across systems, data pipelines, and human actors. The article reframes AI governance as a decision-centred practice, where explainability, traceability, contestability, and proportionality are essential to preserving authority and trust.
Drawing on contemporary governance standards and policy research, the article highlights the distinction between governing of AI systems and governing with AI systems, demonstrating why legitimacy depends on reconstructible decision pathways rather than technical assurance alone. It concludes that governing AI means governing how institutions decide, ensuring accountability, oversight, and defensible reasoning in AI-assisted environments.
Algorithmic systems have become central actors in contemporary pathways of ideological radicalisation, transforming individual vulnerability into systemic governance risk. This article examines how AI-driven recommendation and amplification mechanisms accelerate extremist narratives, obscure responsibility, and compress escalation timelines without explicit coordination or intent.
By integrating perspectives from cybersecurity, digital forensics, and AI governance, the analysis shows how data exploitation, platform optimisation logic, and algorithmic feedback loops reshape radicalisation dynamics beyond traditional models of recruitment and indoctrination. Extremism emerges not only as a content problem, but as a consequence of structural amplification embedded in AI systems.
The article argues that effective prevention requires governance frameworks capable of addressing algorithmic responsibility, forensic traceability, and institutional oversight across digital ecosystems. Without such governance, radicalisation operates below legal thresholds while producing real-world harm. The study concludes that governing AI is essential to interrupt the silent acceleration of extremism and restore accountability where algorithms currently shape behaviour without consequence.
The second part of this study examines the ideological ambiguities and policy implications of the Robinson case, framing it as an instance of “nihilistic-memetic extremism”. This form of radicalisation, where violence acquires ludic and performative traits, challenges traditional left/right classifications and calls for new conceptual frameworks. The article advances original reflections on the role of digital culture in shaping hybrid extremist ideologies and provides concrete recommendations for prevention: enhanced monitoring of online platforms, education in digital resilience, creation of specialised units, and international legal cooperation. Finally, it evaluates the preventive potential of Artificial Intelligence in detecting early signs of radicalisation within dark web ecosystems, while addressing the ethical dilemmas of algorithmic surveillance.
This article explores the assassination of conservative activist Charlie Kirk, placing the event within the broader context of contemporary political extremism and digital culture. It analyses the personal trajectory of the alleged perpetrator, Tyler Robinson, highlighting his self-radicalisation through online subcultures, meme-driven communities, and possible incursions into the dark web. Far from being a traditional militant, Robinson emerges as a hybrid figure shaped by digital echo chambers, toxic irony, and the fusion of ideology with internet performativity. The case exemplifies how online environments can catalyse violent dispositions, transforming personal grievances into lethal political acts.
Between 23 and 28 August 2025, a cluster of severe cyber incidents highlighted the accelerating tempo and complexity of today’s threat landscape. Within a few days, a Citrix NetScaler zero-day was exploited in the wild, DaVita confirmed the exposure of nearly 2.7 million patients’ data, and major service providers such as iiNet and Colt reported large-scale breaches. Public administrations, including the State of Nevada, also suffered ransomware-style attacks that disrupted essential services. At the same time, researchers disclosed a new denial-of-service vector in the HTTP/2 protocol (“MadeYouReset”), Apple patched an Image I/O zero-day actively abused in targeted campaigns, and novel attacker tradecraft emerged, with adversaries patching exploited flaws to conceal intrusions. Even unverified underground data dumps, such as the alleged 15.8 million PayPal credentials, contributed to heightened uncertainty and risk. Taken together, these events underscore a structural shift: breaches are no longer isolated shocks but overlapping waves, compressing detection and response windows. The key lesson from late August is that anticipation, early warning, and proactive intelligence are no longer optional: they are the only way to prevent a week of incidents from turning into a season of crises.
The article explores how to design and implement a cyber intelligence early warning system, conceived as a “radar” capable of detecting weak threat signals before they materialise. By mapping critical assets, integrating diverse sources (OSINT, dark web, internal telemetry, and commercial feeds) and applying risk prioritisation models such as FAIR, the system translates raw information into targeted alerts with high operational impact. A logical architecture is outlined, combining data collection, advanced analysis, continuous feedback loops for constant refinement, and compliance with key regulatory frameworks (GDPR, NIS2, and the Budapest Convention). The article also highlights the role of key metrics (MTTD, MTTR) and the sharing of intelligence with trusted communities, ISACs, and CERTs to amplify early warning capabilities and strengthen organisational resilience.
Open Source Intelligence (OSINT) has evolved into a critical pillar of proactive cyber defence, enabling organisations to detect, analyse, and respond to emerging threats before they materialise. By leveraging publicly available information from diverse digital environments (including the dark web, social media, and technical repositories) predictive OSINT empowers cyber intelligence teams to anticipate attack patterns, identify vulnerabilities, and mitigate risks in real time. This approach not only strengthens security postures but also provides a decisive competitive advantage, allowing entities to stay ahead of adversaries in an increasingly complex and volatile threat landscape.
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