GoKawiilF-Secure publishes taxonomy of 26 AI harms to consumers with 24 proposed defenses
F-Secure released an AI Harms Taxonomy, developed by researcher Dr. Megan Squire, that catalogs 26 distinct ways AI systems can harm consumers and traces how each harm travels through intermediary layers to reach a person. The framework spans five relationship types and 19 intermediary layers, and proposes 24 defenses aimed at specific points in that chain, covering risks such as voice and video impersonation and AI agents making damaging financial decisions.
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By mapping harms to the specific systems and parties that enable them, F-Secure's framework could give companies and regulators a more actionable way to assign responsibility for AI failures than existing lists of hypothetical risks. Dr. Squire frames this as a call for AI developers to anticipate failure modes before deployment, suggesting the taxonomy is meant to inform how safeguards are built into AI products rather than added after harm occurs. Its real-world influence will depend on whether companies adopt the defenses it proposes.
- F-Secure's taxonomy identifies 26 distinct consumer-facing AI harms.
- It maps five relationship types and 19 intermediary layers between threats and victims.
- The framework pairs harms with 24 proposed defenses targeting specific points of failure.
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Published there as: “F-Secure Identifies 26 Ways AI Can Harm Consumers, and Where to Stop Them”
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