Harmful Use Assessment

Harmful use assessment is the process of evaluating whether an AI system could be misused to cause damage, injury, or illegal activity, and identifying safeguards needed to prevent such misuse. For enterprise governance, this assessment helps organizations anticipate potential harms from their AI deployments, understand liability exposure, and implement appropriate controls before incidents occur. This capability is essential for compliance with emerging AI regulations and for maintaining stakeholder trust.

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ResearchUS2026-07-22

Berkeley CLTC Case Studies Expose Documentation and Accountability Gaps at AI Release Decision Points

The UC Berkeley Center for Long-Term Cybersecurity has published 'Decision Points in AI Governance: Three Case Studies,' examining how organizations handle governance at critical junctures in the AI model lifecycle. The research highlights structured documentation, harmful-use deliberation, and pre- and post-release communication as the controls most commonly absent or underdeveloped. Enterprise compliance teams can use the findings to benchmark and strengthen their own release governance and accountability frameworks.

release governance documentation standards model deployment accountability UC Berkeley CLTC pre-deployment review harmful use assessment