Artificial Intelligence Trust, Risk and Security Management (AI TRiSM)
AI TRiSM offers a structured, lifecycle-oriented risk taxonomy that APS agencies can cross-reference against existing governance frameworks.
Key points
- MIT AI Risk Repository spotlights the AI TRiSM framework covering trust, risk, and security management across AI lifecycles.
- Framework organises AI risks under bias, privacy, deepfakes, societal manipulation, autonomous weapons, and malicious use.
- This is a literature synthesis blog post - the underlying 2024 academic paper carries more analytical depth.
Implications for Australian agencies
- Consider APS AI governance teams could consider mapping AI TRiSM's risk taxonomy against the APS AI Policy's responsible use obligations to identify coverage gaps or useful framing.
- Monitor Agencies tracking international risk frameworks may want to monitor the MIT AI Risk Repository's ongoing series, which systematically catalogues frameworks potentially useful for comparative governance work.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Weekly digest, 16 February 2026
"Artificial Intelligence Trust, Risk and Security Management (AI TRiSM)"
Source: MIT AI Risk Repository – Blog
Published: 19 February 2026
URL: https://airisk.mit.edu/blog/ai-trism
The MIT AI Risk Repository's blog spotlights a 2024 academic paper by Habbal, Ali, and Abuzaraida reviewing the AI Trust, Risk, and Security Management (AI TRiSM) framework. The framework organises AI risks into three pillars - trust management (bias, privacy), risk management (societal manipulation, deepfakes, autonomous weapons), and security management (malicious use, insufficient controls) - and is designed to apply across the full AI system lifecycle, with particular focus on healthcare and finance sectors. The blog post is a brief summary pointing readers to the underlying publication rather than original analysis.
Implications for Australian agencies:
- [Consider] APS AI governance teams could consider mapping AI TRiSM's risk taxonomy against the APS AI Policy's responsible use obligations to identify coverage gaps or useful framing.
- [Monitor] Agencies tracking international risk frameworks may want to monitor the MIT AI Risk Repository's ongoing series, which systematically catalogues frameworks potentially useful for comparative governance work.
Retrieved from SIMS, 18 July 2026.