CAISI Issues Request for Information About Securing AI Agent Systems
US voluntary guidelines on agentic AI security will likely shape international norms - relevant to Australian agencies beginning to deploy or govern AI agent systems.
Key points
- NIST's CAISI has issued an RFI on securing AI agent systems, with submissions closing 9 March 2026.
- The RFI targets risks unique to agentic AI: prompt injection, data poisoning, misaligned objectives, and specification gaming.
- Outputs will inform voluntary US guidelines - a likely reference point for Australian agentic AI governance work.
Implications for Australian agencies
- Monitor Agencies with AI strategy or governance functions may want to monitor CAISI's published outputs from this RFI, as resulting voluntary guidelines are likely to be referenced in Australian agentic AI governance discussions.
- Consider Policy and security teams deploying or evaluating AI agent systems could consider whether the RFI's risk taxonomy - prompt injection, data poisoning, misaligned objectives - maps usefully onto existing internal risk assessment frameworks.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Weekly digest, 12 January 2026
"CAISI Issues Request for Information About Securing AI Agent Systems"
Source: NIST – AI News (topic 2753736)
Published: 12 January 2026
URL: https://www.nist.gov/news-events/news/2026/01/caisi-issues-request-information-about-securing-ai-agent-systems
NIST's Center for AI Standards and Innovation (CAISI) has published a Request for Information seeking input from industry, academia, and security researchers on the secure development and deployment of AI agent systems. The RFI focuses on security risks distinct to agentic AI - including indirect prompt injection, data poisoning, specification gaming, and misaligned autonomous actions - rather than general software vulnerabilities. Responses will inform future voluntary guidelines and CAISI's ongoing research. The comment period closes 9 March 2026. Australian agencies exploring agentic AI use cases or developing related governance frameworks may find the resulting guidance directly applicable.
Implications for Australian agencies:
- [Monitor] Agencies with AI strategy or governance functions may want to monitor CAISI's published outputs from this RFI, as resulting voluntary guidelines are likely to be referenced in Australian agentic AI governance discussions.
- [Consider] Policy and security teams deploying or evaluating AI agent systems could consider whether the RFI's risk taxonomy - prompt injection, data poisoning, misaligned objectives - maps usefully onto existing internal risk assessment frameworks.
Retrieved from SIMS, 18 July 2026.