Assess risks of the generative Web
Structure risk assessment rather than relying on promises.
A framework, not a certificate
The NIST AI RMF and its generative AI profile provide categories and actions for risk management. They are voluntary resources, not automatic legal compliance or proof of reliability.
Identify the actual system
Specify who uses generative Web tools, for which task, with which models, data, permissions and consequences. A draft generator has a different risk profile from an agent executing sensitive operations.
Measure system behavior
Evaluate accuracy, missing information, privacy, transparency, accessibility, costs and escalation behavior. Test routine and adversarial cases, recording error rates and limitations.
Monitor the lifecycle
Model, source or protocol updates can change risks. Assign owners, schedule review and retain incident and remediation history.
Risks belong to actual systems
The NIST AI risk framework provides categories and practices for AI governance. It does not replace describing the actual use case: a draft tool using public material differs from an agent with financial or sensitive-data access. Tie risks to owners, mitigation measures and checks.
Living risk register
Build a register covering factual errors, disclosures, bias, dependencies, accessibility and action consequences. For each risk record assessed likelihood, potential harm, mitigation, owner and latest test outcome. Review the register after changing model, provider, corpus or protocol. A static document cannot reflect an evolving system.
Core steps
- 01Describe the actual use case
- 02Identify potential harms
- 03Test mitigations
- 04Schedule risk review
Three questions to ask
Who owns and approves the risk register?
Are changes and incidents logged?
Is a voluntary framework wrongly presented as certification?
Read the source documents
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