2023
Government Guidance
Artificial Intelligence Risk Management Framework 1.0
The framework is organised around Govern, Map, Measure and Manage.
Current, revision underway
National Institute of Standards and Technology
- AI Assurance
Evidence note
NIST AI 600-1: Generative Artificial Intelligence Profile
Government Guidance2024National Institute of Standards and Technology
What does it cover?
A Generative AI profile extending the NIST AI Risk Management Framework with risks and actions particularly relevant to generative AI systems.
Key points
- 1The profile addresses risks including confabulation, data privacy, information integrity, information security and component integration.
- 2It recommends evaluating system performance under conditions similar to deployment.
- 3It recommends reassessing risk when systems are adapted to materially new domains or contexts.
- 4It emphasises ongoing testing, monitoring, security evaluation and incident management.
Why it matters. Lornets interpretation.
Generic model benchmarks are insufficient evidence for a production AI application. Assurance needs to consider the actual deployment context, integrations, monitoring and change over time.
This is the Lornets reading of the source, not a finding of the source itself.
What it does not establish
- 1The profile does not certify individual applications.
- 2It is risk-management guidance rather than a guarantee of safe system behaviour.
- 3Applicable depth should remain proportionate to context.
Source
- Organisation
- National Institute of Standards and Technology
- Evidence type
- Government Guidance
- Published
- 2024-07
- Status
- Current
Relevant Lornets framework areas
Framework domains
- AI Assurance
- Security & Access Control
- Reliability & Recoverability
- Data & Privacy Engineering
- Observability & Operations
Related evidence
Last verified 2026-08-11
2025
Peer-Reviewed Research
An Empirical Study of Production Incidents in Generative AI Cloud Services
49.8% of the studied GenAI incidents were primarily classified as performance degradation.
Microsoft, ISSRE 2025
- AI Assurance
- Reliability & Operations
2022
Government Guidance
NIST Secure Software Development Framework v1.1
SSDF organises secure-development practices into a structured set of outcomes rather than prescribing one development methodology.
Current, revision underway
National Institute of Standards and Technology, SP 800-218, SSDF v1.1
- Security & Supply Chain
- Software Quality & Maintainability