> ## Documentation Index > Fetch the complete documentation index at: https://hydroxai.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # NIST AI RMF > The NIST AI Risk Management Framework — a comprehensive approach to managing risks in AI systems. ## Overview The **NIST AI Risk Management Framework (AI RMF 1.0)**, published by the National Institute of Standards and Technology in January 2023, provides a voluntary framework for managing risks throughout the AI lifecycle. It is designed to help organizations design, develop, deploy, and use AI systems in ways that are trustworthy and responsible. NIST AI RMF is becoming the de facto standard for AI governance in the United States, referenced by federal procurement policies and increasingly adopted by private sector organizations. ## Core functions The framework is organized around four core functions: ### Govern Establish and maintain policies, processes, and organizational structures for AI risk management. | Activity | Description | | ------------------------ | ----------------------------------------------------------------------------- | | Policies & procedures | Define AI governance policies, risk tolerances, and escalation procedures | | Roles & responsibilities | Assign accountable individuals for AI risk management across the organization | | Culture | Foster an organizational culture that prioritizes trustworthy AI | | Third-party management | Establish oversight for AI components from external providers | ### Map Identify and contextualize risks associated with AI systems. | Activity | Description | | -------------------------- | ---------------------------------------------------------------------------------- | | Context establishment | Document the intended purpose, scope, and operational environment of the AI system | | Stakeholder identification | Identify all parties affected by the AI system's outputs and decisions | | Risk identification | Catalog potential risks including bias, safety, security, and privacy concerns | | Benefits & costs | Assess the tradeoffs between AI deployment benefits and potential harms | ### Measure Analyze and assess identified AI risks using quantitative and qualitative methods. | Activity | Description | | -------------------- | --------------------------------------------------------------------------- | | Risk assessment | Evaluate likelihood and impact of identified risks | | Testing & evaluation | Conduct systematic testing including red-teaming and adversarial evaluation | | Metrics & monitoring | Define key risk indicators and establish ongoing monitoring | | Bias measurement | Quantify bias across demographics, use cases, and deployment contexts | ### Manage Prioritize, respond to, and monitor AI risks on an ongoing basis. | Activity | Description | | --------------------- | ---------------------------------------------------------- | | Risk prioritization | Rank risks by severity and allocate mitigation resources | | Risk response | Implement controls, mitigations, and safeguards | | Continuous monitoring | Track risk levels over time with dashboards and alerts | | Incident response | Establish procedures for responding to AI safety incidents | ## Trustworthy AI characteristics NIST AI RMF defines seven characteristics of trustworthy AI: 1. **Valid & reliable** — The AI system performs as intended with consistent results 2. **Safe** — The system does not endanger life, health, property, or the environment 3. **Secure & resilient** — Protected against adversarial attacks and graceful under failure 4. **Accountable & transparent** — Decisions are explainable and responsibility is clear 5. **Explainable & interpretable** — Outputs can be understood by stakeholders 6. **Privacy-enhanced** — Personal data is protected throughout the AI lifecycle 7. **Fair with harmful bias managed** — Bias is identified, measured, and mitigated ## How Know Your AI maps to NIST AI RMF | NIST Function | Know Your AI Feature | | ------------- | ----------------------------------------------------------------- | | **Govern** | Workspace roles, access controls, compliance dashboards | | **Map** | Product configuration, dataset selection, risk categorization | | **Measure** | Evaluation with LLM-as-Judge, security scoring, bias datasets | | **Manage** | Monitoring dashboards, firewall, continuous evaluation scheduling | | Trustworthy Characteristic | Know Your AI Coverage | | --------------------------- | ------------------------------------------------------- | | Valid & reliable | Evaluation scoring, quality metrics | | Safe | Safety datasets, harmful content detection | | Secure & resilient | Red-team testing, jailbreak & prompt injection datasets | | Accountable & transparent | Evaluation evidence, tracing, audit logs | | Explainable & interpretable | Per-prompt judge analysis with reasoning | | Privacy-enhanced | CCPA/CPRA compliance analysis, PII leakage detection | | Fair with bias managed | Bias detection datasets | ## Resources * [NIST AI RMF 1.0 (PDF)](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf) * [NIST AI RMF Playbook](https://airc.nist.gov/AI_RMF_Playbook) * [NIST Trustworthy & Responsible AI Resource Center](https://airc.nist.gov/) How Know Your AI automates regulatory compliance. Run NIST-aligned evaluations on your AI systems.