---
id: frameworks-index
title: Compact framework index
token_budget: ~1500
version: "1.1"
---

# NIST AI RMF 1.0

## GOVERN (cross-cutting)
- GOVERN 1: Policies, processes, procedures for AI risk management
- GOVERN 2: Accountability structures and roles
- GOVERN 3: Workforce diversity in AI risk teams
- GOVERN 4: Organizational risk culture
- GOVERN 5: External stakeholder engagement processes
- GOVERN 6: Third-party AI and supply chain policies

## MAP (context and risk identification)
- MAP 1: Context establishment (purpose, users, setting, laws)
- MAP 2: AI system categorization (tasks, methods, knowledge limits, data)
- MAP 3: Benefits and costs analysis (capabilities, benchmarks, oversight)
- MAP 4: Third-party component risk mapping
- MAP 5: Impact characterization (likelihood, magnitude)

## MEASURE (evaluation and testing)
- MEASURE 1: Methods and metrics selection
- MEASURE 2: Trustworthiness evaluation:
  - 2.1: Test set and tool documentation
  - 2.2: Human subject evaluation requirements
  - 2.3: Performance in deployment conditions
  - 2.4: Functionality monitoring in production
  - 2.5: Validity, reliability, generalizability limits
  - 2.6: Safety evaluation (fail-safe, monitoring, response)
  - 2.7: Security and resilience evaluation
  - 2.8: Transparency and accountability evaluation
  - 2.9: Explainability and interpretability
  - 2.10: Privacy risk evaluation
  - 2.11: Fairness and bias evaluation
  - 2.12: Environmental impact
  - 2.13: TEVV effectiveness
- MEASURE 3: Risk tracking over time
- MEASURE 4: Measurement effectiveness feedback

## MANAGE (response and monitoring)
- MANAGE 1: Risk prioritization and response planning
- MANAGE 2: Benefit maximization / harm minimization
- MANAGE 3: Third-party risk management
- MANAGE 4: Post-deployment monitoring, incident response, change management

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# OWASP Top 10 for LLM Applications (2025)

- LLM01: Prompt Injection — manipulating inputs to override instructions
- LLM02: Sensitive Information Disclosure — leaking PII, secrets, proprietary data
- LLM03: Supply Chain — third-party model, data, and plugin risks
- LLM04: Data and Model Poisoning — tampered training/RAG data
- LLM05: Improper Output Handling — unsanitized output to downstream systems
- LLM06: Excessive Agency — too much LLM autonomy, insufficient oversight
- LLM07: System Prompt Leakage — extraction of internal instructions (new 2025)
- LLM08: Vector and Embedding Weaknesses — RAG/embedding vulnerabilities (new 2025)
- LLM09: Misinformation — hallucinations, false facts, unverified claims
- LLM10: Unbounded Consumption — DoS, cost explosion, resource abuse

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# ISO/IEC 42001:2023

## Clauses
- Clause 4: Context of the organization
- Clause 5: Leadership commitment and AI policy
- Clause 6: Planning (risk assessment, AI impact assessment, objectives)
- Clause 7: Support (resources, competence, documentation)
- Clause 8: Operation (controls, risk treatment, AI system impact assessment)
- Clause 9: Performance evaluation (monitoring, audit, management review)
- Clause 10: Improvement (corrective action, continual improvement)

## Annexes
- Annex A: Control objectives (A.2-A.10: policies, organization, resources, impact, lifecycle, data, transparency, use, third-party)
- Annex B: Implementation guidance
- Annex C: AI-specific objectives and risk sources
- Annex D: Cross-industry guidance
