AI Risk Assessment Frameworks & Methodologies

Key AI Risk Assessment Frameworks

NIST AI Risk Management Framework (AI RMF 1.0)

The National Institute of Standards and Technology's AI Risk Management Framework provides voluntary guidance for managing AI risks throughout the AI lifecycle.

Core Functions:

•Govern: Policies and processes for risk management

•Map: Context and risk identification

•Measure: Risk analysis and tracking

•Manage: Risk response and monitoring

OECD AI Principles

The Organisation for Economic Co-operation and Development established five principles for responsible AI:

1. AI should benefit people and the planet

2. AI systems should respect the rule of law and human rights

3. Transparency and responsible disclosure

4. Robustness, security, and safety

5. Accountability

Practical Risk Assessment Steps

1. System Scoping

Define the AI system boundaries, including inputs, processing, outputs, and affected stakeholders.

2. Threat Identification

Identify potential threats including bias, privacy violations, security vulnerabilities, and unintended consequences.

3. Impact Analysis

Evaluate the severity of potential harms across dimensions: physical safety, psychological well-being, financial impact, discrimination, and societal effects.

4. Likelihood Assessment

Determine the probability of identified risks materializing based on system design, deployment context, and historical evidence.

5. Risk Prioritization

Combine impact and likelihood to prioritize risks for treatment.

6. Mitigation Planning

Develop specific mitigation strategies for each prioritized risk.

7. Documentation & Review

Document all assessments and review periodically or when system changes occur.

Key Points