Best Practices
AI Risk Assessment
Evaluating and managing AI risks systematically.
Risk assessment is foundational to responsible AI deployment. It helps identify potential harms before they occur and ensures appropriate safeguards.
Risk Categories
- Technical: Hallucinations, failures, security vulnerabilities
- Compliance: Regulatory violations, audit failures
- Ethical: Bias, fairness, transparency issues
- Business: Reputation damage, liability, costs
Assessment Framework
- 1. Scope: Define the AI use case and stakeholders
- 2. Identify: Catalog potential risks and harms
- 3. Analyze: Assess likelihood and impact
- 4. Mitigate: Define controls (monitoring, guardrails)
- 5. Monitor: Ongoing risk tracking
- 6. Review: Regular reassessment
Risk Tiers
- High: Customer-facing, regulated, high-stakes decisions
- Medium: Internal tools, moderate impact
- Low: Non-critical, easily reversible
What is AI risk assessment?
AI risk assessment is the process of identifying, analyzing, and evaluating risks associated with AI systems. This includes technical risks (hallucinations, failures), compliance risks (regulatory violations), and business risks (reputation, liability).
How do I assess risk?
Use a structured framework: 1) Identify the AI use case and stakeholders, 2) Catalog potential risks and harms, 3) Assess likelihood and impact, 4) Define mitigations (monitoring, guardrails), 5) Document and review regularly.
Risk classification and monitoring
8 detection types with industry benchmarks.
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