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.
Practical example
An assistant moves from drafting an email to sending it automatically. Reassess the new action and who can authorize it.
What to check
Record the affected people, failure consequences, controls, residual uncertainty, and accountable owner.
Limits and tradeoffs
A model risk score alone cannot represent the full context of an application or determine legal obligations.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.