An AI risk assessment identifies and evaluates the risks created by an AI system or AI use case, including data exposure, security threats like prompt injection, inaccurate or biased outputs, legal and regulatory issues, and vendor dependencies, then defines controls proportionate to the impact. Assessing AI before deployment, and again when it changes, helps organizations adopt AI confidently and demonstrates responsible governance to customers and regulators.
Key Takeaways
- Start with an inventory of AI use cases, including third-party AI features.
- Classify each use case by impact on people, finances and operations.
- Assess data, security, accuracy, fairness, transparency and vendor risks.
- Match controls to risk level: higher-impact uses need stronger oversight.
- Reassess when models, data or use cases change.
Step 1: Inventory AI Use
List internal AI applications, vendor AI features, and employee AI tools (including shadow AI). Record owner, purpose, users, data used and model provider.
Step 2: Classify Impact
| Impact level | Examples | Oversight |
|---|---|---|
| Low | Drafting internal emails, summarizing public content | Policy and training |
| Medium | Internal knowledge assistants, ticket triage | Testing, monitoring, owner review |
| High | Customer-facing advice, hiring, credit, health or legal decisions | Formal assessment, human oversight, ongoing evaluation |
Step 3: Assess Key Risk Areas
Data and privacy
- What personal or confidential data does the system use?
- Where is it processed and stored? Is it used for training?
- Do permissions limit what users can retrieve?
Security
- Prompt injection and jailbreak resistance.
- Data exfiltration through outputs or connected tools.
- Agent identities and permissions.
Accuracy and reliability
- Is output grounded in trusted sources?
- How are errors detected and corrected?
Fairness and transparency
- Could outputs disadvantage groups of people?
- Do users know they are interacting with AI?
Legal and vendor
- Contract terms, data processing agreements, IP ownership.
- Applicable regulations in the US, UAE and other markets.
Step 4: Define Controls
- Data minimization, labels and DLP.
- Secure hosting such as Azure OpenAI with private networking.
- Grounding with RAG and citation requirements.
- Human review for high-impact outputs.
- Logging, monitoring and incident procedures.
- Red-teaming and evaluation test sets.
Step 5: Document and Review
Record the assessment, risk ratings, controls, residual risk and approver. Align with NIST AI RMF or ISO 42001 and review at least annually.
How Skytech Enterprise Helps
Our AI risk and readiness assessment evaluates AI use cases and delivers prioritized controls, supported by AI governance programs. Request an AI risk assessment.
Frequently Asked Questions
What is an AI risk assessment?
It is a structured evaluation of the potential harms and risks of an AI system or use case and the controls needed to manage them.
When should an AI risk assessment be done?
Before deployment, when the model, data or use case changes significantly, and periodically for high-impact systems.
What is prompt injection?
Prompt injection is an attack where malicious instructions hidden in input or content manipulate an AI system into ignoring its rules or leaking data.
Do third-party AI tools need assessment?
Yes. Vendor AI features can process your data and affect decisions, so they should be assessed proportionately.
Which framework should guide AI risk assessment?
The NIST AI RMF is a practical starting point; ISO/IEC 42001 provides a certifiable management system for ongoing governance.
Who should approve high-risk AI use cases?
An accountable executive or AI governance committee with input from security, legal, data and business owners.