Why AI Risk Assessments Are Essential
Every AI tool or application introduces risks that must be identified and managed before deployment. Unlike traditional software, AI systems can produce unpredictable outputs, amplify biases, mishandle sensitive data, and create regulatory exposure — often in ways that are not immediately obvious.
A structured AI risk assessment helps your company evaluate these risks systematically, rather than discovering them after an incident. This template provides a practical framework that any company in Malaysia or Singapore can use, regardless of size or industry.
When to Conduct an AI Risk Assessment
An AI risk assessment should be conducted:
- Before deploying any new AI tool or application
- Before expanding the use of an existing AI tool to new departments or data types
- When an AI vendor releases a significant update or changes their terms of service
- After any AI-related incident or near-miss
- As part of your annual risk review cycle
AI Risk Assessment Template
Section 1: AI System Information
Complete this section to document the basic details of the AI system being assessed.
| Field | Details |
|---|---|
| Assessment date | [DATE] |
| Assessor(s) | [NAMES AND ROLES] |
| AI system/tool name | [NAME] |
| Vendor/provider | [VENDOR NAME] |
| Version | [VERSION NUMBER] |
| Intended use case | [DESCRIPTION] |
| Department(s) affected | [LIST] |
| Data types involved | [LIST] |
| Number of users | [ESTIMATED COUNT] |
| Deployment status | [Planned / Pilot / Production] |
Section 2: Risk Categories
Evaluate the AI system against each of the following risk categories. Use the scoring matrix at the end of this section.
2.1 Data Privacy Risk
| Risk Factor | Assessment |
|---|---|
| Does the system process personal data? | Yes / No |
| Types of personal data involved | [List: names, IC numbers, financial data, health data, etc.] |
| Where is data stored? | [Country/region] |
| Is data used for model training? | Yes / No / Unknown |
| Cross-border data transfer? | Yes / No |
| PDPA (Malaysia) compliance confirmed? | Yes / No / Not Assessed |
| PDPA (Singapore) compliance confirmed? | Yes / No / Not Assessed |
| Data retention period | [Duration or policy reference] |
| Data deletion capability | Yes / No |
Likelihood: [1-5] Impact: [1-5] Risk score: [Likelihood x Impact] Mitigation measures: [Describe]
2.2 Accuracy and Reliability Risk
| Risk Factor | Assessment |
|---|---|
| Can outputs contain factual errors (hallucinations)? | Yes / No |
| How critical are outputs to business decisions? | Low / Medium / High / Critical |
| Is human review required before output use? | Always / Sometimes / Never |
| Has output accuracy been tested? | Yes / No |
| Accuracy rate in testing | [Percentage or qualitative assessment] |
| Failure mode if output is incorrect | [Describe potential consequences] |
Likelihood: [1-5] Impact: [1-5] Risk score: [Likelihood x Impact] Mitigation measures: [Describe]
2.3 Bias and Fairness Risk
| Risk Factor | Assessment |
|---|---|
| Could outputs affect decisions about individuals? | Yes / No |
| Use cases involving recruitment, performance, or promotion? | Yes / No |
| Has the system been tested for demographic bias? | Yes / No |
| Are outputs reviewed for discriminatory content? | Always / Sometimes / Never |
| Can users provide feedback on biased outputs? | Yes / No |
Likelihood: [1-5] Impact: [1-5] Risk score: [Likelihood x Impact] Mitigation measures: [Describe]
2.4 Security Risk
| Risk Factor | Assessment |
|---|---|
| Authentication method | [SSO / Username-password / API key / Other] |
| Encryption in transit? | Yes / No |
| Encryption at rest? | Yes / No |
| Access controls/RBAC? | Yes / No |
| Audit logging available? | Yes / No |
| Vendor security certifications | [SOC 2, ISO 27001, etc.] |
| Penetration testing conducted? | Yes / No / By vendor only |
| Integration with company systems | [List integrations] |
Likelihood: [1-5] Impact: [1-5] Risk score: [Likelihood x Impact] Mitigation measures: [Describe]
2.5 Regulatory and Legal Risk
| Risk Factor | Assessment |
|---|---|
| Industry-specific AI regulations applicable? | Yes / No |
| MAS guidelines applicable? (Singapore financial services) | Yes / No |
| BNM guidelines applicable? (Malaysia financial services) | Yes / No |
| PDPC guidance applicable? (Singapore) | Yes / No |
| Intellectual property risks identified? | Yes / No |
| Contractual obligations with clients re: AI use? | Yes / No |
| Insurance coverage for AI-related claims? | Yes / No |
Likelihood: [1-5] Impact: [1-5] Risk score: [Likelihood x Impact] Mitigation measures: [Describe]
2.6 Operational and Dependency Risk
| Risk Factor | Assessment |
|---|---|
| What happens if the AI system is unavailable? | [Describe impact and workaround] |
| Vendor lock-in risk | Low / Medium / High |
| Alternative tools available? | Yes / No |
| SLA with vendor | [Uptime guarantee and support level] |
| Cost predictability | Fixed / Usage-based / Uncertain |
Likelihood: [1-5] Impact: [1-5] Risk score: [Likelihood x Impact] Mitigation measures: [Describe]
Section 3: Risk Scoring Matrix
| Score | Likelihood | Impact |
|---|---|---|
| 1 | Rare | Negligible |
| 2 | Unlikely | Minor |
| 3 | Possible | Moderate |
| 4 | Likely | Major |
| 5 | Almost certain | Severe |
Risk rating:
| Combined Score (L x I) | Rating | Action Required |
|---|---|---|
| 1-4 | Low | Accept with monitoring |
| 5-9 | Medium | Mitigate and monitor |
| 10-15 | High | Mitigate before deployment |
| 16-25 | Critical | Do not deploy without executive approval and significant mitigation |
Section 4: Overall Assessment Summary
| Risk Category | Score | Rating | Key Mitigation |
|---|---|---|---|
| Data Privacy | [X] | [Rating] | [Summary] |
| Accuracy/Reliability | [X] | [Rating] | [Summary] |
| Bias/Fairness | [X] | [Rating] | [Summary] |
| Security | [X] | [Rating] | [Summary] |
| Regulatory/Legal | [X] | [Rating] | [Summary] |
| Operational/Dependency | [X] | [Rating] | [Summary] |
| Overall | [Max] | [Highest] | [Primary action] |
Section 5: Approval
| Role | Name | Decision | Date |
|---|---|---|---|
| Assessor | [Name] | Assessed | [Date] |
| Risk Owner | [Name] | Approved / Rejected / Conditional | [Date] |
| CISO/DPO | [Name] | Approved / Rejected / Conditional | [Date] |
| Business Sponsor | [Name] | Approved / Rejected / Conditional | [Date] |
How to Use This Template
For First-Time Assessments
Work through every section thoroughly. The first assessment establishes your baseline understanding of the risks and sets the mitigation plan.
For Reassessments
Focus on what has changed since the last assessment — new data types, new users, vendor updates, or incidents. Update scores and mitigation measures accordingly.
Integration with Existing Risk Processes
This AI risk assessment should feed into your company's broader enterprise risk management framework. AI risks should be reported alongside operational, financial, and compliance risks to provide a complete picture for leadership.
Regulatory Alignment
Singapore
This template aligns with the PDPC's AI Governance Framework guidance on risk assessment, as well as MAS guidelines on technology risk management (MAS TRM) for financial institutions.
Malaysia
This template supports compliance with Malaysia's PDPA requirements around data processing impact assessment and Bank Negara Malaysia's risk management expectations for technology adoption.
Related Reading
- AI Policy Template — Build your policy framework around identified risks
- AI Evaluation Framework — Measure quality, risk, and ROI of AI implementations
- Copilot Governance & Access — Apply risk assessment to Microsoft Copilot deployment
Frequently Asked Questions
AI risk assessments should be conducted before any new AI deployment, when expanding existing AI use to new data types or departments, after vendor updates, after incidents, and as part of annual risk reviews. High-risk AI applications should be reassessed quarterly.
AI risk assessments should involve a cross-functional team: the business owner of the AI use case, IT/security, the Data Protection Officer or legal counsel, and a representative from the affected department. For high-risk applications, consider engaging an external assessor.
A Critical rating (score 16-25) means the AI tool should not be deployed without executive-level approval and significant risk mitigation. This may include restricting data inputs, adding mandatory human review, implementing additional security controls, or choosing an alternative tool.