ISO 27001 AI Risk Assessment Template for Business
Artificial intelligence introduces unique information security risks that fall outside traditional ISO 27001 risk assessment frameworks. Data bias, model drift, adversarial attacks and regulatory compliance challenges require a systematic risk assessment approach tailored to AI systems. This article provides a comprehensive AI risk assessment template aligned with ISO 27001 requirements, helping organisations identify, evaluate and treat AI-specific risks while maintaining certification. The template is designed for use by information security managers, AI governance leads and internal auditors.
AI Risk Assessment Framework
The ISO 27001 AI risk assessment follows the same process as a conventional ISMS risk assessment, as defined in clauses 6.1.2 and 6.1.3. The key difference lies in the risk identification phase, where AI-specific risk factors must be systematically evaluated. The framework consists of five stages: context establishment, risk identification, risk analysis, risk evaluation and risk treatment.
Organisations should establish an AI risk context that defines the scope of AI systems covered, the risk criteria (including AI-specific factors such as model accuracy thresholds and fairness metrics), and the roles and responsibilities for AI risk management. This context should be documented in the ISMS scope document and risk management policy.
AI-Specific Risk Factors
Traditional information security risk factors such as confidentiality, integrity and availability remain relevant for AI systems. However, AI introduces additional risk dimensions that must be assessed separately. The table below identifies the key AI-specific risk factors and their impact on ISO 27001 controls.
| Risk Factor | Description | Potential Impact | ISO 27001 Control Mapping |
|---|---|---|---|
| Data bias | Training data contains systematic biases leading to unfair or discriminatory outcomes | Regulatory penalties, reputational damage, unfair treatment of protected groups | A.5.18 (Information security in supplier relationships), A.5.9 (Classification of information) |
| Model drift | Model performance degrades over time as data distributions change | Inaccurate predictions, operational failures, incorrect decisions | A.8.8 (Management of technical vulnerabilities), A.12.6.1 (Capacity management) |
| Adversarial attacks | Malicious inputs designed to manipulate model outputs | Evasion of security controls, data poisoning, model theft | A.5.27 (Information security in cloud services), A.8.24 (Use of cryptography) |
| Regulatory compliance | Failure to meet AI-specific regulations such as the EU AI Act or local data protection laws | Fines, business restrictions, mandatory recall of AI systems | A.5.31 (Legal, statutory, regulatory and contractual requirements) |
| Explainability | Inability to explain or interpret model decisions | Lack of accountability, audit failures, customer distrust | A.5.7 (Threat intelligence), A.8.1 (Asset management) |
| Data quality | Poor quality training data leading to unreliable model outputs | Business decisions based on incorrect insights, regulatory non-compliance | A.8.2 (Information classification), A.8.3 (Media handling) |
| Model theft | Unauthorised copying or extraction of trained model parameters | Loss of intellectual property, competitive disadvantage | A.8.24 (Use of cryptography), A.8.25 (Secure development lifecycle) |
| Third-party model risk | Use of external or open-source models with unknown security posture | Supply chain vulnerabilities, licensing violations | A.5.19 (Information security in supplier relationships) |
AI Risk Register Template
The risk register is the central document of the AI risk assessment process. The template below provides a structured format for recording AI-specific risks, their likelihood and impact ratings, and treatment plans. Each risk should be assigned a unique identifier and reviewed on a defined schedule.
| Risk ID | Risk Description | AI System Affected | Likelihood (1–5) | Impact (1–5) | Risk Level | Treatment Plan | Residual Risk |
|---|---|---|---|---|---|---|---|
| AI-R01 | Bias in recruitment model leads to discriminatory outcomes | Candidate screening system v2.3 | 3 | 5 | 15 (High) | Implement bias monitoring dashboard; conduct quarterly fairness audits | 9 (Medium) |
| AI-R02 | Model drift in credit scoring results in incorrect risk assessments | Credit risk engine v4.1 | 4 | 4 | 16 (High) | Deploy automated drift detection; monthly retraining pipeline | 8 (Medium) |
| AI-R03 | Adversarial inputs bypass fraud detection model | Transaction fraud classifier v1.8 | 3 | 5 | 15 (High) | Implement adversarial training; deploy input sanitisation layer | 6 (Low) |
| AI-R04 | Non-compliance with EU AI Act for high-risk classification | All high-risk AI systems | 2 | 5 | 10 (Medium) | Conduct regulatory gap analysis; implement compliance documentation | 4 (Low) |
| AI-R05 | Model theft via API extraction attacks | NLP production API v3.0 | 4 | 4 | 16 (High) | Rate limiting; differential privacy; watermarking | 8 (Medium) |
| AI-R06 | Third-party vision model contains hidden backdoor | Image classification service | 2 | 4 | 8 (Medium) | Vendor security assessment; model validation before deployment | 4 (Low) |
| AI-R07 | Low-quality training data causes unreliable medical diagnosis recommendations | Clinical decision support system | 3 | 5 | 15 (High) | Data quality framework; source validation; human-in-the-loop review | 5 (Low) |
Control Mapping for AI Risks
Each identified AI risk should be mapped to the relevant ISO 27001 Annex A controls to ensure comprehensive coverage within the ISMS. The following mapping table connects AI-specific risk factors to the controls that address them. This mapping should be reflected in the Statement of Applicability (SoA).
| AI Risk Factor | ISO 27001 Control | Control Objective | Implementation Guidance for AI |
|---|---|---|---|
| Data bias | A.5.9, A.5.18 | Classification of information; supplier relationships | Classify training data by sensitivity; audit third-party data sources for representativeness |
| Model drift | A.8.8, A.12.6.1 | Vulnerability management; capacity management | Deploy continuous model monitoring; establish performance baselines and alerting thresholds |
| Adversarial attacks | A.5.27, A.8.24 | Cloud services; cryptography | Implement input validation; use encrypted model storage; deploy anomaly detection |
| Regulatory compliance | A.5.31 | Legal and regulatory compliance | Maintain AI regulatory register; conduct compliance reviews at each model version release |
| Explainability | A.5.7, A.8.1 | Threat intelligence; asset management | Document model interpretability methods; maintain model inventory with explainability ratings |
| Data quality | A.8.2, A.8.3 | Information classification; media handling | Implement data quality checks in MLOps pipeline; track data provenance |
| Model theft | A.8.24, A.8.25 | Cryptography; secure development | Encrypt model artifacts; implement access controls on model repositories |
| Third-party risk | A.5.19 | Supplier security | Vendor risk assessments for AI providers; model provenance verification |
Residual Risk and Review Cycle
After applying treatment plans, organisations must assess residual risk for each AI system. Residual risk should be evaluated against the organisation’s risk acceptance criteria defined in the ISMS. Risks exceeding the acceptance threshold require additional treatment or formal risk acceptance by senior management.
The AI risk assessment review cycle should be more frequent than for traditional IT systems, given the dynamic nature of AI risks. The recommended review schedule is:
- Continuous Monitoring – Real-time monitoring of model performance metrics, bias indicators and drift detection. Automated alerts trigger investigation when thresholds are breached.
- Monthly Review – Review of model monitoring dashboards, incident logs and retraining triggers. Minor adjustments to treatment plans as needed.
- Quarterly Risk Review – Formal review of the AI risk register, assessment of new risks arising from model updates or new deployments, and review of treatment plan effectiveness.
- Annual ISMS Review – Full AI risk assessment integrated into the ISMS management review process (clause 9.3). Includes updates to the risk assessment methodology, SoA and risk treatment plan.
- Trigger-Based Review – Ad-hoc review following significant incidents, regulatory changes, major model updates or changes in the AI threat landscape.
Frequently Asked Questions
How does an AI risk assessment differ from a standard ISO 27001 risk assessment?
The process is identical in structure, but AI risk assessment requires additional risk identification dimensions including data bias, model drift, adversarial robustness, explainability and model-specific regulatory compliance. The risk criteria should also include AI-specific metrics such as model accuracy thresholds, fairness metrics and explainability scores. The frequency of review should typically be higher for AI risks due to the dynamic nature of model behaviour.
Do I need to create a separate risk register for AI systems?
While not mandatory, maintaining a separate AI risk register is recommended for organisations with multiple AI systems or high-risk AI use cases. A dedicated register allows for more granular tracking of AI-specific risks and facilitates integration with AI governance frameworks such as the EU AI Act or ISO 42001. AI risks can alternatively be integrated into the main ISMS risk register with appropriate classification.
What is the role of AI governance in ISO 27001 risk assessment?
AI governance provides the policy framework within which AI risk assessments are conducted. The AI governance policy should define risk appetite for AI systems, establish ethical principles for AI deployment and assign responsibilities for AI risk management. The governance framework ensures that risk assessment results are reviewed by appropriate stakeholders, including data scientists, legal teams and business owners, before treatment decisions are made.
How do I assess risk for third-party AI models used through APIs?
Third-party AI models accessed via API introduce supplier risk, data privacy risk and model dependency risk. The assessment should include vendor security posture evaluation, contractual terms regarding data handling and model updates, service level agreements for availability and accuracy, and contingency plans for vendor lock-in or service discontinuation. ISO 27001 control A.5.19 (Information security in supplier relationships) provides the framework for this assessment.
Can I use automated tools for AI risk assessment?
Yes, automated tools can significantly improve the efficiency and consistency of AI risk assessments. Tools supporting ModelOps/MLOps platforms can automate bias detection, drift monitoring and adversarial testing. GRC platforms with AI-specific modules can help manage the AI risk register, track treatment plans and generate reports. However, automated tools should complement rather than replace expert judgement, particularly for qualitative risk assessment and treatment decision-making.
What is the relationship between ISO 27001 AI risk assessment and ISO 42001?
ISO 42001 (Artificial Intelligence Management System) provides a dedicated management system framework for AI. Organisations implementing both standards can integrate AI risk assessments within the ISO 42001 framework while leveraging ISO 27001 controls for security risk treatment. The risk assessment methodology defined in ISO 27001 clauses 6.1.2 and 6.1.3 can be applied to AI risks with AI-specific extensions drawn from ISO 42001 guidance. Organisations should maintain a single risk register that serves both standards.
How Bitrixme Can Help
Bitrixme offers ISO 27001 AI risk assessment services, including framework design, risk register development, control mapping and audit preparation. Our consultants combine deep expertise in information security management with practical knowledge of AI systems and governance. We help organisations integrate AI risk assessment into their existing ISMS efficiently, ensuring compliance with both ISO 27001 and emerging AI regulations. Contact us to learn more about our AI governance and risk assessment services.
