Don’t just treat AI as smart – treat it as a traceable operational asset. This guide explores how AI systems interact with existing information security practices and ISO/IEC 27001:2022 Annex A controls. It also provides examples of documentation and records that may support internal assurance and audit preparation activities. It covers common operational areas such as training pipelines, inference endpoints, access controls, and vendor-managed AI services, intended to provide a conceptual framework for managing AI processes within a structured security framework.
1. Why AI is a Critical Asset in Your ISMS
AI systems are no longer experimental – they are often central to business operations. From training pipelines to inference endpoints, AI processes may be considered critical components within an ISO 27001-aligned ISMS.
Key Points:
- Models process sensitive data – similar to databases or file servers. For example, a chatbot trained on customer support logs may contain personal information subject to privacy obligations.
- Security incidents can affect compliance, operational continuity, and reputation. A misconfigured endpoint could lead to data exposure or unintended model behaviour.
- Annex A controls are commonly applied to a broad range of information assets. These can be extended to include AI-related assets, providing a basis for defining safeguards for both data and AI operations.
- Startups may implement layered protections, such as access controls, encryption, and monitoring, to address these operational risks.
2. Securing AI Training Pipelines and Inference Endpoints
AI models may be considered critical assets within your ISMS. This section highlights practical approaches for managing model training and inference, mapped to ISO/IEC 27001:2022 Annex A controls to support traceable evidence of operational security practices. Startups may treat model security similarly to other critical IT assets within the ISMS.
|
Control Area |
ISO 27001 Mapping |
Example Practical Implementation |
|
Model Training Environment |
A.8.25 (Secure development life cycle) A.8.3 (Information access restriction) |
Training datasets may be isolated and access restricted. Storage can be encrypted, and separate environments (A.8.31) may be used for sensitive data to help limit the likelihood of cross-contamination. |
|
Inference Endpoints |
A.8.3 (Information access restriction) A.8.15 (Logging) |
API usage, rate limits, and authentication (A.8.5) may be monitored. Startups can consider alerts for unusual access patterns or repeated failed requests via monitoring (A.8.16). |
|
Model Drift Monitoring |
A.8.16 (Monitoring activities) A.8.7 (Protection against malware) |
Model performance can be tracked over time. Significant deviations may signal potential data poisoning (A.8.7) or operational issues. Sudden accuracy drops may be assessed as anomalous behaviour under A.8.16. |
Tip: Model drift may be treated by some organisations as an operational security metric. Unexpected changes in model performance could suggest issues such as training data changes, data integrity problems, or configuration errors. Tracking these changes over time can support evidence of ongoing monitoring and risk management within the ISMS.
3. Integrating AI Logs into Your ISMS
Maintaining structured logs for AI operations may support operational transparency and traceability within an ISMS. Logging practices can be designed to support the alignment with ISO/IEC 27001:2022 Annex A controls, particularly A.8.15 – Logging and A.8.16 – Monitoring activities, to provide a documented record of system activities and potential anomalies.
Example Practical Considerations:
- Centralised Logging (A.8.15): Logs from model training, inference, and system metrics may be collected in a single repository. For instance, versioned AWS S3 buckets or Git repositories could store immutable log entries which may be referenced as part of documented logging practices addressing requirements relating to log production, storage, and protection.
- Prompt Injection Monitoring (A.8.16): Logs may capture evidence of input sanitisation or output filtering. This aligns with the A.8.16 requirement to monitor for anomalous behaviour and take appropriate actions to evaluate potential security incidents.
- Mapping to ISO Controls: Startups might choose to associate log entries with relevant Annex A clauses (e.g. mapping a failed API call to A.8.3 Information access restriction). This approach may assist in establishing traceability for security reviews.
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Versioned Evidence (A.8.15): Log files can be versioned to record access and modifications. Since A.8.15 requires logs to be protected and analysed, using Git commit history or cloud access logs provides context on who interacted with the logs and when.
Managing the AI Supply Chain
Startups often rely on third-party AI APIs (e.g. OpenAI, Anthropic, Google Gemini). Model security may be influenced by the security and operational practices of these vendors. ISO/IEC 27001:2022 Annex A.5.19 – Information Security in Supplier Relationships addresses how organisations may consider information security risks arising from supplier relationships.
Shared Responsibility Note: While third-party vendors manage the model foundation, startups typically retain responsibility for decisions relating to data submitted to APIs and the management of authentication credentials. A simple diagram or mapping of responsibilities – vendor vs startup – may help clarify this shared accountability.
Practical Considerations:
- Vendor Assessment: Copies of ISO 27001 or SOC 2 Type 2 reports from vendors may be collected without implying any guarantee of the organisation’s own compliance outcomes. These documents can provide visibility into upstream security practices only.
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Contractual Safeguards: Contracts may include provisions addressing the permitted use of customer data, such as restrictions on secondary uses beyond agreed scopes. Data Processing Addendums (DPAs) may clarify these terms.
4. AI-Specific Access Control, Encryption, and Incident Response
This section outlines practical approaches for access control, encryption, and incident response specifically for AI operations, mapped to ISO/IEC 27001:2022 controls.
|
Control |
ISO 27001 Mapping |
Example Practical Implementation |
|
Access Rights |
A.5.18 (Access rights) |
Role-based access may be applied to training data and model weights. For example, only ML engineers may have permissions to deploy models to production. Limiting access can help limit exposure to accidental or malicious changes. |
|
Encryption |
A.8.24 (Use of cryptography) |
Datasets may be encrypted at rest and in transit using enterprise-grade encryption protocols. Encryption is commonly used to help protect sensitive information during storage and communication. |
|
Incident Response |
A.5.24 (InfoSec incident management planning and preparation) |
AI-specific incidents, such as prompt injection or model inversion, may be captured in ISMS logs. Logging these events can provide traceability and support internal reviews or third-party assurance activities. |
5. Human-in-the-Loop Oversight
Automation does not remove the need for human review in critical AI processes. Incorporating oversight provides traceability and supports alignment with ISO/IEC 27001:2022.
- Human Review of Decisions (A.5.37): Procedures for AI-driven processing may include defined manual intervention points. For example, automatically flagged content may be queued for human moderation, which is one method for addressing the requirement for Documented operating procedures (A.5.37).
- Segregation of Duties (A.5.3): To prevent unmonitored automated actions, startups may choose to segregate the "Execution" (AI model) from the "Review" (Human moderator). This reflects the intent of the control to manage conflicting duties (A.5.3).
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Operational Logging (A.8.15): Logs can be configured to capture human overrides. This provides the record-keeping required by Logging (A.8.15) to provide a record of human oversight for review.
6. Visualising the AI Audit Trail
A clear workflow can help illustrate how AI decisions relate to your ISMS and support traceability. One example of a simplified audit trail is:
Workflow:
Model Lifecycle → Data Checksum → Vendor Report → Control → ISMS Evidence
- Purpose: This flow may help illustrate how model versions, data provenance, and vendor assessments are connected to ISMS controls and records.
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Benefit: Maintaining such a traceable record can support internal review and provide contextual traceability, though determinations regarding certification outcomes rest with the certifying auditor.
7. Potential Next Steps
The following actions may help startups document and manage AI-related controls with reference to ISO 27001 and SOC 2 practices:
- Third-party AI provider assurance: Maintaining copies of current ISO/IEC 27001 certificates and, where available, the latest independent audit reports (such as SOC 2 Type II) may assist in documenting upstream security practices considered during supplier risk evaluations, without implying assurance of the organisation’s own compliance outcomes.
- DPAs and contractual safeguards: Including appropriate data processing agreements can help clarify responsibilities and data handling expectations with vendors.
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ISMS evidence templates: Using structured templates may provide a consistent way to capture and link AI risk and control information within your ISMS.
Conclusion
Applying these practical controls can provide a starting point for startups in managing AI models as traceable operational assets, linking AI development and usage activities with ISO/IEC 27001:2022 information security practices. By documenting processes, logging key events, and mapping controls to Annex A references, organisations can document aspects of a structured approach to AI governance and information security.
Next Step: Browse ISO 27001 templates designed to support consistent process documentation and internal information organisation.
Next Article: In The ISO 27001 Surveillance Audit: Maintain Your ISMS in Year 2 and Beyond, we explore typical post-certification changes and how ongoing monitoring and reviews may support sustained conformity.
Related Guides
Explore these ISO 27001 resources to help your SME build a practical, lean ISMS:
Start Here: Complete Guide
- ISO 27001 for SMEs and Startups: The Chill Implementation Guide (2026 Edition) – Full roadmap covering all clauses and Annex A controls, with practical steps, examples, and guidance.
Scaling and The Future of Compliance – Detailed Guides by Topic
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ISO 27001 to SOC 2 Mapping: Evidence Comparison Guide for SMEs – A practical comparison of how ISO 27001 controls and evidence may align with SOC 2 Security criteria, highlighting common overlaps and gaps.
- The ISO 27001 Surveillance Audit: Maintain Your ISMS in Year 2 and Beyond – Practical guidance for SMEs on keeping an ISMS active through Year 2 and 3 audits, reviews, and change management.
- ISO 27001 for GDPR and CCPA: Informational Overview for SMEs and Startups (2026 Edition) – Practical guidance on using ISO 27001 to support privacy frameworks, mapping Annex A controls to GDPR and CCPA considerations.
- Exploring the Hybrid ISO 27001 Compliance Stack (2026) – A conceptual guide to how startups combine governance, cloud-native tools, and people for structured evidence management.
Please Note: This article provides general information only and does not constitute legal, regulatory, or compliance advice. Using our products or following this guidance cannot guarantee certification, improved business outcomes, or regulatory compliance. Organisations remain responsible for ensuring all actions meet certification and compliance requirements.
This article also mentions examples of commonly used tools. Chill Compliance does not endorse any vendor and has no commercial or affiliate relationship with the providers listed. These examples are for general information only, and readers may wish to evaluate each tool independently, as features and pricing can vary.