Introduction: Beyond the Checklist
ISO 27001 Annex A 5.35 Independent review of information security requires your organisation’s entire approach to security to be reviewed by an independent party. The purpose is simple: to ensure that your security measures, covering people, processes, and technology, remain suitable, adequate, and effective. For any business, this is a sensible practice. But for you, as an AI company, this control is far more than a compliance checkbox. The goal is to implement oversight that provides assurance to your customers and partners without encumbering the agile, experimental workflows that drive AI innovation. In a field defined by rapid innovation and complex data ecosystems, a robust and independent review process is a critical practice for maintaining operational resilience and, most importantly, building the trust that underpins your success in a rapidly evolving technological landscape.
Table of contents
The AI Challenge: Why Independent Reviews are Different for You
While the principles of Annex A 5.35 are universal, their application within an AI environment presents unique and complex challenges. Standard security audits may not fully appreciate the specific vulnerabilities and operational sensitivities inherent in developing and deploying artificial intelligence. Understanding these specific risks is the first step toward building a robust and relevant security programme that protects your most valuable assets without hindering your core mission.
Protecting Your Crown Jewels: Securing Training Data
Your training datasets are the irreplaceable intellectual property at the heart of your models. An independent security review must assess how these assets are protected. The challenge is providing access for a reviewer without exposing the unique statistical distributions, proprietary labelling schemas, or complex feature engineering techniques embedded within the data. Losing this is more than a privacy breach; it is the loss of the very IP that defines your model’s performance and competitive advantage. Planning a review therefore requires careful consideration of how to verify security while safeguarding these critical assets.
Maintaining Model Integrity: Algorithmic Processes Under Scrutiny
Your AI models are not static assets. An improperly scoped review could inadvertently disrupt their delicate lifecycle. A non-specialist reviewer might demand tests that could cause model drift, require costly rollbacks, or interfere with a model that is continuously learning. This is particularly challenging when the “secure state” of your model is constantly evolving. The independent review must be meticulously planned to validate security without compromising the integrity or performance of your core AI models, ensuring assurance is gained without disrupting the very processes that create your value.
The AI Supply Chain: A New Frontier for Vulnerabilities
Modern AI development relies on a complex supply chain. Your workflows likely depend on foundational open-source libraries like TensorFlow or PyTorch, pre-trained models from public repositories, third-party data providers, and specialised MLOps platforms. Each element introduces a potential vulnerability, from data poisoning risks in sourced datasets to security flaws in underlying frameworks. As ISO 27001 control 5.21, “Managing Information Security in the ICT Supply Chain,” recognises, these risks extend far beyond your direct control. Your independent review must therefore assess this entire ecosystem, complicating its scope significantly.
Addressing these distinct challenges requires more than awareness; it demands a structured and tailored approach to the review process itself.
Your Blueprint for Compliance: Practical Steps for AI Businesses
Successfully implementing Annex A 5.35 requires a structured, actionable plan that acknowledges the unique aspects of your AI operations. The following steps provide a blueprint to help you integrate robust security oversight into your workflows, turning a compliance requirement into a valuable tool for continual improvement without stifling innovation.
Establishing Your Review Programme
Your approach to independent reviews should be formalised and proactive. It should consist of two primary types of review activities:
- Planned Reviews: This is a recurring, scheduled activity, typically conducted on an annual basis. It provides a comprehensive assessment of your entire information security management system (ISMS) to ensure its ongoing effectiveness.
- Trigger-Based Reviews: These are ad-hoc reviews prompted by specific events. An independent review should be considered whenever significant changes occur, such as a major security incident, amendments to laws or regulations, the launch of a new AI product, a new business venture, or major changes to your existing security controls.
Selecting the Right Reviewer
The credibility of an independent review hinges on the competence and independence of the reviewer. The individual or team must not be involved in the area being assessed, a principle often summarised as not “marking your own homework.” This objectivity is essential for uncovering blind spots.
| Suitable Reviewers | Unsuitable Reviewers |
|---|---|
| An External Consultant or certified audit firm. | Your CISO or Information Security Manager. |
| An Internal Audit Team that reports to the board. | Your IT Manager reviewing IT controls they manage. |
| An Independent Manager from another department. | Anyone with authority over the area being reviewed. |
For an AI company, competence is just as critical as independence. An effective reviewer needs more than just audit skills; they require a baseline understanding of the machine learning lifecycle. This knowledge allows them to assess risks accurately without prescribing controls that would cripple model performance or the pace of innovation, ensuring the review adds value rather than just overhead.
Defining a Clear Scope for AI Workflows
A comprehensive review must evaluate if your security practices are effective and align with your information security policy and its objectives. For an AI-centric business, the scope must be tailored to address your specific operational risks. Key areas to include are:
- Secure data handling protocols during the review itself, to mitigate risks to sensitive training data.
- The environments for model training and validation, including controls to prevent unauthorised model tampering or inference during the audit.
- The security controls around inference endpoints, including protection against model inversion or extraction attacks.
- Your processes for managing vulnerabilities in third-party libraries and pre-trained models, linking directly to your AI supply chain risk assessment.
Managing Findings and Driving Improvement
The review process culminates in a formal report detailing all findings, which must be presented to the relevant management. Any identified weaknesses trigger a corrective action plan, with each action tracked to completion. This cycle of review, action, and improvement is not just a procedural requirement but the engine of continual improvement that sits at the heart of the ISO 27001 standard.
While this blueprint provides the necessary framework, constructing the required procedures and reports from scratch can divert critical resources. This is where a dedicated toolkit becomes an invaluable asset for implementation.
The High Table Solution: A Toolkit Built for Clarity and Control
Translating compliance theory into practice often presents the biggest hurdle for innovative, fast-moving companies. The High Table ISO 27001 Toolkit offers a practical solution, providing the structure and documentation needed to address the specific challenges of implementing Annex A 5.35.
Structured Governance Without the Guesswork
The High Table toolkit provides the essential templates to meet the documentation requirements of an independent review programme. It contains pre-built templates for an Internal Audit Procedure, which helps you establish and formalise your review programme, and an Internal Audit Report, which ensures that your findings are documented in a clear, professional, and audit-ready format. This removes the guesswork and provides a solid foundation for your compliance activities.
Why a Toolkit Gives You Control
For an innovative AI company, maintaining control over your documentation is essential. A documentation toolkit provides a key advantage: it offers a solid, auditor-verified foundation that is also flexible enough to be tailored to your unique and complex AI workflows. Unlike rigid online platforms, a toolkit gives you full ownership and control over your documentation, allowing you to accurately reflect your specific processes and technologies rather than forcing your operations to fit a generic template. This makes robust governance an achievable goal, not an operational burden.
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Conclusion: Building Trust Through Verified Security
For an AI company, implementing a robust process for independent security reviews is not just about compliance; it is a strategic imperative. It serves as a powerful tool for mitigating the unique risks associated with your technology, from protecting proprietary training data to securing a complex AI supply chain. More importantly, it provides verifiable proof of your commitment to security, helping you build and maintain the trust of customers, partners, and regulators. By leveraging a practical resource like the High Table ISO 27001 Toolkit, you can transform this complex requirement into a manageable, valuable, and repeatable business process that supports both security and innovation.
