In the high-velocity world of artificial intelligence, rapid innovation isn’t just a goal; it’s survival. But moving fast shouldn’t mean breaking things, especially when those “things” are security protocols protecting proprietary algorithms and sensitive datasets.
For AI companies, where intellectual property is the crown jewel, managing changes to your Information Security Management System (ISMS) needs to be structured and predictable. You need to maintain trust without slowing down your dev cycles.
This is where ISO 27001 Clause 6.3, “Planning of Changes,” steps in. It provides a formal framework to ensure that as your neural networks and pipelines evolve, your security posture remains rock solid.
Table of contents
Deconstructing Clause 6.3: What You Really Need to Know
Let’s demystify Clause 6.3, specifically regarding the ISO 27001:2022 update. The headline here is simple: this clause formalises what mature tech companies usually do anyway. If you are already managing your git commits and infrastructure changes thoughtfully, there is no need to panic. This is simply about documenting and standardising that process for the auditor.
What is Planning of Changes?
At its core, the requirement is straightforward: any changes made to your ISMS must be carried out in a planned manner.
While this sounds like common sense, the standard now makes it explicit. This ensures that a systematic approach to modifications, whether it’s swapping out a Python library or changing cloud access keys, is a non-negotiable part of your certified ISMS. It prevents those ad-hoc “quick fixes” that often introduce unforeseen vulnerabilities.
The Official Definition
The standard defines the requirement for ISO 27001 Clause 6.3 with concise clarity:
“When the organisation determines the need for changes to the information security management system, the changes shall be carried out in a planned manner.”
Understanding this definition is step one. Next, we look at how to bake this into your operations.
Your 10-Step Framework for Implementing Clause 6.3
A robust implementation plan is vital for turning Clause 6.3 from a rule into a workflow. Below is a step-by-step framework to embed planned change management into your AI company’s DNA. Following these steps helps generate the specific evidence an auditor will look for.
1. Establish a Change Management Process
This is your blueprint. In an AI environment, changes range from updating a model in production to reconfiguring AWS IAM roles. A formal process ensures every change follows a predictable path. Startups often resist this, fearing bureaucracy, but you should view it as a mechanism for reducing risk and improving system stability. Write a clear policy and train your team on it.
2. Assess the Impact of Changes
Before you push code, assess the blast radius. If you update a core machine learning library, does it break the security dependencies of another tool? Does it alter data integrity? Involve your data scientists and engineers to use risk methodologies that identify potential negative outcomes before they happen.
3. Plan Changes in a Controlled Manner
A great algorithm without a deployment plan is a potential incident. For an AI firm, this means mapping out resource needs (like GPU time for regression testing), setting realistic timelines, and defining communication strategies. Don’t rush security checks just to ship a feature.
4. Authorise Changes
Who gives the green light? You need clear approval levels. A minor bug fix might only need a peer review, while a new cloud service integration should require CTO or CISO sign-off. Track these approvals formally, your auditor will want to see the digital paper trail.
5. Implement Changes as Planned
Stick to the script. Whether you are deploying a new encryption standard or updating firewall rules for a research cluster, adherence to the plan is key. Deviations cause downtime. Use project management tools to track progress and have a rollback plan ready if things go south.
6. Test Changes
Assumptions are dangerous. Thoroughly test all changes before they hit the live environment. In AI, this means not just functional testing, but security scanning and integration testing to ensure the change hasn’t introduced new vulnerabilities into your ecosystem.
7. Communicate Changes
Don’t let a change be a surprise. If you are altering a data processing pipeline, the downstream teams need to know. Poor communication leads to chaos. Send clear updates via Slack, email, or your intranet to all affected stakeholders.
8. Review Changes
The job isn’t done at deployment. Conduct a post-implementation review (PIR). Did the change work? Did it break anything else? Capture lessons learned to drive continual improvement.
9. Document Changes
If it isn’t written down, it didn’t happen, at least, that’s what the auditor will say. Maintain accurate records of the rationale, impact assessment, approval, and review. A centralised ticketing system (like Jira or ServiceNow) is usually the best “single source of truth” here.
10. Manage Emergency Changes
Sometimes, you have to patch a zero-day vulnerability now. You need a fast-track process for these emergencies that balances speed with control. Define what constitutes an emergency, establish an expedited approval route, and ensure you document everything retrospectively.
Passing the Test: An Auditor’s View of Clause 6.3
Understanding the auditor’s mindset is the cheat code for your certification audit. They aren’t just looking for compliance; they are looking for control. They want a clear trail of evidence from policy to plan to record.
Here is the evidence they will demand:
Review of Your Change Management Process
They want to see that your process is alive, not just a dusty PDF.
- Document review of policies.
- Interviews with DevOps and security staff.
- Comparison against frameworks like ITIL.
Assessment of Impact Assessment
Do you actually check for risks before changing things?
- Reviewing completed impact assessment templates.
- Testing your team with a hypothetical “bad change” scenario.
Evaluation of Change Planning
Evidence that you don’t just “wing it.”
- Review of implementation plans and timelines.
- Check of resource allocation (e.g., did you actually book the testing time?).
Examination of Change Authorisation
Proof that the right people said “yes.”
- Reviewing approval workflows in your ticketing system.
- Verifying approval hierarchies (e.g., Junior devs shouldn’t approve prod infrastructure changes).
Assessment of Change Implementation & Testing
Did you do what you said you would, and did you test it?
- System logs showing the change happened when planned.
- Review of test cases and successful test reports.
Evaluation of Documentation & Review
The paper trail.
- Post-implementation review reports.
- Audit trails within your change management software.
- Incident rates related to recent changes.
Conclusion: Embedding Controlled Change into Your AI Culture
Ultimately, ISO 27001 Clause 6.3 isn’t a bureaucratic hurdle designed to slow you down. For an innovative AI company, it is a business enabler. By mastering the principles of planned change, you create a resilient environment that protects your IP and builds customer trust. Embedding this discipline ensures that as your technology accelerates, your security framework scales right along with it.
