ISO 27001:2022 Annex A 5.27 Learning from information security incidents for AI Companies

ISO 27001 Annex A 5.27 is a security control that requires organizations to analyze and learn from security incidents to prevent recurrence. The primary implementation requirement involves conducting formal post-incident reviews and root cause analyses, providing the business benefit of long-term operational resilience and AI infrastructure hardening.

At the heart of a resilient security program is the ability to respond effectively when things go wrong. This is the core purpose of ISO 27001 Annex A 5.27 Learning from information security incidents. In simple terms, this control ensures that you learn from past mistakes so you do not repeat them.

For an AI company, where proprietary data is your most valuable asset and complex algorithms are the engine of your business, implementing this control is not merely a compliance exercise. It is a fundamental strategy for protecting your core business value, building customer trust, and turning every challenge into a source of strength.

Table of contents

• The “No-BS” Translation: Decoding the Requirement
• The Business Case: Why This Actually Matters for AI Companies
• DORA, NIS2 and AI Regulation: You Must Learn
• ISO 27001 Toolkit vs SaaS Platforms: The Learning Trap
• Why Learning From Incidents is Different for AI Companies
  • Exposure of Sensitive Training Datasets
  • Disruption of Algorithmic Processes
  • Vulnerabilities in the AI Supply Chain
• What You Need To Do: A Practical Framework for Incident Learning
  • Establish a Formal Incident Review Process
  • Conduct Thorough Root Cause Analysis (RCA)
  • Document and Track Your Lessons
• The Evidence Locker: What the Auditor Needs to See
• Common Pitfalls & Auditor Traps
• Handling Exceptions: The “Break Glass” Protocol
• The Process Layer: “The Standard Operating Procedure (SOP)”

The “No-BS” Translation: Decoding the Requirement

Let’s strip away the consultant-speak. Annex A 5.27 is about stopping history from repeating itself. It asks: “Did you actually fix the root cause, or did you just reboot the server?”

The Auditor’s View (ISO 27001)	The AI Company View (Reality)
“Knowledge gained from information security incidents shall be used to reduce the likelihood or impact of future incidents.”	Do a Post-Mortem. If a developer leaks an API key, don’t just revoke it. Ask why they had the key in the first place. Then change the process so keys are injected automatically. Fix the system, not just the symptom.
“Documented procedures for post-incident review.”	Write down the lessons. Keep a “Lessons Learned” log. “In Q1 we had an outage because of X. We implemented Y to stop it.”

The Business Case: Why This Actually Matters for AI Companies

Why should a founder care about “Lessons Learned”? Because recurring incidents are a sign of a dying company.

The Sales Angle

Enterprise clients will ask: “Can you share a redacted Post-Incident Report (PIR) from a recent security event?” If your answer is “We don’t have incidents,” they know you are lying. If your answer is “Here is how we identified a minor vulnerability in our inference API, patched it within 4 hours, and rolled out a new WAF rule to prevent recurrence,” you win their trust. A 5.27 proves maturity.

The Risk Angle

The “Groundhog Day” Breach: You get hacked via a phishing email. You reset passwords. Two weeks later, you get hacked via phishing again. Why? Because you didn’t implement MFA keys. Learning from the first incident would have prevented the second (and more expensive) one.

DORA, NIS2 and AI Regulation: You Must Learn

Regulators demand evidence of improvement.

• DORA (Article 13): Requires financial entities to have a process for “learning from incidents.” You must analyse the causes and share the lessons.
• NIS2 Directive: Mandates that incident handling procedures must include “lessons learned.” If you report a breach, the regulator will ask: “What have you changed to stop this happening again?”
• EU AI Act: High-risk AI providers must have a post-market monitoring system. If your model exhibits bias or safety failures (incidents), you must analyse why and update the model. Continuous learning is a legal requirement for AI safety.

ISO 27001 Toolkit vs SaaS Platforms: The Learning Trap

SaaS platforms close tickets, but they don’t force you to think. Here is why the ISO 27001 Toolkit is superior.

Feature	ISO 27001 Toolkit (Hightable.io)	Online SaaS Platform
The Method	Structured Thinking. Templates guide you through “5 Whys” and “Root Cause Analysis” (RCA).	Checkbox Compliance. Platforms often just have a text box for “Resolution.” They don’t force a deep dive into why it happened.
Ownership	Your Knowledge Base. You build a library of PIRs (Post-Incident Reports) in your own drive.	Locked Data. Your incident history is stuck in their proprietary format. Hard to export and learn from later.
Simplicity	Action Oriented. “Assign corrective action to [Name] by [Date].” Clear accountability.	Workflow Bloat. Forces you through a generic 10-step wizard for every minor issue, causing users to skip details.
Cost	One-off fee. Pay once. Learn forever.	Subscription. You pay monthly for a tool that is essentially a diary.

Why Learning From Incidents is Different for AI Companies

A security incident in an AI context goes beyond typical IT disruptions.

Exposure of Sensitive Training Datasets

If an incident leads to the exposure of proprietary training datasets, the impact is catastrophic. Learning from incidents that threaten your data assets is crucial. Did the data leak because of a misconfigured S3 bucket? If so, the lesson is to implement automated infrastructure-as-code scanning, not just “train staff.”

Disruption of Algorithmic Processes

An incident could poison your model with subtle biases. A structured learning process allows you to analyse how the poisoning happened (e.g., unchecked public data source) and implement input validation filters to prevent recurrence.

Vulnerabilities in the AI Supply Chain

If a supplier is breached, you must learn from their failure. Update your supplier risk assessment (A 5.19) and perhaps switch vendors or enforce stricter controls (e.g., own-key encryption).

What You Need To Do: A Practical Framework for Incident Learning

Moving from compliance to resilience requires a structured process.

Establish a Formal Incident Review Process

Create a procedure that defines when to learn. Not every failed login needs an RCA. Define triggers (e.g., “Any P1 or P2 incident triggers a mandatory Post-Mortem within 48 hours”).

Conduct Thorough Root Cause Analysis (RCA)

Use the “5 Whys” technique. Peel back the layers to find the foundational cause.

Level	Question	Answer
Problem	Ransomware infected the server.
Why 1	Why did it get in?	Admin clicked a phishing link.
Why 2	Why did the link work?	Email filter didn’t catch it.
Why 3	Why didn’t the filter catch it?	Filter definition was outdated.
Why 4	Why was it old?	Auto-update server crashed.
Why 5 (Root)	Why did no one know?	No monitoring on the update server.

Document and Track Your Lessons

Establish a “Corrective Action Log.” Every lesson must translate into a task (e.g., “Install monitoring on update server”) with an owner and a deadline.

The Evidence Locker: What the Auditor Needs to See

When the audit comes, prepare these artifacts:

• Incident Learning Procedure (PDF): The document defining how you do RCAs.
• Post-Incident Reports (PIRs): Completed forms for past incidents showing the 5 Whys analysis.
• Corrective Action Log (Excel/Linear): A list of actions generated from incidents, showing “Status: Completed.”
• Risk Register Updates: Evidence that you updated your Risk Register based on a recent incident (e.g., increased probability of Phishing).

Common Pitfalls & Auditor Traps

Here are the top 3 ways AI companies fail this control:

• The “Blame Game” RCA: The Root Cause is listed as “Human Error (Dave).” This is wrong. The root cause is a process failure that allowed Dave to make a mistake. Auditors hate “Human Error” as a root cause.
• The “Empty” Log: You have incidents, but your Corrective Action Log is empty. This proves you are putting out fires but not fireproofing the house.
• The “Forgotten” Action: You identified a fix 6 months ago (“Turn on MFA”) but never did it. The same incident happens again. Major Non-Conformity.

Handling Exceptions: The “Break Glass” Protocol

Sometimes you can’t fix the root cause immediately (e.g., legacy dependency). You need to accept the risk temporarily.

The Risk Acceptance Workflow:

• Identification: RCA shows we need to rewrite the entire authentication module (3 months work).
• Decision: CTO accepts the risk of delay.
• Mitigation: Implement a temporary “band-aid” (e.g., daily manual log reviews) until the fix is ready.
• Log: Document the risk acceptance in the Risk Register with a review date.

The Process Layer: “The Standard Operating Procedure (SOP)”

How to operationalise A 5.27 using your existing stack (Linear, Notion).

• Step 1: Trigger (Automated). Incident ticket in Linear is marked “Resolved.” Bot posts to Slack: “Please schedule Post-Mortem.”
• Step 2: Meeting (Manual). Incident Commander hosts a 30-min “blameless” retrospective.
• Step 3: Documentation (Manual). Fill out the “PIR Template” in Notion (5 Whys, Timeline, Impact).
• Step 4: Action (Manual). Create new Linear tickets for the fixes. Tag them “Corrective Action.”
• Step 5: Review (Automated). Monthly report shows “Open Corrective Actions.”

For an innovative AI company, mastering the discipline of learning from information security incidents is a strategic imperative. The High Table ISO 27001 Toolkit helps you document this logic in minutes, transforming your response to adversity into a powerful engine for building resilience.

ISO 27001 Annex A 5.27 for AI Companies FAQ

About the author