Auditing the AI Supply Chain: What Cybersecurity Auditors Should Really Be Testing
“Don’t worry, we use AI for that.”
Most auditors have heard some version of this in the last year usually around fraud detection, anomaly monitoring, or access reviews. The risk is that “we use AI” ends the conversation instead of starting a deeper control discussion.
You don’t need to become a data scientist to audit AI-enabled environments. You just need a clear way to examine the AI systems that are quietly turning into key controls in SOC 2 and ISO 27001 engagements. Thinking in terms of the AI supply chain makes that work practical.
Where AI/Machine Language (ML) Actually Shows Up in Controls
AI is now embedded in controls you already rely on:
Fraud and transaction monitoring
Models scoring logins, payments, or account changes for fraud risk.Security and anomaly detection
Tools flagging suspicious behavior or unusual logins.Access and identity governance
AI-assisted access reviews, entitlement recommendations, and auto-provisioning.Email and web security
Systems classifying phishing, spam, and malicious attachments.Ticket triage and incident response
Models grouping alerts and suggesting severity or next steps.
If these systems are reducing false negatives or driving human action (blocking access, triggering investigations), they’re part of your control environment, not just “nice-to-have tooling.”
Your job isn’t to rebuild the model. It’s to verify that:
The AI-enabled control is designed to manage risk appropriately, and
It is operating effectively over the period, with governance around its supply chain.
Mapping the AI Supply Chain Before You Test
Treat each AI-enabled control like a mini supply chain. At a minimum, understand:
Business use case
What decision is the AI influencing?
What risk does it mitigate (fraud, unauthorized access, data exfiltration)?
Data sources
What training data was used?
What feeds the model in production (logs, transactions, HR data)?
How is that data validated and governed?
Model & logic
Is it in-house, open source, or vendor “black box”?
Are assumptions, limitations, and intended use documented?
Infrastructure & pipeline
Where does the model run (cloud, on-prem, SaaS)?
How are models deployed and updated?
Human-in-the-loop
Who reviews the AI’s decisions?
When can humans override or adjust?
This supply chain view helps you scope procedures:
What belongs in SOC 2 testing around security, availability, and confidentiality.
Which parts map to ISO 27001 controls around access control, operations security, change management, and suppliers.
Evidence You Should Expect for AI Governance
Once you know what you’re dealing with, you can ask for familiar forms of evidence.
1. Governance & Accountability
Look for:
AI/ML policy or standard (even if brief)
Named owner for the AI use case
Risk assessment covering the AI system
Architecture diagrams showing data flows and integrations
2. Training Data & Data Governance
Expect to see:
Documentation of training datasets (source systems and ranges)
Evidence of data quality checks
Access controls over dataset storage
Controls to prevent use of unauthorized or sensitive data in training
You don’t need to inspect feature engineering, but you should see that the data feeding the model is controlled like any other critical dataset.
3. Model Performance & Monitoring
For critical AI-enabled controls, look for:
Model validation or risk assessment describing purpose, assumptions, and metrics
Performance dashboards or reports used by engineering or security teams
Defined thresholds for unacceptable performance and who gets alerted
4. Change Management & Drift
Treat model updates like application changes:
Version control showing model and configuration changes
Deployment pipeline evidence (build logs, approvals, test runs)
Release notes documenting what changed and why
Evidence of drift monitoring and periodic review
5. Human-in-the-Loop Controls
Even “automated” controls usually have humans somewhere in the loop. Look for:
Workflow definitions showing when humans must review or approve decisions
Sampling and quality checks over AI-generated determinations
Training for analysts reviewing AI alerts
Metrics such as percent of AI decisions overridden
How to Test AI Without Becoming a Data Scientist
You can use a simple pattern across SOC 2 and ISO 27001 audits.
Step 1: Clarify Purpose & Risk
Ask:
“What decisions does this AI system influence?”
“What could go wrong if it’s wrong or offline?”
“Which SOC 2 criteria or ISO 27001 controls does this support?”
This helps you decide whether it’s a key control or supporting tool, and how deep your testing needs to go.
Step 2: Evaluate Design
Test design by walking through:
Inputs – Are data sources authenticated, authorized, and validated?
Processing – Is there a clear description of what the model should do and where it must not be used?
Outputs & actions – How are alerts or scores used, reviewed, and documented?
If the answers are vague or undocumented, that’s a design issue—even with a sophisticated model.
Step 3: Test Operating Effectiveness
Use familiar techniques:
Sample transactions or alerts and trace them from input → model output → human review → final action.
Review change and release history by selecting model updates during the period and verifying approvals, testing, and deployment controls.
Inspect monitoring and incident handling by reviewing drift or performance reports and how related incidents were handled.
Frame testing around control objectives:
Unauthorized access is prevented or detected.
Abnormal behavior is flagged and investigated.
Model changes are controlled and auditable.
Sensitive data is handled appropriately.
Where Audit Practices Go From Here
AI won’t stay a special topic forever; it’s becoming part of the control fabric you already audit.
If you treat AI systems as supply chains with their own data, models, pipelines, and people, you can ask sharper questions than “do you use AI?”, reuse procedures across clients and frameworks, and show clients and boards that your firm can safely audit modern, AI-enabled environments.
That’s how cybersecurity auditors move from “we’re nervous about AI” to “we know how to audit it” and position themselves for the most complex, interesting work ahead.
Mini-FAQ: Auditing AI-Enabled Controls
Q1: Do I have to understand the model algorithm to audit an AI control?
No. Your focus is on governance and control design, not model math. You should understand the purpose, data sources, change process, and monitoring, and then test those like any other key control.
Q2: When does an AI system rise to the level of a “key control”?
Treat it as key when its decisions directly affect risk outcomes (e.g., blocking access, approving transactions, suppressing alerts) rather than just providing optional insights or reports.
Q3: What’s a simple red flag with AI controls?
Any situation where management says “the model handles that” but can’t show who owns it, how performance is tracked, or when it was last reviewed. That usually points to a “set-and-forget” risk.
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