DPO
What is a Data Protection Officer (DPO)?
DPO is an acronym for Data Protection Officer. A DPO is a person who is given formal responsibility for data protection compliance within an...
Not All Data Is Equal: A Practical Guide to Prioritising Data Sensitivity for Compliance and Risk Management
Tai Chesselet
May 30, 2025
As a data privacy manager, you’re responsible for protecting all types of data — but treating all data the same is neither efficient nor effective. From our experience working with various organisations, we’ve found that not all data carries the same level of sensitivity, regulatory risk, or business impact.
That’s where a data sensitivity compliance matrix becomes an essential tool. By categorising and scoring data based on its risk level, privacy teams can better prioritise their efforts, implement stronger controls where it matters most, and stay confidently compliant.
Data Sensitivity Compliance Matrix
| Data Sensitivity Level | Examples | Regulatory Impact | Risk Score (1–5) | Recommended Controls |
| Highly Sensitive / Special Category Data | Children’s data, health records, biometric/genetic data, sexual orientation | Very High – GDPR Art. 9, HIPAA, POPIA, etc. | 5 | Explicit consent, encryption, access limitation, regular audits |
| Personally Identifiable Information (PII) | ID numbers, names with contact info, financial data, login credentials | High – GDPR, CCPA, POPIA, global privacy laws | 4 | Encryption, MFA, breach notification plans, data minimisation |
| Sensitive Operational Data | HR files, payroll, legal documents, internal investigations | Moderate – Data protection if tied to individuals or HR | 3 | Role-based access, employee training, legal review processes |
| Commercially Sensitive / Proprietary Data | Trade secrets, source code, R&D plans, pricing strategies | Low to Moderate – IP protection, reputational risk | 2 | NDAs, IP safeguards, restricted internal access |
| Historical / Compliance Data | Archived records, old audit logs, expired agreements | Low – Retention required, minimal active risk | 1 | Retention policies, regular disposal, storage access controls |
The 5 Levels of Data Sensitivity – Ranked by Risk
Here’s how to think about your organisation’s data in order of priority:
1. Highly Sensitive / Special Category Data
🔒 Examples: Children’s data, health records, biometric/genetic data
📉 Risk: Very High – tightly regulated under GDPR Article 9, POPIA, HIPAA
🛡 Controls: Requires explicit consent, strict access controls, encryption, and regular audits.
2. Personally Identifiable Information (PII)
🧾 Examples: ID numbers, contact details, financial info, login credentials
📉 Risk: High – central to most privacy laws globally
🛡 Controls: Data minimisation, MFA, breach readiness, and encryption are key.
3. Sensitive Operational Data
🏢 Examples: HR records, internal investigations, payroll data
📉 Risk: Moderate – often overlooked but can carry privacy and legal risks
🛡 Controls: Role-based access, HR compliance, legal oversight, and awareness training.
4. Commercially Sensitive Data
💼 Examples: Source code, R&D plans, pricing models
📉 Risk: Low to Moderate – not always personal, but high reputational impact
🛡 Controls: Use NDAs, limit internal access, and protect intellectual property.
5. Historical / Compliance Data
📁 Examples: Archived records, old logs, expired contracts
📉 Risk: Low – but improper retention can still breach regulations
🛡 Controls: Apply retention schedules, disposal protocols, and storage safeguards.
How this helps you stay compliant (and practical)
By using a structured framework to classify your data, you can:
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Focus resources on protecting the most sensitive and legally risky information.
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Communicate priorities clearly across departments (especially IT and legal).
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Meet audit and reporting requirements with a defensible, risk-based strategy.
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Reduce your organisation’s data footprint, improving efficiency and lowering exposure.
Take Action: Use PrivIQ to build your own Data Risk Universe
Start by reviewing your organisation’s data landscape:
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Where is this data coming from?
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Who has access?
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Is it being retained for the right reasons, and long enough (but not too long)?
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Are your controls aligned to the real-world risk?
- PrivIQ’s AI-enhanced / Human verified Data Privacy Framework can help you build a data mapping and classification policy that is compliant, practical and scalable
Want help building your data mapping strategy or compliance matrix? Reach out to our team to book a quick demo on how PrivIQ can streamline the entire process, Get in touch.