DCH-36: De-Identification (Anonymization)
Mechanisms exist to de-identify sensitive and/or regulated data through (1) Anonymization; (2) De-identification; (3) Pseudonymization; and/or (4) Redaction.
Renumbered: Formerly DCH-23 and PRI-05.3 in SCF 2025.4.
Control Question: Does the organization de-identify sensitive and/or regulated data through (1) Anonymization; (2) De-identification; (3) Pseudonymization; and/or (4) Redaction?
General (20)
| Framework | Mapping Values |
|---|---|
| CSA CCM 4.1.0 | LOG-08 |
| CSA IoT SCF 2 | GVN-05 |
| IEC TR 60601-4-5 2021 | 5.2 - CR 4.1 |
| ISO 27002 2022 | 8.11 8.33 |
| ISO 27018 2025 | 8.11 8.33 |
| NIST AI 600-1 | MS-2.2-002 MS-2.2-004 |
| NIST 800-53 R4 | DM-1(1) DM-3(1) |
| NIST 800-53 R5.2 | SI-19 SI-19(04) |
| NIST 800-53B R5 (privacy) (source) | SI-19 |
| NIST 800-82 R3 | SI-19 SI-19(04) |
| NIST 800-160 Vol2 R1 | SI-19(04) |
| NIST Privacy Framework 1.0 | CT.DP-P2 CT.DP-P3 |
| PCI DSS 4.0.1 (source) | 3.4.1 |
| PCI DSS 4.0.1 SAQ B (source) | 3.4.1 |
| PCI DSS 4.0.1 SAQ B-IP (source) | 3.4.1 |
| PCI DSS 4.0.1 SAQ C (source) | 3.4.1 |
| PCI DSS 4.0.1 SAQ C-VT (source) | 3.4.1 |
| PCI DSS 4.0.1 SAQ D Merchant (source) | 3.4.1 |
| PCI DSS 4.0.1 SAQ D Service Provider (source) | 3.4.1 |
| SCF DPMP 2026 | 5.10 |
US (9)
| Framework | Mapping Values |
|---|---|
| USA Federal CMS MARS-E 2.0 | DM-1(1) |
| USA Federal Data Privacy Framework 2023 | III.14.a.i III.14.g.i |
| USA California CCPA 2025 | 7123(c)(5)(C) |
| USA Montana CDPA 2025 | 30-14-2808(1)(c) 30-14-2815(1)(a) 30-14-2815(4) |
| USA Oregon CPA 2023 | Section 7(1)(a)(A) |
| USA Oregon 646A 2025 | 646A.583(1)(a)(A) |
| USA Tennessee TIPA 2025 | 47-18-3203(a)(2)(C) |
| USA Texas CDPA 2025 | 541.106(a)(1) |
| USA Virginia CDPA 2023 | 59.1-581.A.1 |
EMEA (10)
| Framework | Mapping Values |
|---|---|
| EMEA Belgium Act 8 1992 | Title 4, Chapter III, Section 3, Art. 198 Title 4, Chapter III, Section 3, Art. 199 Title 4, Chapter III, Section 3, Art. 200 Title 4, Chapter III, Section 3, Art. 201 |
| EMEA Germany C5 2026 | OPS-11.01B.5 OPS-11.01B.6 OPS-11.01AC DEV-07.03B INQ-03.02B |
| EMEA Germany FDPA 2017 | 3.2.48(2)6 3.2.50 3.4.64(2) |
| EMEA EU AI Act | Article 10.5(b) |
| EMEA Hungary Act CXII 2011 | II.11.12(2) |
| EMEA Kenya DPA 2019 | IV.39(2) |
| EMEA Russia 152-FZ 2025 | Art. 13.1 |
| EMEA Saudi Arabia CSCC-1 2019 | 2-6-1-1 |
| EMEA Serbia 87/2018 | IV.2.50(1) |
| EMEA Turkey LDDP 2016 | 7(3) |
APAC (1)
| Framework | Mapping Values |
|---|---|
| APAC Japan APPI 2020 | IV.2.35-2(1) |
Americas (2)
| Framework | Mapping Values |
|---|---|
| Americas Argentina Reg 132 2018 | H.1.1 |
| Americas Brazil LGPD 2018 | II.II.11.II(c) II.II.13 II.II.13.1 II.II.13.2 II.II.13.3 II.II.13.4 II.IV.16.II |
Capability Maturity Model
Level 0 — Not Performed
Practices are non-existent, based on the inability to demonstrate an implemented and operational capability. A reasonable person would conclude the control is not being performed.
Level 1 — Performed Informally
SCR-CMM Level 1 criteria definitions are not available for this control: ▪ A reasonable person would conclude this control requires a structured process. ▪ At this level of maturity, the "ad hoc" nature of performing a capability informally would indicate the intent of the control is not met due to a lack of consistency and formality.
Level 2 — Planned & Tracked
Data Classification & Handling (DCH) capabilities are requirements-driven, but are not standardized across the entity (e.g., local/regional level consistency). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with DCH domain capabilities are formally documented and centrally-managed by the entity. ▪ Standardized Operating Procedures (SOP) associated with DCH domain capabilities are documented and maintained by process owners. ▪ IT and/or cybersecurity personnel work with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with DCH domain capabilities to address applicable statutory, regulatory and/or contractual requirements for Technology Assets, Applications, Services and/or Data (TAASD). ▪ Data classification and handling-related controls are primarily administrative and preventative in nature (e.g., policies, standards and procedures & guidelines). ▪ Data classification and handling management may be a defined function (e.g., team or department) or assigned as an additional duty to existing IT and/or cybersecurity personnel. ▪ A formalized data classification scheme exists to identify categories of data, based on protection requirements from applicable laws, regulations and/or contractual obligations.
Level 3 — Well Defined
Data Classification & Handling (DCH) capabilities are standardized across the entity for applicability to People, Processes, Technologies, Data and/or Facilities (PPTDF) to ensure consistency for Technology Assets, Applications, Services and/or Data (TAASD). Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Policies and standards associated with DCH domain capabilities are formally documented and centrally-managed by the entity's Governance, Risk & Compliance (GRC) team, or similar function. ▪ Standardized Operating Procedures (SOP) associated with DCH domain capabilities are well-documented and kept current by process owners. ▪ A Governance, Risk & Compliance (GRC) team, or similar function, is appropriately staffed and supported to implement and maintain DCH domain capabilities. ▪ Technology is leveraged to enhance the efficiency and accuracy of data classification and handling operations (e.g., GRC platform). ▪ The entity's Governance, Risk & Compliance (GRC) team, or similar function, works with business stakeholders and process owners to appropriately scope and reasonably implement cybersecurity and data protection controls associated with DCH domain capabilities to address Minimum Compliance Requirements (MCR) (e.g., applicable statutory, regulatory and/or contractual requirements) and Discretionary Security Requirements (DSR) (e.g., entity-required controls). ▪ An implemented and operational capability exists to mask sensitive and/or regulated data through data anonymization, pseudonymization, redaction and/or de-identification.
Level 4 — Quantitatively Controlled
Data Classification & Handling (DCH) capabilities, in addition to being standardized across the entity and centrally managed to ensure consistency across Technology Assets, Applications, Services and/or Data (TAASD), efforts are metrics driven to provide sufficient insight for decision makers to predict optimal performance, ensure continued operations and/or identify areas for improvement. Capability criteria associated with this control reasonably expect the following criteria to exist: ▪ Applicable SCR-CMM Level 3 (Well Defined) capabilities are implemented and operational. ▪ Metrics reporting includes quantitative analysis of Key Performance Indicators (KPIs). ▪ Metrics reporting includes quantitative analysis of Key Risk Indicators (KRIs). ▪ Scope of metrics, KPIs and KRIs covers organization-wide cybersecurity and data protection controls, including functions performed by third-parties. ▪ Organizational leadership maintains a formal process to objectively review and respond to metrics, KPIs and KRIs (e.g., monthly or quarterly review). ▪ Based on metrics analysis, process improvement recommendations are submitted for review and are handled in accordance with change control processes. ▪ Business and technical stakeholders are involved in reviewing and approving proposed changes to evolve capabilities.
Level 5 — Continuously Improving
Utilize SCR-CMM Level 3 or Level 4 (if available) criteria definitions: ▪ There are no defined Level 5 criteria, since it is reasonable to assume a continuously-improving process is not necessary to operationalize this control. ▪ Level 5 capabilities should be considered “world-class” where the control builds on Level 4 capabilities, but are continuously improving through Artificial Intelligence (AI) and/or Machine Learning (ML) technologies. ▪ While it may be possible to develop responsive capabilities for this control through the use of AI and/or ML technologies, the criteria would be organization-specific to define.
Assessment Objectives
- DCH-36_A01 elements of sensitive and/or regulated data to be de-identified are defined.
- DCH-36_A02 the frequency at which to evaluate the effectiveness of de-identification is defined.
- DCH-36_A03 sensitive and/or regulated data is anonymized by removing the defined data elements from datasets.
- DCH-36_A04 the effectiveness of de-identification is evaluated at the defined frequency.
- DCH-36_A05 sensitive and/or regulated data is de-identified by removing, masking, encrypting, hashing or replacing direct identifiers in a dataset.
- DCH-36_A06 the de-identification technique(s) to be applied to sensitive and/or regulated data (anonymization, de-identification, pseudonymization and/or redaction) are defined.
- DCH-36_A07 sensitive and/or regulated data is pseudonymized by replacing direct identifiers with pseudonyms, with any information needed to re-identify the data held separately and protected.
- DCH-36_A08 sensitive and/or regulated data elements are redacted from datasets, documents and/or outputs where those elements are not needed.
Evidence Requirements
- E-TDA-21 Test Data Management
-
Documented evidence of restrictions on the use of live / sensitive data in testing and test data integrity controls.
Technology Design & Acquisition
Technology Recommendations
Micro/Small
- Data classification program
- Data privacy program
- Product / project management
Small
- Data classification program
- Data privacy program
- Product / project management
Medium
- Data classification program
- Data privacy program
- Product / project management
Large
- Data classification program
- Data privacy program
- Product / project management
Enterprise
- Data classification program
- Data privacy program
- Product / project management