Safeguard 3.7: Establish and Maintain a Data Classification Scheme

Safeguard 3.7 requires you to define a data classification scheme (labels plus handling rules), apply it consistently to the data types your organization creates or stores, and keep it current as systems and data change. To operationalize it fast, publish a short standard, map classes to concrete controls (encryption, access, sharing, retention), and keep repeatable evidence that classification is applied and reviewed ¹.

Key takeaways:

• A “classification scheme” is useless without mandatory handling rules tied to each class ¹.
• Auditors will look for coverage and repeatability: defined classes, scope, owners, and ongoing review evidence ¹.
• Start with a small set of classes and high-risk data stores, then expand based on what you actually inventory and process.

Safeguard 3.7: establish and maintain a data classification scheme requirement is a control-design and control-operations problem. You need a defensible taxonomy (the labels), but you also need operational binding (the required handling for each label) and proof it runs as a program, not a one-time document ¹.

Most organizations stall on classification because they treat it as a policy exercise, or they try to classify everything perfectly on day one. For a CCO, GRC lead, or security compliance owner, the shortest path is to define a scheme that is “good enough to drive controls,” attach it to your real data flows, and build lightweight governance that forces updates when the business adds new systems, new third parties, or new uses of sensitive data.

This page gives requirement-level implementation guidance you can assign, track, and test: who must own the scheme, what steps to execute, what evidence to retain, and the audit questions you should pre-answer. References are limited to the CIS Controls v8 materials provided ¹.

Regulatory text

Framework requirement (excerpt): “CIS Controls v8 safeguard 3.7 implementation expectation (Establish and Maintain a Data Classification Scheme).” ¹

Operator interpretation: You must (1) define data classes in a documented scheme, (2) define how each class must be handled, (3) ensure the organization applies the scheme to relevant data across its environment, and (4) maintain the scheme through periodic review and change control as systems, data types, and risks change ¹.

What an assessor is really testing:

• The scheme exists, is approved, and is communicated.
• The scheme drives actual security and privacy decisions (access, encryption, sharing, storage locations, retention, disposal).
• The scheme stays accurate over time, with evidence of review and updates ¹.

Plain-English requirement meaning

You need a shared language for “how sensitive is this data?” and you must connect that language to mandatory behaviors. A spreadsheet called “Data Classification Policy” is not enough if teams can store regulated data in random collaboration tools without controls.

A workable scheme answers:

1. What classes exist? (Example: Public, Internal, Confidential, Restricted)
2. What data belongs in each class? (Examples and decision rules)
3. What must we do for each class? (Encryption, access control, sharing restrictions, approved repositories, retention/disposal)
4. Who decides and who enforces? (Data owners, IT/SecOps, application owners, Records/Privacy)
5. How do we keep it current? (Review cadence, triggers, change management)

Who it applies to (entity and operational context)

Applies to: Enterprises and technology organizations that store, process, or transmit business data in systems, endpoints, cloud services, and third-party platforms ¹.

Operational contexts where 3.7 shows up immediately:

• Cloud and SaaS sprawl: Teams adopt new tools; classification must drive which tools are approved for which data.
• Third-party data sharing: Classification must set minimum contract and technical controls when data leaves your boundary.
• Security architecture decisions: Zero trust access, encryption standards, logging levels, and DLP depend on class.
• Incident response and breach triage: Class determines severity and notification pathways.

What you actually need to do (step-by-step)

Step 1: Define the classes (keep it small)

Create a classification standard with a limited number of classes your teams can apply consistently. Avoid bespoke classes per department.

Minimum fields to document:

• Class name and short definition
• Examples of data in class
• “Default” rule (what class applies if uncertain)
• Owner (function accountable for the standard)

Practical decision rule: classify based on impact if disclosed/altered/unavailable. Keep the definitions operational, not philosophical.

Step 2: Bind each class to handling requirements (the part most programs miss)

For each class, define required controls in a single “handling matrix” that teams can follow without interpretation.

Include, at minimum:

• Storage: approved systems and prohibited locations (e.g., personal drives)
• Access: minimum authentication strength, role-based access expectations
• Encryption: at rest/in transit requirements (state requirements; do not rely on implied defaults)
• Sharing: internal-only vs allowed with third parties, approval gates
• Retention & disposal: where the retention rule is defined and who owns it
• Logging/monitoring: higher class typically needs stronger monitoring

Deliverable: a one-page table that can be pasted into engineering standards, third-party intake checklists, and procurement requirements.

Step 3: Map the scheme to your data inventory and “crown jewel” data sets

You do not need to classify every file first. You do need to classify:

• Key business systems (customer platforms, finance, HR, product telemetry, support systems)
• Data stores that concentrate sensitive data (data lakes, shared drives, ticketing systems)
• High-risk flows to third parties (payroll, benefits, marketing platforms, analytics, outsourcing)

Create a “classification coverage map”:

• System / repository
• Data types stored
• Assigned class (or range)
• Data owner
• Enforcement controls in place (links to standards or configs)

Step 4: Operationalize assignment (who labels what, when)

Pick the mechanism that fits your environment:

• System-level classification: classify an application/database as “Restricted,” then enforce requirements at the system boundary.
• Object-level classification: label files/emails/records, typically where DLP and collaboration tooling supports it.

Define triggers:

• New system onboarding requires a class before production use.
• New third-party engagement requires class for shared data.
• Major product changes require a re-evaluation.

Step 5: Enforce through workflows and guardrails

Your scheme becomes real when it is embedded in:

• Procurement / third-party due diligence: intake form asks “what class of data will be shared?”
• Architecture reviews: design cannot pass without mapping class to required controls
• Access requests: higher-class data requires owner approval and stronger authentication
• Approved tools list: collaboration and storage tools are tiered by class they are allowed to hold

If you use Daydream for third-party risk, build the classification question into every intake so you can drive contractual security requirements and evidence collection based on the class of data a third party touches. That is the fastest way to make classification “stick” in operational reality.

Step 6: Maintain it (review + change control + training)

Maintenance is where audit findings happen. Put the scheme under document control and define:

• Review owner and approvers
• Review triggers (new regulations, new data types, new repositories, major incidents)
• How exceptions are approved, time-bounded, and tracked to closure

Train only what people must do:

• How to choose a class
• Where to store each class
• What tools are allowed
• Who to ask when unsure

Required evidence and artifacts to retain

Keep evidence that shows design, adoption, and maintenance:

Design artifacts

• Data Classification Standard (approved, versioned)
• Data Handling Matrix tied to classes
• RACI: data owners, security, privacy, IT, records management responsibilities

Operational artifacts

• Classification coverage map (systems/repositories and assigned classes)
• Third-party intake records showing classification for shared data
• Architecture review or change tickets showing class selection and required controls
• Exception register (business justification, compensating controls, expiry date, approval)

Maintenance evidence

• Review logs (meeting notes, approvals, change history)
• Training acknowledgments or targeted communications
• Sampling results: periodic checks that repositories follow handling rules (document the method and outcomes)

Common exam/audit questions and hangups

Expect questions like:

1. “Show me your data classification policy/standard and who approved it.”
2. “How do you ensure teams apply classification to new systems and third parties?”
3. “Give examples where classification changed security requirements.” (e.g., tool selection, encryption, access)
4. “How do you handle exceptions?” Auditors look for time-bounded exceptions with compensating controls.
5. “How do you keep the scheme current?” Provide change history and review evidence.

Hangups:

• “We have labels, but no handling rules.” That is a control gap because labels do not change behavior.
• “We classify data, but we can’t show where.” If you cannot map classes to systems and flows, you cannot demonstrate coverage.

Frequent implementation mistakes (and how to avoid them)

1. Too many classes or unclear definitions. Fix: reduce to a small set; add examples and a default rule.
2. No linkage to technical controls. Fix: publish the handling matrix and make it mandatory in architecture/procurement gates.
3. Treating file-by-file labeling as the only approach. Fix: use system-level classification for major repositories; apply object-level labels where tooling supports it.
4. Ignoring third parties. Fix: require class selection during third-party intake and contract addenda; store evidence centrally (Daydream fits naturally here).
5. No maintenance mechanism. Fix: put ownership and review triggers in writing; keep a change log.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement, so this page does not cite enforcement actions.

Risk implication for operators: weak classification causes inconsistent handling. That shows up as preventable exposure through misconfigured sharing, uncontrolled SaaS adoption, and unclear contract/security requirements for third parties. In assessments, it often becomes a “foundational control” finding because other controls depend on knowing what data is sensitive.

A practical 30/60/90-day execution plan

First 30 days (foundation + fast adoption points)

• Appoint an owner and approvers for the classification standard.
• Draft classes and the handling matrix (one page).
• Identify initial in-scope systems: top business applications, shared repositories, and core third-party data flows.
• Add a mandatory “data class” question to third-party intake and new system intake.
• Publish quick guidance: “Where can I store each class?”

Days 31–60 (coverage mapping + enforcement hooks)

• Build the classification coverage map for priority systems and repositories.
• Update architecture review checklists to require class-to-controls mapping.
• Define exception process and register.
• Run a small sampling check: pick a handful of repositories and confirm handling matches class; document results and fixes.

Days 61–90 (maintenance + scale)

• Expand coverage map to additional systems and data flows.
• Tune controls where gaps appear (access, encryption, tool restrictions).
• Run training for data owners and system owners.
• Schedule the first formal review and record the outcome (even if the outcome is “no changes”).

Frequently Asked Questions

Do I need to label every file and email to meet safeguard 3.7?

No. Many organizations meet the intent by classifying systems and repositories first, then using object-level labels where tooling and workflows support it. The key is consistent handling rules tied to the class ¹.

How many classification levels should we have?

Use the smallest set that drives distinct handling rules and decisions. If two levels have the same controls, merge them to reduce confusion and misclassification.

Who should be the “data owner” for classification decisions?

Assign ownership to the business function accountable for the data’s use and risk (e.g., HR for HR data), with Security/Privacy providing standards and oversight. Document the RACI so exceptions and disputes have a decision path.

How do we operationalize classification for third parties?

Require a data class selection during third-party intake, then map that class to minimum contract clauses and technical requirements (encryption, access controls, breach notice, data return/destruction). Store the intake record and approvals as evidence.

What evidence is most persuasive in an audit?

A current classification standard, a handling matrix, and a coverage map showing key systems and third-party flows with assigned classes. Add review/change history and a small set of completed samples that prove the rules are followed.

What if teams disagree on the right class for a dataset?

Use a documented escalation: data owner proposes, Security/Privacy advises based on impact, and a designated risk owner resolves. Track the decision and revisit if the data use changes.

Footnotes

1. CIS Controls v8; CIS Controls Navigator v8