PT-2(1): Data Tagging

PT-2(1): data tagging requirement means you must attach defined tags (your organization’s specified tag content) to the defined data objects (your organization’s specified data types/fields) so systems and staff can consistently identify, handle, share, retain, and protect data based on those tags. Operationalize it by standardizing a tag schema, implementing tagging at creation/ingest, and enforcing tag-aware controls downstream.

Key takeaways:

• Define a single enterprise tagging schema tied to handling rules (classification, sensitivity, purpose, retention, distribution).
• Implement tagging at the earliest point (creation, collection, or ingestion), then propagate tags across storage, pipelines, and exports.
• Keep evidence that tags are applied, validated, and enforced (schemas, configs, samples, logs, and recurring tests).

Compliance teams usually inherit “tagging” as a loose concept: someone labels a spreadsheet “Confidential,” a data lake has a few columns, and the DLP tool guesses the rest. PT-2(1) is narrower and more operational: you must attach data tags containing organization-defined information to organization-defined data elements, consistently enough that downstream safeguards can rely on them. The hard part is not inventing labels. It’s making tags durable across systems, integrations, and third parties while keeping them accurate over time.

For a CCO, Compliance Officer, or GRC lead, the fastest path is to treat PT-2(1) as a requirement to (1) define the tag content, (2) define what must be tagged, (3) implement attachment and propagation mechanisms, and (4) prove it works with repeatable evidence. If you already run data classification, privacy inventories, DLP, or retention programs, PT-2(1) becomes the connective tissue that makes those controls enforceable at scale. If you don’t, this control is where you start building that foundation without boiling the ocean.

Regulatory text

NIST requirement (excerpt): “Attach data tags containing {{ insert: param, pt-02.01_odp.01 }} to {{ insert: param, pt-02.01_odp.02 }}.” ¹

Operator interpretation: NIST is explicitly parameterizing two decisions you must make and document:

1. What information goes in the tag (the “tag content” parameter).
2. What data must receive tags (the “data objects/elements” parameter).
   Once defined, you must implement a mechanism to attach those tags to the specified data and keep the mechanism operating in production.

What “data tags” means in practice

A “tag” can be:

• Metadata fields in a database (e.g., data_classification=restricted, purpose=billing)
• Object metadata in cloud storage (e.g., S3 object tags / Azure blob index tags)
• File labels (e.g., document classification labels in productivity suites)
• Data catalog labels linked to datasets/columns
• Message headers/attributes in queues/streams
• Embedded labels in structured formats (where appropriate)

PT-2(1) does not force one technology. It forces consistency and demonstrability. ²

Plain-English interpretation (what you are being asked to do)

You must be able to answer, reliably and with evidence:

• Which data is which (by category/classification/purpose/owner), based on tags you defined.
• Where the tags live (metadata, headers, catalog, or file labels) and how they follow the data.
• What controls rely on the tags (access control, DLP rules, retention, sharing restrictions, encryption requirements, monitoring).

If tagging exists only as a policy statement or a wiki page, you will struggle in assessment. Auditors will look for tag schema, technical enforcement, and proof that real data is tagged.

Who it applies to (entity and operational context)

PT-2(1) is commonly applied in:

• Federal information systems implementing NIST SP 800-53 controls ²
• Contractor systems handling federal data where NIST SP 800-53 is contractually flowed down or used as the control baseline ²

Operationally, this requirement hits teams that handle:

• Data platforms (data lakes/warehouses, ETL/ELT pipelines)
• SaaS content systems (email, documents, ticketing)
• Application teams producing/processing regulated or sensitive data
• Identity/access management and security tooling (DLP, CASB, SIEM)
• Records management and privacy operations (retention, purpose limitation)

Third party angle: if you send data to third parties, tags often need to persist into exports or be translated into contractual handling instructions.

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

Step 1: Define your tag schema (tag content parameter)

Create a Data Tagging Standard that defines:

• Tag keys and allowed values (controlled vocabulary)
• Meaning and handling rules for each value
• Who can assign/override tags, and approval for exceptions
• Default tags when confidence is low (e.g., “unclassified/unknown” with restricted sharing)

A practical minimum set many programs start with:

• Classification/sensitivity (e.g., Public, Internal, Confidential, Restricted)
• Data type (e.g., HR, Finance, Customer Support)
• Purpose of processing (privacy-aligned where applicable)
• Retention category (record series / retention schedule mapping)
• Residency/transfer restriction flags (if relevant)

Keep it tight. Too many tags causes inconsistent assignment and breaks automation.

Step 2: Define the tagging scope (data elements parameter)

Document what must be tagged, using scoping tiers:

• Tier 1 (mandatory): datasets/systems that store or process sensitive data
• Tier 2 (next): shared collaboration platforms and high-volume pipelines
• Tier 3: low-risk or ephemeral data (still documented, lighter enforcement)

Be explicit about the unit of tagging:

• Dataset-level, table-level, column-level, object/file-level, message-level, or record-level
  Choose the lowest level needed for real control decisions. Over-granularity stalls delivery.

Step 3: Choose attachment points (where tags get applied)

Implement tagging at the earliest practical point:

• At creation (documents/emails generated with a label)
• At collection (web forms/API ingestion attaches purpose, system, owner)
• At ingest (ETL stamps metadata onto datasets/objects)
• At import from third parties (supplier-provided labels mapped into your schema)

Write down the “tagging choke points” you control, then make them mandatory in those workflows.

Step 4: Implement technical mechanisms to attach and persist tags

Typical patterns:

• Cloud object stores: object tags + bucket policies that enforce required tag keys on upload.
• Data warehouse/lake: metadata tables / data catalog labels + pipeline steps that fail builds when tags missing.
• Productivity suites: mandatory labeling policies and templates.
• APIs/events: required metadata fields; reject or quarantine payloads without tags.

Key design choice: propagation rules. If a pipeline joins two datasets, decide how tags combine (e.g., “highest sensitivity wins,” “restricted purpose blocks downstream use”).

Step 5: Bind tags to controls (so tagging matters)

If tags don’t drive controls, they become decorative. Map each tag to at least one enforcement action:

• Access control rules (role-based access by classification)
• DLP/CASB policies (block external sharing for Restricted)
• Encryption requirements (enforce for Confidential/Restricted)
• Retention and deletion workflows (tag -> retention schedule)
• Monitoring priorities (tag -> alert routing/response SLA)

Maintain a Tag-to-Control Mapping table that auditors can read in minutes.

Step 6: Validate tagging quality and fix drift

Put in recurring checks:

• Completeness: required objects have required tag keys.
• Accuracy: sample-based verification against data discovery results and system owners.
• Drift: detect when schemas change, new pipelines appear, or tags stop propagating.

If you use Daydream for third-party risk and control operations, treat PT-2(1) as a requirement object with: owner, procedure, systems in scope, and a recurring evidence schedule so audits don’t become a scavenger hunt.

Required evidence and artifacts to retain

Assessors typically want “policy, procedure, implementation, operation.” Retain:

• Data Tagging Standard (schema, definitions, allowed values, ownership)
• Scope register (systems/datasets in scope and tagging level)
• Tag-to-Control Mapping (how tags change access/sharing/retention)
• System configurations showing enforcement (screenshots/exports of policies, IaC snippets, platform configs)
• Pipeline/job definitions that attach/propagate tags (code excerpts, job configs)
• Sample evidence: exports showing real objects with tags (redacted where needed)
• Operational logs/reports: exceptions, failed uploads due to missing tags, remediation tickets
• Periodic test results: tagging completeness checks and outcomes
• Third party data handling specs where tags must be preserved or translated

Common exam/audit questions and hangups

Expect these:

• “What exactly are your organization-defined tags and values?”
• “Which data must be tagged, and where is that scope documented?”
• “Show me three examples of sensitive datasets and the tags attached.”
• “Where is tagging enforced technically versus manually?”
• “How do tags propagate through ETL and exports?”
• “How do you prevent users or pipelines from bypassing tagging?”
• “What happens when data is derived or combined?”

Hangup: teams show a data catalog label but cannot prove downstream enforcement (sharing, access, retention). Close that gap with the tag-to-control mapping plus control configs.

Frequent implementation mistakes (and how to avoid them)

Mistake	Why it fails	What to do instead
Too many tag values	Users guess; automation breaks	Start with a small controlled vocabulary and add only when a control needs it
Tagging only in one system	Tags get lost in exports and pipelines	Define propagation and implement at ingestion + storage + egress points
No “required tags” enforcement	Missing tags become normal	Reject/quarantine untagged data and track exceptions
Manual tagging for high-volume data	Doesn’t scale	Automate at creation/ingest; allow manual override with approval
Tags not tied to controls	Auditors see labels without impact	Map tags to access, DLP, retention, and monitoring behaviors
No evidence cadence	You scramble at audit time	Schedule recurring reports/samples as standing evidence

Risk implications (why auditors care)

PT-2(1) is a prerequisite for reliable privacy and security decisions. Without tags:

• DLP policies become generic and noisy.
• Retention and deletion become inconsistent.
• Data sharing with third parties becomes harder to constrain and evidence.
• Incident response spends time figuring out what the data was, instead of containing exposure.

Your biggest compliance risk is not “no tags.” It’s no proof that tagging is applied and used.

Practical 30/60/90-day execution plan

First 30 days (foundation and scoping)

• Assign a control owner and backups (Security/GRC + Data Platform).
• Publish a one-page tagging schema draft (keys, values, definitions).
• Inventory in-scope systems and select initial Tier 1 targets (highest-risk repositories and pipelines).
• Decide attachment points and propagation rules for Tier 1.
• Define evidence you will produce (config exports, sample tagged objects, completeness report).

Days 31–60 (implement and enforce in Tier 1)

• Implement required tag keys in Tier 1 platforms (upload/ingest gating where feasible).
• Configure at least one tag-driven control per Tier 1 platform (e.g., block external sharing for Restricted).
• Build a basic completeness report (tag coverage by repository/dataset).
• Create an exceptions workflow (who approves, how long exceptions last, where tracked).

Days 61–90 (expand, test, and operationalize)

• Expand tagging to Tier 2 systems based on risk and data flows.
• Test propagation across a full path (ingest → transform → analytics → export).
• Run a recurring review with data owners: accuracy sampling and remediation.
• Formalize evidence collection and package it for audit (Daydream can track the procedure, owners, and recurring artifacts so evidence stays current).

Frequently Asked Questions

Do we have to tag every single record, or is dataset-level tagging enough?

PT-2(1) allows organization-defined scope, so you choose what must be tagged and at what level. Pick the lowest level that your access, sharing, and retention decisions actually require, then document that choice and apply it consistently. ²

What counts as an acceptable “data tag” for auditors?

Auditors generally accept metadata that is attached to the data object and persists through handling, such as object tags, database metadata fields, catalog labels with enforced propagation, or document labels. What matters is that the tag content and the tagged data elements are defined and demonstrably implemented. ¹

We already have data classification. Is that the same as PT-2(1)?

Classification is often one tag dimension, but PT-2(1) is broader: it requires you to attach tags with defined content to defined data elements. If classification exists only in policy and not attached to the data in systems, you still have a PT-2(1) gap. ²

How do we handle derived datasets that combine multiple sources?

Define deterministic propagation rules (for example, highest sensitivity wins, and purposes must be compatible) and enforce them in pipelines. Keep a record of the rule and show an example where a derived dataset received the expected tags.

What do we do with third parties that cannot accept our tag format?

Translate tags into contractual handling instructions and export manifests (e.g., a data dictionary or header fields) that preserve the meaning of the tag. Document the mapping and keep evidence in the third-party onboarding package and data exchange runbooks.

What evidence is strongest for PT-2(1) during an assessment?

A tagging standard, a clear scope list, enforced required tags in at least your highest-risk repositories, and samples showing tagged objects plus logs/reports proving tagging is operating. Pair that with a tag-to-control mapping that shows tags drive real restrictions. ²

Footnotes

1. NIST SP 800-53 Rev. 5 OSCAL JSON

2. NIST SP 800-53 Rev. 5