SI-19(2): Archiving

To meet the si-19(2): archiving requirement, you must prevent personally identifiable information (PII) from being included in archived datasets when those PII elements will not be needed after archiving. Operationally, this means defining “archive purpose,” stripping unnecessary PII before archival, and proving the control runs consistently across systems and third parties that handle federal data. ¹

Key takeaways:

• Define archival purpose and “PII needed post-archive” criteria for each dataset before it can be archived. ¹
• Build a technical and procedural gate: redact/tokenize/drop non-required PII fields prior to archive write. ¹
• Keep assessor-ready evidence: data maps, archive manifests, transformation logs, and exception approvals tied to each archive job. ¹

SI-19(2) is a narrowly scoped control enhancement with a very practical intent: archived data tends to live “forever,” spreads easily, and is rarely re-reviewed. If you archive datasets that contain PII you do not need for the archived purpose, you create silent breach exposure, discovery risk, and unnecessary compliance scope. SI-19(2) forces a disciplined question at the moment of archiving: “Which PII elements must remain in the archive for a defined, legitimate post-archive need?” Anything else must not be archived. ¹

For a CCO or GRC lead, the fastest path to operationalizing SI-19(2) is to treat “archiving” as a controlled data-processing event with clear entry criteria, field-level handling rules, and auditable outputs. You will need both governance (definitions, ownership, exceptions) and engineering execution (ETL transforms, DLP rules, schema controls, immutable logging). The common failure mode is “we have retention,” but no field-level proof that unnecessary PII is excluded from archives. SI-19(2) tests that exact gap. ¹

Regulatory text

Requirement (verbatim): “Prohibit archiving of personally identifiable information elements if those elements in a dataset will not be needed after the dataset is archived.” ¹

Operator translation:
You need a rule, enforced in process and technology, that prevents “extra” PII from entering archives. The decision is field-level, not just dataset-level: some parts of a dataset may be needed for the archive’s purpose, while other PII elements are not. If an element is not needed post-archive, it must be removed, redacted, tokenized, or otherwise excluded prior to archiving. ¹

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Plain-English interpretation (what the control is really testing)

Assessors are looking for three things:

1. You can identify PII elements at the attribute/field level. If you cannot enumerate which fields are PII, you cannot comply consistently. ¹
2. You have a defined “need” for PII in the archived state. “We might need it someday” is not a need. Document the specific post-archive use cases (e.g., statutory recordkeeping, audit reconstruction, fraud investigations) and tie them to allowed fields. ¹
3. You actively prevent prohibited PII from being archived and can prove it. Policy alone fails if engineering pipelines still write full-fidelity production records into object storage “cold” tiers. ¹

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Who it applies to (entity and operational context)

Entity scope: This control commonly applies in federal information systems and contractor systems that handle federal data, including systems operated by third parties under federal contracts. ²

Operational scope (where it shows up in real environments):

• Data warehouses and lakehouses where older partitions are moved to low-cost storage.
• Backup-to-archive workflows (snapshot exports, long-term backups treated as archives).
• Case management and ticketing exports retained for long periods.
• Email, collaboration, and messaging exports placed into archives for eDiscovery-like needs.
• SIEM and security data lakes that ingest logs containing PII (user identifiers, IP-to-person mappings, HR identifiers).
• Third-party archives (managed backup providers, managed data platforms) where your data is archived outside your direct control.

A practical boundary: SI-19(2) triggers when you intentionally store data for long-term retrieval outside the primary system of record. If your organization treats backups as archives, assume it applies there too, and document that interpretation. ¹

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

1) Assign ownership and define “archive” in your environment

• Name a control owner (often Security Engineering, Data Platform, or Records/Privacy with IT execution).
• Define what counts as an archive versus backup, replica, or cache in your data lifecycle policy.
• Create a system list: which platforms write archives (object storage buckets, tape, archive vaults, cold tiers). ¹

Deliverable: “Archiving scope statement” plus system inventory entries for archive stores.

2) Build field-level PII inventory for datasets that get archived

For each dataset eligible for archiving:

• Identify the PII elements (columns/fields/log attributes).
• Tag them with data classification and sensitivity.
• Identify post-archive purpose and the minimum fields required to satisfy it. ¹

Tip: If teams cannot agree on the minimum fields, force a decision through an exception workflow. No decision means no archive.

3) Create an “allowed-in-archive” field list (and default deny)

Implement a simple rule set per dataset:

• Allowed fields: necessary for the stated post-archive purpose.
• Prohibited fields: PII elements not required post-archive.
• Conditional fields: allowed only when a documented scenario applies (e.g., legal hold). ¹

This becomes your enforcement spec for engineering.

4) Implement the technical gate before archive write

Common patterns that satisfy SI-19(2) if executed with evidence:

• ETL transformation: a dedicated archival job that drops prohibited columns and writes a separate archive schema.
• Tokenization/pseudonymization: replace identifiers with tokens when the archive only needs linkage, not raw identifiers.
• Redaction: mask free-text fields that might contain PII (requires stronger testing and sampling).
• DLP-based blocking: prevent writes to archive storage when prohibited PII patterns are detected (useful as a backstop, not the primary control). ¹

Engineering acceptance criteria (make it testable):

• Archive jobs fail closed if schema unexpectedly includes prohibited PII fields.
• Logs record the transformation version, dataset ID, and output location.
• Changes to archival schemas require review (data owner + privacy/security). ¹

5) Handle exceptions with tight boundaries

You will have edge cases: investigations, litigation holds, regulatory inquiries.

• Require written justification of why specific PII elements are needed post-archive.
• Time-bound the exception where feasible.
• Add compensating controls: stronger encryption, stricter access, separate vault, separate key management, separate retention schedule. ¹

6) Operational monitoring and periodic review

• Monitor archive job runs for failures, drift, or schema changes.
• Sample archived outputs to validate prohibited fields are absent.
• Re-approve archive field lists when the dataset purpose changes. ¹

How Daydream fits naturally: Many programs fail SI-19(2) because ownership, dataset mapping, and recurring evidence are scattered across tickets, wikis, and pipelines. Daydream becomes the system of record that ties the control owner, procedure, dataset-by-dataset archive rules, and recurring evidence artifacts into a single assessor-ready trail, aligned to SI-19(2). ¹

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Required evidence and artifacts to retain

Maintain these artifacts in a place your audit team can access without chasing engineers:

1. Policy / standard

   • Data retention and archiving standard defining archive, PII handling, and SI-19(2) requirements. ¹

2. Dataset-level archive decision records

   • Dataset inventory showing which datasets are archived.
   • For each dataset: archive purpose, allowed PII fields, prohibited fields, owner approval. ¹

3. Technical implementation evidence

   • ETL/job code references or configuration exports that drop/tokenize/redact prohibited PII.
   • Schema definitions for “archive tables” versus “production tables.”
   • DLP rules (if used) and test results. ¹

4. Run-time proof

   • Archive job run logs, pipeline execution logs, and error reports.
   • Sample archive manifests (file lists) with checksums or integrity markers if you use them.
   • Access logs for archive storage, especially for exception archives. ¹

5. Exception records

   • Approved exceptions with scope, justification, and compensating controls. ¹

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Common exam/audit questions and hangups

“Show me how you determine which PII elements are needed post-archive.”
Hangup: teams answer with retention schedules, not field-level need. Provide the dataset-level “allowed-in-archive” lists and approvals. ¹

“How do you prevent engineers from archiving full datasets by default?”
Hangup: no technical guardrail. Show the pipeline gate (schema enforcement, transformation step, or storage policy) and a failed-job example when prohibited fields appear. ¹

“Do backups count?”
Hangup: inconsistent definitions. Document your definition of archiving and how backups are handled under your data lifecycle policy, then show the same PII-minimization intent where applicable. ¹

“What about third parties that archive on your behalf?”
Hangup: contracts say “secure,” but do not specify field-level minimization. Show DPAs/SOW language, technical specs you provide, and evidence they follow your archive schema. ²

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Frequent implementation mistakes (and how to avoid them)

1. Mistake: Treating the archive as a storage tier, not a data product.
   Fix: require an archive schema and explicit allowed fields for each dataset. ¹

2. Mistake: “We encrypt archives” as the primary answer.
   Encryption is necessary but does not meet the prohibition. You still archived unneeded PII. Fix: remove unnecessary PII first, then encrypt what remains. ¹

3. Mistake: No exception workflow.
   Teams will bypass controls for urgent investigations. Fix: create an emergency exception path with retroactive approval and strong logging. ¹

4. Mistake: Free-text fields ignored (notes, descriptions, chat transcripts).
   These often contain PII that column tagging misses. Fix: treat free-text as high-risk; either exclude it from archives by default or apply redaction with validation sampling. ¹

5. Mistake: Evidence exists, but isn’t repeatable.
   A one-time screenshot does not show ongoing operation. Fix: define recurring evidence outputs per archive job (logs, manifests, schema checks). Daydream can track these recurring artifacts against SI-19(2) so audits don’t become a scavenger hunt. ¹

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Enforcement context and risk implications

No public enforcement cases were provided for this requirement in the source catalog, so do not plan on “case law” as your internal justification.

Operational risk still stacks up quickly:

• Breach impact expansion: unnecessary PII in archives increases affected population if archive storage is accessed or exfiltrated.
• Discovery and records risk: archived PII can become subject to legal discovery holds and internal investigations.
• Assessment failure risk: SI-19(2) is easy to test by inspecting archived schemas and sample files. If you cannot show field-level minimization, you will struggle to defend compliance. ¹

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Practical 30/60/90-day execution plan

First 30 days (triage and control design)

• Confirm which systems and teams perform archiving; document your “archive” definition.
• Identify highest-risk archive stores (object storage buckets, data lake cold tiers, managed backup vaults).
• Choose an operating model: dataset-level allowed field lists + technical enforcement gate.
• Stand up an exception workflow with required approvals and logging. ¹

By 60 days (implement on priority datasets and prove it works)

• Build PII field inventory and archive purpose for the most material archived datasets.
• Implement archive transformation pipelines (drop/tokenize/redact) and schema validation to fail closed.
• Produce initial evidence pack: approvals, configs, run logs, and sample outputs. ¹

By 90 days (scale and operationalize)

• Expand coverage across remaining archived datasets and any third-party archiving arrangements.
• Add monitoring for schema drift and archive job failures.
• Formalize recurring evidence collection in your GRC system. If you need a single place to map SI-19(2) ownership, procedures, and recurring artifacts, Daydream is a natural fit. ¹

Frequently Asked Questions

Does SI-19(2) mean I can never archive PII?

No. It prohibits archiving PII elements that are not needed after the dataset is archived. You can archive the minimum PII required for a defined post-archive purpose, and you should document that purpose and the allowed fields. ¹

What qualifies as “needed” after archiving?

“Needed” should tie to a documented post-archive use case (audit reconstruction, statutory recordkeeping, investigations) and be approved by the data owner and privacy/security. If you cannot articulate a purpose for a specific PII field, treat it as prohibited for the archive. ¹

Are backups in scope for the si-19(2): archiving requirement?

SI-19(2) speaks to archiving; whether backups are treated as archives depends on your lifecycle definitions and how backups are retained and accessed. If backups effectively function as long-term archives in your environment, apply the same PII minimization logic and document the decision. ¹

How do I enforce this technically in a data lake?

Create an archive-specific write path that outputs a minimized schema, and block direct writes of full-fidelity datasets into archive storage. Add schema checks so the job fails if prohibited PII fields appear, and retain the run logs as evidence. ¹

What about unstructured archives like PDFs, emails, or chat exports?

Treat unstructured content as high-risk because PII is harder to identify and remove. If you must archive it, define tighter access controls and retention rules, and prefer excluding unneeded content types rather than assuming redaction is perfect. ¹

How do I handle third parties that perform archiving for us?

Push your archive schema and “allowed PII fields” rules into contracts, SOWs, and technical specifications, then collect proof of operation (job configs, manifests, sample outputs). Your compliance burden remains even if a third party operates the archive. ²

Footnotes

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

2. NIST SP 800-53 Rev. 5