SC-42(2): Authorized Use

The sc-42(2): authorized use requirement sits at the seam between privacy governance and security engineering. Many organizations already control “who can access” data, but SC-42(2) asks a different question: “What are you allowed to do with the data once you have it?” That distinction drives your implementation approach. You need explicit, documented authorized purposes for data collected by defined components (for example, applications, services, sensors, telemetry), and you need operational controls that prevent repurposing, secondary use, or opportunistic analytics outside those purposes.

For a Compliance Officer, CCO, or GRC lead, the fastest path is to treat SC-42(2) as a purpose-limitation control with measurable enforcement points. Build a purpose register, map it to datasets and processing jobs, assign owners who can approve or deny new uses, and implement technical constraints that align access paths and workflows to allowed uses. Then run it like an operational program: review new use-cases, monitor access and processing, and retain evidence that the controls are working.

What SC-42(2) requires (plain English)

SC-42(2) requires measures that ensure data or information collected by specified system elements is used only for authorized purposes. In practice, you must (1) clearly define what “authorized purposes” are, (2) restrict systems and staff to those purposes, and (3) maintain proof that unauthorized use is prevented or detected and remediated.

This is not satisfied by a generic “acceptable use” policy alone. Auditors usually look for a direct line from:

• Collected data → approved purpose(s) → authorized users/workflows → enforced controls → logs/reviews showing the controls work.

Regulatory text

“Employ the following measures so that data or information collected by {{ insert: param, sc-42.01_odp }} is only used for authorized purposes: {{ insert: param, sc-42.02_odp }}.” ¹

Operator translation: Identify the collection point(s) and data types in scope, define allowable purposes for that collected data, and implement administrative and technical measures that prevent the data from being used for anything else. Maintain evidence that those measures are consistently applied. ²

Who it applies to (entity and operational context)

Applies to:

• Federal information systems implementing NIST SP 800-53 controls.
• Contractor systems handling federal data where NIST SP 800-53 is contractually required or used as the security control baseline. ¹

Operationally applies to:

• Systems that collect data (apps, APIs, web forms, mobile apps, endpoint telemetry, call recordings, chat transcripts, security monitoring, IoT/sensors).
• Data pipelines that transform or enrich collected data (ETL/ELT, SIEM ingestion, data lake jobs, analytics notebooks, ML training pipelines).
• Third parties that receive collected data for processing (cloud services, managed detection providers, analytics tools). While SC-42(2) is a system control, you typically enforce it through third-party contractual and technical constraints as well.

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

Step 1: Define scope: “data collected by what”

1. List collection points in scope (applications, services, endpoints, sensors, monitoring tools).
2. Identify data elements collected at each point (PII fields, identifiers, device telemetry, logs, content).
3. Assign a system/data owner accountable for approving purposes and exceptions.

Deliverable: a scoped inventory tied to named systems and datasets.

Step 2: Define “authorized purposes” in an enforceable way

Create a Purpose & Use Register with these columns:

• Dataset/data element
• Source/collection point
• Authorized purpose(s) (short, testable statements)
• Allowed processing activities (store, analyze, share, model-train, etc.)
• Allowed recipients (internal roles, specific third parties)
• Prohibited uses (explicit “no” list)
• Approval authority (role/title) and review cadence trigger (events that require re-approval)

Avoid vague purposes like “business operations.” A better purpose is “fraud detection for account takeover,” “security monitoring and incident response,” or “billing reconciliation.”

Step 3: Map purposes to real workflows and access paths

For each authorized purpose, document:

• Which users/roles perform the activity
• Which tools they use (BI tool, SIEM, ticketing, notebook environment)
• Which data stores are accessed
• Which exports/shares are allowed

This becomes your enforcement blueprint: if a workflow is not mapped, it is not authorized.

Step 4: Implement technical guardrails that enforce purpose limits

Pick controls that match how misuse would occur in your environment. Common patterns:

A. Access controls aligned to purpose

• Role-based access where roles correspond to approved purposes (security monitoring team vs marketing analytics).
• Attribute-based access controls where dataset tags (e.g., “SECURITY-ONLY”) restrict use environments.

B. Environment separation

• Separate “security operations” data stores from “analytics” data stores when purposes differ.
• Restrict exports from sensitive zones; require an approval workflow for extracts.

C. Data tagging and policy enforcement

• Tag datasets with allowed purposes and allowed processing environments.
• Enforce via data platform policy engines where available (warehouse policies, lake permissions, DLP rules).

D. Job and pipeline controls

• Require code review/approval for new ETL jobs that introduce a new purpose or new recipient.
• Maintain a registry of scheduled jobs with a documented purpose and owner.

E. Monitoring and logging focused on misuse signals

• Log access to sensitive datasets and high-risk actions (bulk export, new sharing links, cross-domain copies).
• Create alerts for access by roles not mapped to an authorized purpose.

Step 5: Build an exception path you can defend

You will have edge cases (investigations, legal holds, urgent operational needs). Define:

• Who can approve an exception
• What justification is required (link to a ticket/case)
• Time bounds and revocation steps
• Post-action review requirement

Auditors accept exceptions when they are rare, justified, time-bound, and reviewed.

Step 6: Operate it as a recurring control

Minimum operational motions:

• Review new systems/data sources before onboarding into your data platform.
• Review new use-cases and third-party sharing requests against the Purpose & Use Register.
• Periodically test whether the controls actually block unauthorized access or processing.

Daydream (as a GRC workflow layer) fits naturally here by tying SC-42(2) to an owner, a documented procedure, and a recurring evidence request schedule so the control stays “alive” between assessments. ¹

Required evidence and artifacts to retain

Keep artifacts that show definition, enforcement, and operation:

Governance artifacts

• Purpose & Use Register (versioned)
• Data classification and handling standard (where purpose restrictions are recorded)
• RACI: control owner, approvers, data owners

Technical artifacts

• Access control configuration snapshots (role mappings, groups, policies)
• Data platform policy exports (tags, row/column policies, DLP rules)
• Data flow diagrams showing collection → storage → processing → sharing
• Job registry for pipelines with owner + documented purpose

Operational evidence

• Tickets/approvals for new purposes, new recipients, and exceptions
• Sample logs showing monitoring of sensitive data access and exports
• Periodic review results (access reviews, policy reviews) with remediation tickets

Auditor preference: time-stamped evidence tied to a specific dataset and a specific control statement.

Common exam/audit questions and hangups

Expect questions like:

• “Show me where the authorized purposes are documented for this dataset.”
• “How do you prevent a user with database access from exporting data for a different purpose?”
• “How do you approve new analytics use-cases or ML training on collected data?”
• “How do you control third-party processing to the authorized purpose?”
• “Show evidence from the last operating period that access and processing were reviewed.”

Hangup: teams produce a privacy notice or policy statement, but cannot point to enforcement points (permissions, pipeline approvals, monitoring).

Frequent implementation mistakes (and how to avoid them)

1. Writing broad purposes that can’t be tested.
   Fix: require each purpose to map to a named workflow, owner, and system.

2. Treating access control as sufficient.
   Fix: add pipeline/job governance and export/sharing controls. Most misuse happens via secondary processing or extracts.

3. No “prohibited use” list.
   Fix: explicitly document disallowed uses, especially around analytics, profiling, or reuse beyond the collection context.

4. Exceptions handled in chat or email.
   Fix: force exceptions into a ticketing workflow with time bounds and an approver.

5. Evidence scattered across tools.
   Fix: define an evidence binder per key dataset/system; Daydream-style recurring evidence workflows reduce last-minute collection.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement. Practically, the risk shows up as:

• unauthorized secondary use of sensitive data,
• improper sharing with third parties,
• analytics/ML use that exceeds approved purposes,
• audit findings for missing evidence that purpose limits are defined and enforced.

For regulated or federal environments, the immediate consequence is assessment failure or loss of confidence in privacy and data governance controls, which can cascade into contract or authorization risks.

Practical execution plan (30/60/90-day)

First 30 days (foundation)

• Assign a control owner and data owners for in-scope systems.
• Build the first version of the Purpose & Use Register for the highest-risk datasets.
• Document an approval workflow for new purposes and exceptions (ticket-based).
• Identify enforcement points (IAM roles, data platform policies, export controls, pipeline approvals).

Next 60 days (enforcement)

• Implement role and policy changes for priority datasets to align access with authorized purposes.
• Stand up logging for sensitive dataset access and bulk export events.
• Require purpose justification in change management for new pipelines and new data shares.
• Run a tabletop test: attempt an unauthorized use path and confirm it is blocked or detected.

By 90 days (operationalize)

• Expand the register to remaining in-scope datasets and third-party data transfers.
• Run the first recurring access/purpose review and document remediation.
• Produce an “assessment packet” per key system: register extract, controls, sample approvals, sample logs, review outcomes.
• Automate evidence collection where possible; use Daydream to track control operation, owners, and recurring artifacts mapped to SC-42(2). ¹

Frequently Asked Questions

What counts as an “authorized purpose” under SC-42(2)?

An authorized purpose is a documented, approved reason for processing collected data that maps to a real workflow, owner, and system path. If you cannot link a use to an approved purpose and an approver, treat it as unauthorized until approved.

Do I need a separate purpose for every data element?

You need enough granularity to enforce limits. Many teams define purposes at the dataset level, then add tighter purposes for sensitive fields (for example, identifiers, content, or monitoring telemetry).

How do we handle security logs that might be valuable for other analytics?

Document “security monitoring and incident response” as the primary purpose, then require a formal approval before using the same logs for other analytics. Enforce separation by restricting exports or copying into non-security analytics environments.

How does SC-42(2) relate to third parties?

If a third party receives or processes collected data, you need contract terms and technical controls that restrict the third party’s processing to your authorized purposes. Keep approvals and data-sharing records as evidence.

What evidence is strongest in an audit?

A purpose register tied to a specific dataset, plus system-enforced access/policy configurations and a closed-loop approval ticket for a real use-case. Add logs showing access monitoring and a completed review with remediation.

We already have a privacy policy. Why is that not enough?

A policy states intent; SC-42(2) expects measures that constrain actual use. Auditors usually look for enforcement points like permissions, pipeline approvals, export controls, and monitoring aligned to documented purposes. ²

Footnotes

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

2. NIST SP 800-53 Rev. 5