Control statement
Employ [Organization-defined: techniques] for [Organization-defined: data storage objects] to detect and protect against unauthorized data mining.
Discussion
Data mining is an analytical process that attempts to find correlations or patterns in large data sets for the purpose of data or knowledge discovery. Data storage objects include database records and database fields. Sensitive information can be extracted from data mining operations. When information is personally identifiable information, it may lead to unanticipated revelations about individuals and give rise to privacy risks. Prior to performing data mining activities, organizations determine whether such activities are authorized. Organizations may be subject to applicable laws, executive orders, directives, regulations, or policies that address data mining requirements. Organizational personnel consult with the senior agency official for privacy and legal counsel regarding such requirements. Data mining prevention and detection techniques include limiting the number and frequency of database queries to increase the work factor needed to determine the contents of databases, limiting types of responses provided to database queries, applying differential privacy techniques or homomorphic encryption, and notifying personnel when atypical database queries or accesses occur. Data mining protection focuses on protecting information from data mining while such information resides in organizational data stores. In contrast, [AU-13](#au-13) focuses on monitoring for organizational information that may have been mined or otherwise obtained from data stores and is available as open-source information residing on external sites, such as social networking or social media websites. [EO 13587](#0af071a6-cf8e-48ee-8c82-fe91efa20f94) requires the establishment of an insider threat program for deterring, detecting, and mitigating insider threats, including the safeguarding of sensitive information from exploitation, compromise, or other unauthorized disclosure. Data mining protection requires organizations to identify appropriate techniques to prevent and detect unnecessary or unauthorized data mining. Data mining can be used by an insider to collect organizational information for the purpose of exfiltration.
Organization-defined parameters
These values must be resolved through the organization’s tailoring and governance process. Bracketed parameter references in the control text identify where a decision is required.
From control text to operational evidence
Use Data Mining Protection as a testable risk decision. Translate the official statement into accountable people, repeatable processes, configured technology, and evidence that demonstrates the outcome over time. In this family, pay particular attention to identity, authorization, least privilege, session boundaries, and access lifecycle governance.
Implementation workflow
- Define the control boundary, responsible owner, inherited portions, and systems or processes in scope.
- Resolve each organization-defined parameter before declaring the control implemented.
- Document how the implementation satisfies every clause of the official control statement.
- Collect evidence as a normal byproduct of operation rather than only before an assessment.
- Review exceptions, changes, and monitoring results on a risk-based cadence.
Evidence examples
- access approvals and entitlement records
- role and group configuration exports
- periodic access review results
- authentication and authorization logs
Common failure patterns
- standing privileges that outlive business need
- shared or orphaned accounts
- access rules implemented differently across systems
- approvals that cannot be traced to actual permissions
Questions practitioners should ask
- What risk decision is this control intended to support in this system?
- Which parts are implemented locally, inherited, shared, or not applicable—and what evidence supports that decision?
- Do the documented narrative, deployed configuration, operating process, and collected evidence agree?
- What event or threshold requires the implementation to be reviewed or changed?
Assessment objectives and methods
Show the assessment objective
[Organization-defined: techniques] are employed for [Organization-defined: data storage objects] to detect and protect against unauthorized data mining.
Examine
- Access control policy
- procedures for preventing and detecting data mining
- policies and procedures addressing authorized data mining techniques
- procedures addressing protection of data storage objects against data mining
- system design documentation
- system configuration settings and associated documentation
- system audit logs
- system audit records
- procedures addressing differential privacy techniques
- notifications of atypical database queries or accesses
- documentation or reports of insider threat program
- system security plan
- privacy plan
- other relevant documents or records
Interview
- Organizational personnel with responsibilities for implementing data mining detection and prevention techniques for data storage objects
- legal counsel
- organizational personnel with information security and privacy responsibilities
- system developers
Test
- Mechanisms implementing data mining prevention and detection
Related controls
These relationships come from the official OSCAL catalog. They indicate useful dependencies or context, not automatic inheritance or equivalence.
Related defensive techniques
D3FEND maps this base control or one of its enhancements to the following defensive techniques. The ontology relation label is preserved and does not by itself prove implementation or effectiveness.
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Authoritative sources
Bare Metal Cyber is an independent educational publisher and is not affiliated with or endorsed by NIST. Official control requirements and interpretations remain with NIST and the responsible authorizing organization.