Guidelines for Data Classification
The purpose of this Guideline is to establish a framework for classifying institutional data based on its level of sensitivity, value and criticality to the University as required by the University's Information Security Policy. Classification of data will aid in determining baseline security controls for the protection of data.
This Policy applies to all faculty, staff and third-party Agents of the University as well as any other University affiliate who is authorized to access Institutional Data. In particular, this Guideline applies to those who are responsible for classifying and protecting Institutional Data, as defined by the Information Security Roles and Responsibilities.
Confidential Data is a generalized term that typically represents data classified as Restricted, according to the data classification scheme defined in this Guideline. This term is often used interchangeably with sensitive data.
A Data Steward is a senior-level employee of the University who oversees the lifecycle of one or more sets of Institutional Data. See the Information Security Roles and Responsibilities for more information.
Institutional Data is defined as all data owned or licensed by the University.
Non-public Information is defined as any information that is classified as Private or Restricted Information according to the data classification scheme defined in this Guideline.
Sensitive Data is a generalized term that typically represents data classified as Restricted, according to the data classification scheme defined in this Guideline. This term is often used interchangeably with confidential data.
Data classification, in the context of information security, is the classification of data based on its level of sensitivity and the impact to the University should that data be disclosed, altered or destroyed without authorization. The classification of data helps determine what baseline security controls are appropriate for safeguarding that data. All institutional data should be classified into one of three sensitivity levels, or classifications:
|Data should be classified as Restricted when the unauthorized disclosure, alteration or destruction of that data could cause a significant level of risk to the University or its affiliates. Examples of Restricted data include data protected by state or federal privacy regulations and data protected by confidentiality agreements. The highest level of security controls should be applied to Restricted data.|
|Data should be classified as Private when the unauthorized disclosure, alteration or destruction of that data could result in a moderate level of risk to the University or its affiliates. By default, all Institutional Data that is not explicitly classified as Restricted or Public data should be treated as Private data. A reasonable level of security controls should be applied to Private data.|
|Data should be classified as Public when the unauthorized disclosure, alteration or destruction of that data would result in little or no risk to the University and its affiliates. Examples of Public data include press releases, course information and research publications. While little or no controls are required to protect the confidentiality of Public data, some level of control is required to prevent unauthorized modification or destruction of Public data.|
Classification of data should be performed by an appropriate Data Steward. Data Stewards are senior-level employees of the University who oversee the lifecycle of one or more sets of Institutional Data. See Information Security Roles and Responsibilities for more information on the Data Steward role and associated responsibilities.
Data Stewards may wish to assign a single classification to a collection of data that is common in purpose or function. When classifying a collection of data, the most restrictive classification of any of the individual data elements should be used. For example, if a data collection consists of a student's name, address and social security number, the data collection should be classified as Restricted even though the student's name and address may be considered Public information.
On a periodic basis, it is important to reevaluate the classification of Institutional Data to ensure the assigned classification is still appropriate based on changes to legal and contractual obligations as well as changes in the use of the data or its value to the University. This evaluation should be conducted by the appropriate Data Steward. Conducting an evaluation on an annual basis is encouraged; however, the Data Steward should determine what frequency is most appropriate based on available resources. If a Data Steward determines that the classification of a certain data set has changed, an analysis of security controls should be performed to determine whether existing controls are consistent with the new classification. If gaps are found in existing security controls, they should be corrected in a timely manner, commensurate with the level of risk presented by the gaps.
The goal of information security, as stated in the University's Information Security Policy, is to protect the confidentiality, integrity and availability of Institutional Data. Data classification reflects the level of impact to the University if confidentiality, integrity or availability is compromised.
Unfortunately there is no perfect quantitative system for calculating the classification of a particular data element. In some situations, the appropriate classification may be more obvious, such as when federal laws require the University to protect certain types of data (e.g. personally identifiable information). If the appropriate classification is not inherently obvious, consider each security objective using the following table as a guide. It is an excerpt from Federal Information Processing Standards (FIPS) publication 199 published by the National Institute of Standards and Technology, which discusses the categorization of information and information systems.
Preserving authorized restrictions on information access and disclosure, including means for protecting personal privacy and proprietary information.
|The unauthorized disclosure of information could be expected to have a limited adverse effect on organizational operations, organizational assets, or individuals.||The unauthorized disclosure of information could be expected to have a serious adverse effect on organizational operations, organizational assets, or individuals.||The unauthorized disclosure of information could be expected to have a severe or catastrophic adverse effect on organizational operations, organizational assets, or individuals.|
Guarding against improper information modification or destruction, and includes ensuring information non-repudiation and authenticity.
|The unauthorized modification or destruction of information could be expected to have a limited adverse effect on organizational operations, organizational assets, or individuals.||The unauthorized modification or destruction of information could be expected to have a serious adverse effect on organizational operations, organizational assets, or individuals.||The unauthorized modification or destruction of information could be expected to have a severe or catastrophic adverse effect on organizational operations, organizational assets, or individuals.|
Ensuring timely and reliable access to and use of information.
|The disruption of access to or use of information or an information system could be expected to have a limited adverse effect on organizational operations, organizational assets, or individuals.||The disruption of access to or use of information or an information system could be expected to have a serious adverse effect on organizational operations, organizational assets, or individuals.||The disruption of access to or use of information or an information system could be expected to have a severe or catastrophic adverse effect on organizational operations, organizational assets, or individuals.|
As the total potential impact to the University increases from Low to High, the classification of data should become more restrictive moving from Public to Restricted. If an appropriate classification is still unclear after considering these points, contact the Information Security Office for assistance.
The Information Security Office and the Office of General Counsel have defined several types of Restricted data based on state and federal regulatory requirements. They're defined as follows:
|An Authentication Verifier is a piece of information that is held in confidence by an individual and used to prove that the person is who they say they are. In some instances, an Authentication Verifier may be shared amongst a small group of individuals. An Authentication Verifier may also be used to prove the identity of a system or service. Examples include, but are not limited to:
|2.||Covered Financial Information|
|See the University's Gramm-Leach-Bliley Information Security Program.|
|3.||Electronic Protected Health Information ("EPHI")|
|EPHI is defined as any Protected Health Information ("PHI") that is stored in or transmitted by electronic media. For the purpose of this definition, electronic media includes:
|4.||Export Controlled Materials|
Export Controlled Materials is defined as any information or materials that are subject to United States export control regulations including, but not limited to, the Export Administration Regulations (EAR) published by the U.S. Department of Commerce and the International Traffic in Arms Regulations (ITAR) published by the U.S. Department of State. See the Office of Research Integrity and Compliance's FAQ on Export Control for more information.
|5.||Federal Tax Information ("FTI")|
|FTI is defined as any return, return information or taxpayer return information that is entrusted to the University by the Internal Revenue Services. See Internal Revenue Service Publication 1075 Exhibit 2 for more information.|
|6.||Payment Card Information|
Payment card information is defined as a credit card number (also referred to as a primary account number or PAN) in combination with one or more of the following data elements:
Payment Card Information is also governed by the University's PCI DSS Policy and Guidelines (login required).
|7.||Personally Identifiable Education Records|
|Personally Identifiable Education Records are defined as any Education Records that contain one or more of the following personal identifiers:
See Carnegie Mellon’s Policy on Student Privacy Rights for more information on what constitutes an Education Record.
|8.||Personally Identifiable Information|
|For the purpose of meeting security breach notification requirements, PII is defined as a person’s first name or first initial and last name in combination with one or more of the following data elements:
|9.||Protected Health Information ("PHI")|
|PHI is defined as "individually identifiable health information" transmitted by electronic media, maintained in electronic media or transmitted or maintained in any other form or medium by a Covered Component, as defined in Carnegie Mellon’s HIPAA Policy. PHI is considered individually identifiable if it contains one or more of the following identifiers:
Per Carnegie Mellon’s HIPAA Policy, PHI does not include education records or treatment records covered by the Family Educational Rights and Privacy Act or employment records held by the University in its role as an employer.
|10.||Controlled Technical Information ("CTI")|
|Controlled Technical Information means "technical information with military or space application that is subject to controls on the access, use, reproduction, modification, performance, display, release, disclosure, or dissemination" per DFARS 252.204-7012.|
|11.||For Official Use Only ("FOUO")|
|Documents and data labeled or marked For Official Use Only are a pre-cursor of Controlled Unclassified Information (CUI) as defined by National Archives (NARA)|
|12.||Personal Data from European Union (EU)|
The EU’s General Data Protection Regulation (GDPR) defines personal data as any information that can identify a natural person, directly or indirectly, by reference to an identifier including
Any personal data that is collected from individuals in European Economic Area (EEA) countries is subject to GDPR. For questions, send email to email@example.com.
|0.1||07/02/2008||Doug Markiewicz||Original draft|
|0.2||09/25/2008||Doug Markiewicz||Replaced Categorization section with Data Collections and added sections on Reclassification and Calculating Classifications.|
|0.3||10/20/2008||Doug Markiewicz||Rewrote section on Calculating Classifications due to flaws in original system. Updated Purpose, Applies To and Definitions.|
|0.4||11/04/2008||Doug Markiewicz||Removed equation, made a minor update to the definition of Public Data and updated Additional Information. Sorted Appendix A so that terms appear in alphabetical order and added Covered Financial Information as a term.|
|0.5||02/20/2009||Doug Markiewicz||Added a missing bullet to the last identifier listed in Appendix A Definition G. The definition itself was not modified.|
|0.6||02/26/2009||Doug Markiewicz||Various updates based on Feedback. Major changes include adding 'Data Steward' to the Definitions, adding references to Information Security Roles & Responsibilities and adding Federal Tax Information to Appendix A.|
|0.7||03/18/2009||Doug Markiewicz||Updated definition of PHI in Appendix A to reference the HIPAA Information Security Policy. Added Authentication Verifier to Appendix A.|
|0.8||09/17/2009||Doug Markiewicz||Updated Applied To for consistency with related publications. Removed Education Records from Appendix A per the recommendation of General Counsel. Updated Personally Identifiable Education Records in Appendix A to reference the Policy on Student Privacy Rights.|
|0.9||01/22/2010||Doug Markiewicz||Updated Appendix A to include Export Controlled Materials.|
|1.0||09/15/2011||Doug Markiewicz||Updated definition of Protected Health Information to align with the new HIPAA Policy. Removed DRAFT designation.|
Updated Appendix A to include Controlled Technical Information.
Updated Appendix A to include FOUO and CUI.
|1.3||05/23/2018||Mary Ann Blair||
Updated Appendix A to include Personal Data from European Union