Name common information assurance principles relevant to intelligence data.

Name common information assurance principles relevant to intelligence data.

• A. Confidentiality, Integrity, Availability (CIA triad); access control; classification; marking; handling procedures.
• B. Speed, Efficiency, Flexibility; budget; staffing.
• C. Encryption, Fraud detection, Anonymity.
• D. Ownership, Availability, Confidentiality.

Answer: (A)

Protecting intelligence data relies on preserving three core properties: confidentiality, integrity, and availability. Confidentiality keeps sensitive information from unauthorized eyes; integrity ensures data remains accurate and untampered; availability makes sure authorized users can access the information when needed. These principles are put into practice through controls like access control (who can view or modify data), classification (level of sensitivity), marking (indicating handling requirements), and handling procedures (how data is stored, transmitted, and disposed of). Together, they provide the comprehensive framework for information assurance in intelligence contexts. Other options miss key IA elements—focusing on performance or resources, or combining only a subset of IA properties, or introducing concepts like ownership that aren’t standard IA principles.

Protecting intelligence data relies on preserving three core properties: confidentiality, integrity, and availability. Confidentiality keeps sensitive information from unauthorized eyes; integrity ensures data remains accurate and untampered; availability makes sure authorized users can access the information when needed. These principles are put into practice through controls like access control (who can view or modify data), classification (level of sensitivity), marking (indicating handling requirements), and handling procedures (how data is stored, transmitted, and disposed of). Together, they provide the comprehensive framework for information assurance in intelligence contexts. Other options miss key IA elements—focusing on performance or resources, or combining only a subset of IA properties, or introducing concepts like ownership that aren’t standard IA principles.