NIST SP 800-53 Rev. 5 Common Control Library

NIST control statement

Centrally manage [Assignment: controls and related processes].

Discussion

Central management refers to organization-wide management and implementation of selected controls and processes. This includes planning, implementing, assessing, authorizing, and monitoring the organization-defined, centrally managed controls and processes. As the central management of controls is generally associated with the concept of common (inherited) controls, such management promotes and facilitates standardization of control implementations and management and the judicious use of organizational resources. Centrally managed controls and processes may also meet independence requirements for assessments in support of initial and ongoing authorizations to operate and as part of organizational continuous monitoring. Automated tools (e.g., security information and event management tools or enterprise security monitoring and management tools) can improve the accuracy, consistency, and availability of information associated with centrally managed controls and processes. Automation can also provide data aggregation and data correlation capabilities; alerting mechanisms; and dashboards to support risk-based decision-making within the organization. As part of the control selection processes, organizations determine the controls that may be suitable for central management based on resources and capabilities. It is not always possible to centrally manage every aspect of a control. In such cases, the control can be treated as a hybrid control with the control managed and implemented centrally or at the system level. The controls and control enhancements that are candidates for full or partial central management include but are not limited to: [AC-2(1)](#ac-2.1), [AC-2(2)](#ac-2.2), [AC-2(3)](#ac-2.3), [AC-2(4)](#ac-2.4), [AC-4(all)](#ac-4), [AC-17(1)](#ac-17.1), [AC-17(2)](#ac-17.2), [AC-17(3)](#ac-17.3), [AC-17(9)](#ac-17.9), [AC-18(1)](#ac-18.1), [AC-18(3)](#ac-18.3), [AC-18(4)](#ac-18.4), [AC-18(5)](#ac-18.5), [AC-19(4)](#ac-19.4), [AC-22](#ac-22), [AC-23](#ac-23), [AT-2(1)](#at-2.1), [AT-2(2)](#at-2.2), [AT-3(1)](#at-3.1), [AT-3(2)](#at-3.2), [AT-3(3)](#at-3.3), [AT-4](#at-4), [AU-3](#au-3), [AU-6(1)](#au-6.1), [AU-6(3)](#au-6.3), [AU-6(5)](#au-6.5), [AU-6(6)](#au-6.6), [AU-6(9)](#au-6.9), [AU-7(1)](#au-7.1), [AU-7(2)](#au-7.2), [AU-11](#au-11), [AU-13](#au-13), [AU-16](#au-16), [CA-2(1)](#ca-2.1), [CA-2(2)](#ca-2.2), [CA-2(3)](#ca-2.3), [CA-3(1)](#ca-3.1), [CA-3(2)](#ca-3.2), [CA-3(3)](#ca-3.3), [CA-7(1)](#ca-7.1), [CA-9](#ca-9), [CM-2(2)](#cm-2.2), [CM-3(1)](#cm-3.1), [CM-3(4)](#cm-3.4), [CM-4](#cm-4), [CM-6](#cm-6), [CM-6(1)](#cm-6.1), [CM-7(2)](#cm-7.2), [CM-7(4)](#cm-7.4), [CM-7(5)](#cm-7.5), [CM-8(all)](#cm-8), [CM-9(1)](#cm-9.1), [CM-10](#cm-10), [CM-11](#cm-11), [CP-7(all)](#cp-7), [CP-8(all)](#cp-8), [SC-43](#sc-43), [SI-2](#si-2), [SI-3](#si-3), [SI-4(all)](#si-4), [SI-7](#si-7), [SI-8](#si-8).

NIST control statement

a. Employ spam protection mechanisms at system entry and exit points to detect and act on unsolicited messages; and b. Update spam protection mechanisms when new releases are available in accordance with organizational configuration management policy and procedures.

Discussion

System entry and exit points include firewalls, remote-access servers, electronic mail servers, web servers, proxy servers, workstations, notebook computers, and mobile devices. Spam can be transported by different means, including email, email attachments, and web accesses. Spam protection mechanisms include signature definitions.

NIST control statement

Withdrawn in this source release; review the official source for disposition.

NIST control statement

Automatically update spam protection mechanisms [Assignment: frequency].

Discussion

Using automated mechanisms to update spam protection mechanisms helps to ensure that updates occur on a regular basis and provide the latest content and protection capabilities.

NIST control statement

Implement spam protection mechanisms with a learning capability to more effectively identify legitimate communications traffic.

Discussion

Learning mechanisms include Bayesian filters that respond to user inputs that identify specific traffic as spam or legitimate by updating algorithm parameters and thereby more accurately separating types of traffic.