Data Quality Problem in security tooling arises from which issues?

Data Quality Problem in security tooling arises from which issues?

• A. Incomplete asset inventories, stale CMDBs, and missing logs
• B. Overly aggressive alert throttling
• C. Excessive automation
• D. Too many security policies

Answer: (A)

Data quality in security tooling hinges on having a complete, current data foundation from assets, configuration data, and telemetry. When asset inventories are incomplete, some devices or endpoints aren’t known to the tools, creating blind spots and unreliable risk assessments. If the CMDB is stale, the relationships, configurations, and ownership information used to contextualize alerts and enforce controls become outdated, leading to misinterpretations and ineffective responses. Missing logs mean critical telemetry is absent, so detection rules may miss events, correlations break down, and forensic analysis is hindered. The other options describe ways to manage workload or policy but don’t address the reliability and completeness of the data the tools rely on, which is what data quality is really about.

Data quality in security tooling hinges on having a complete, current data foundation from assets, configuration data, and telemetry. When asset inventories are incomplete, some devices or endpoints aren’t known to the tools, creating blind spots and unreliable risk assessments. If the CMDB is stale, the relationships, configurations, and ownership information used to contextualize alerts and enforce controls become outdated, leading to misinterpretations and ineffective responses. Missing logs mean critical telemetry is absent, so detection rules may miss events, correlations break down, and forensic analysis is hindered.

The other options describe ways to manage workload or policy but don’t address the reliability and completeness of the data the tools rely on, which is what data quality is really about.