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Database activity monitoring (DAM, a.k.a. Enterprise database auditing and Real-time protection [1]) is a database security technology for monitoring and analyzing database activity. DAM may combine data from network-based monitoring and native audit information to provide a comprehensive picture of database activity.
The auditor should plan a company's audit based on the information found in the previous step. Planning an audit helps the auditor obtain sufficient and appropriate evidence for each company's specific circumstances. It helps predict audit costs at a reasonable level, assign the proper manpower and time line and avoid misunderstandings with ...
Database auditing involves observing a database to be aware of the actions of database users. Database administrators and consultants often set up auditing for security purposes, for example, to ensure that those without the permission to access information do not access it.
The native audit trails are extracted on a regular basis and transferred to a designated security system where the database administrators do/should not have access. This ensures a certain level of segregation of duties that may provide evidence the native audit trails were not modified by authenticated administrators, and should be conducted ...
An IT audit is different from a financial statement audit.While a financial audit's purpose is to evaluate whether the financial statements present fairly, in all material respects, an entity's financial position, results of operations, and cash flows in conformity to standard accounting practices, the purposes of an IT audit is to evaluate the system's internal control design and effectiveness.
The Institute of Internal Auditors, a global professional audit standards body, has issued practice advisory 2330-1 stating the goals of audit working papers are to: [1] Document the planning, performance, and review of audit work; Provide the principal support for audit communication such as observations, conclusions, and the final report;
Data auditing is the process of conducting a data audit to assess how company's data is fit for given purpose. This involves profiling the data and assessing the impact of poor quality data on the organization's performance and profits.
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.