Data Diddling
Data Diddling Definition
Data diddling is the deliberate alteration of information at the point of entry into a computer system. Instead of storing the raw input, attackers modify, add, or delete data to make the output misleading. For example, salaries in a payroll system might be inflated, sales figures exaggerated, or tax entries falsified. Unlike other cybercrimes that target stored or transmitted information, data diddling happens at the earliest stage, which makes it harder to detect until inconsistencies appear.
How Data Diddling Works
Data diddling can be carried out manually by insiders, such as employees or contractors, or automatically through malicious software designed to tamper with input. Once altered, the system accepts the false information as valid, treating it as genuine and letting it flow through normal processing. This manipulation is often used to commit financial fraud, distort records, or damage the integrity of business operations.
Types of Data Diddling
- Input manipulation: Editing data before storage, such as entering incorrect numbers during manual input.
- Processing manipulation: Modifying data while the system is calculating or transforming it.
- Output manipulation: Changing data at the reporting or exporting stage to create misleading results.
Data Diddling Prevention Tips
- Validate input to check data accuracy at the point of entry.
- Implement access controls to limit who can add, change, or approve data.
- Track modifications with audit trails to identify unusual activity.
- Use antivirus software to detect and block malware designed to tamper with input.
- Train employees to follow proper data handling practices and spot insider threats.
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FAQ
Timing. Data diddling happens right when information is being entered, before it becomes part of a database or system. Most other cybercrimes, such as data breaches or ransomware, focus on information that already exists within files or networks. This early-stage focus makes data diddling unique because the system itself treats the fabricated data as if it was genuine.
Yes. While many modern systems include checks to validate data, data diddling still occurs because attackers often exploit human factors. Insiders with trusted access, weak oversight, or poorly configured processes can still introduce false data. Automated tools and malware also make it easier for manipulation to happen without immediate detection. This especially applies to industries like banking, retail, and healthcare that handle large volumes of sensitive records.
Data diddling is often prosecuted as computer fraud or data manipulation under cybersecurity and financial regulations. Penalties vary by jurisdiction but can include heavy fines, loss of business licenses, and even prison time for those directly involved. Because it undermines trust in digital systems, regulators treat it as a serious offense.
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