Data diddling is a cybercrime where information is illicitly altered or manipulated before or during its entry into a computer system. This tampering can involve changing, deleting, or adding data to skew results or outcomes. Unlike other forms of hacking, which typically occur after data has been inputted, data diddling happens at the point of entry, making it particularly insidious and challenging to detect.

How does Data Diddling Work?

Data diddling operates by altering data at the point of entry, either before or during its input into a computer system. This manipulation can be executed manually by individuals responsible for data entry or automatically through malware. The altered data is then processed by the system, leading to skewed results or fraudulent outcomes.

Human intervention plays a significant role in data diddling. Data entry clerks or other personnel with access to the data entry points can intentionally modify the data. This can involve changing, deleting, or adding information to achieve specific objectives, such as financial gain or competitive advantage.

In addition to manual manipulation, data diddling can also be carried out remotely. Hackers may deploy malware to automatically alter data as it is entered into the system. This automated approach can be particularly insidious, as it often goes undetected until the fraudulent data has already been processed, making it challenging to trace and rectify the alterations.

What are Examples of Data Diddling?

Examples of data diddling span various sectors, each with unique implications. In financial transactions, data diddling can involve hiding part of profits to evade taxes or fabricating sales figures to secure better loan proposals. These manipulations can significantly distort financial statements, leading to fraudulent benefits for the perpetrators.

Another common example is in payroll systems, where data diddling might involve altering employee salary data, manipulating hours worked, or changing tax withholdings. Such actions can result in financial discrepancies that benefit certain individuals or the organization. Additionally, in government databases, altering dates or figures in legal documents can have far-reaching consequences, affecting public records and legal proceedings.

What are the Potential Risks of Data Diddling?

The potential risks of suffering from data diddling are significant and multifaceted. Here are some of the key risks:

• Financial losses: Unauthorized changes in data can lead to substantial financial losses, such as hidden profits for tax evasion or fabricated financial data to secure better loan proposals.

• Damage to business integrity: The integrity of critical business information can be compromised, potentially damaging a company's reputation and causing harm to competitors.

• Increased risk of fraud: Data diddling can facilitate fraudulent activities, including tax evasion and inflating business metrics, leading to an increased risk of fraud and embezzlement.

• Operational disruptions: Altered data can make entire systems unusable, resulting in significant operational disruptions and inefficiencies.

• Legal consequences: Engaging in or falling victim to data diddling can lead to severe legal repercussions and regulatory fines.

How can you Protect Against Data Diddling?

Protecting against data diddling requires a multi-faceted approach. Here are some key strategies:

• Implement Antivirus Software: Use robust antivirus software to safeguard networks, systems, and devices from malware that could alter data during entry.

• Conduct Regular Training: Ensure that employees are well-trained in data handling protocols and aware of the risks associated with data diddling.

• Enforce Access Management: Implement strict access controls and permissions to limit who can enter or modify data, reducing the risk of insider threats.

• Utilize Multi-Factor Authentication: Require additional authentication steps for accessing and managing sensitive information to ensure that only authorized personnel can make changes.

• Perform Regular Audits: Conduct frequent audits and monitor data changes to detect and address any unauthorized alterations promptly.