Data Diddling
Data diddling is the act of extracting and changing the value of data without the knowledge of the owner is a crime. Data diddling (also called false data entry) simply means altering data before entering it or entering it into the computer system. Data diddling can also occur when data is altered just before it is processed by a computer system and this same data can be modified back after processing the fake data that was entered. This form of cybercrime usually occurs within the organization providing customer services i.e. electricity organization, medical organization, transport organization, etc. where customer bills, personal information, or orders of valuable and important records are manipulated.
'Data diddling' or 'Data Tampering' can also be defined as a form of computer-based fraud in which data is altered before, during, or after it is entered into a computer system.
Data diddling actions are also classified to be forging or counterfeiting of documents. Computer Forensic Experts are good at investigating data diddling cases by recovering or tracing original/raw data/documents on a computer system.
This manipulation can lead to financial loss, unauthorized access, or other malicious activities.
Here are several types of data diddling:
- Input Data Diddling:
- Description: Manipulating data before it enters a computer system. This can involve altering input data through various means, such as modifying forms or electronic inputs.
- Example: Changing the figures on a purchase order form before it is entered into the system.
- Program Data Diddling:
- Description: Modifying the actual computer program or script that processes the data to produce fraudulent results.
- Example: Altering the logic in a payroll processing script to change employee salary figures.
- Output Data Diddling:
- Description: Manipulating data after it has been processed but before it is presented to users or stored in databases.
- Example: Changing the numbers on a financial report after the data has been processed but before it is presented to management.
- Database Data Diddling:
- Description: Modifying data directly within the database, either through unauthorized access or exploiting vulnerabilities.
- Example: Changing customer records in a database to manipulate account balances.
- Logging and Audit Trail Data Diddling:
- Description: Manipulating or deleting logs and audit trails to cover up fraudulent activities and make it difficult to trace unauthorized actions.
- Example: Deleting entries in a system log that record changes made by an unauthorized user.
- Network Data Diddling:
- Description: Manipulating data as it travels over a network. This can involve intercepting and altering data packets.
- Example: Intercepting and modifying financial transaction data during transmission over an insecure network.
- Supply Chain Data Diddling:
- Description: Manipulating data within the supply chain, such as altering product quantities, prices, or delivery schedules.
- Example: Changing the quantity or price of goods in a supply chain management system.
- Cloud Data Diddling:
- Description: Manipulating data stored in cloud-based systems, taking advantage of vulnerabilities or insecure configurations.
- Example: Altering files stored in a cloud storage service to manipulate document content.
- Mobile Device Data Diddling:
- Description: Manipulating data on mobile devices, including altering application data or exploiting vulnerabilities in mobile apps.
- Example: Modifying transaction data in a mobile banking app to change account balances.
- Payment Card Data Diddling:
- Description: Manipulating data related to payment card transactions, often to steal sensitive information.
- Example: Tampering with a point-of-sale system to capture and alter credit card details during transactions.
- Employee Data Diddling:
- Description: Manipulating employee data, such as payroll records, benefits information, or performance metrics.
- Example: Changing hours worked in a timekeeping system to increase overtime pay.
Preventing data diddling involves implementing strong access controls, regularly auditing and monitoring data, securing databases, and educating users about the importance of data integrity. Additionally, organizations should employ encryption and implement measures to detect and respond to suspicious activities that could indicate data manipulation.
Preventing data diddling requires a combination of technical measures, security policies, and user awareness.
Here are some best practices to help prevent data diddling:
- Implement Access Controls:
- Enforce strict access controls to limit users’ access to data based on their roles and responsibilities. Only authorized personnel should have the ability to modify sensitive information.
- Use Encryption:
- Implement encryption for sensitive data both in transit and at rest. This helps protect data from unauthorized access and tampering, especially when stored in databases or transmitted over networks.
- Employ Database Security Measures:
- Implement robust database security measures, including strong authentication, access monitoring, and auditing capabilities. Regularly review and update database security configurations.
- Monitor Logs and Audit Trails:
- Regularly review logs and audit trails for suspicious activities. Log analysis tools can help detect anomalies and unauthorized changes to data. Ensure that logs are protected against tampering.
- Enable Version Control:
- Implement version control mechanisms for critical documents and data. This allows you to track changes over time and revert to previous versions if unauthorized modifications are detected.
- Use Digital Signatures:
- Implement digital signatures for important documents and transactions. Digital signatures provide a way to verify the authenticity and integrity of electronic documents.
- Implement Change Control Procedures:
- Establish change control procedures for any modifications to systems, applications, or databases. This includes thorough testing and validation before changes are applied in a production environment.
- Regularly Update and Patch Systems:
- Keep all systems, applications, and software up to date with the latest security patches. Regular updates help address vulnerabilities that could be exploited for data diddling.
- Educate and Train Users:
- Train employees on security awareness and the importance of data integrity. Make them aware of common data diddling techniques and encourage reporting of suspicious activities.
- Conduct Regular Security Audits:
- Perform regular security audits to identify vulnerabilities and weaknesses in your systems. This includes reviewing configurations, permissions, and user activities.
- Implement Least Privilege Principle:
- Adhere to the principle of least privilege, granting users the minimum level of access required to perform their job functions. This minimizes the potential impact of insider threats.
- Use Secure Network Practices:
- Secure your network infrastructure with firewalls, intrusion detection/prevention systems, and secure Wi-Fi protocols. This helps prevent unauthorized access to sensitive data during transmission.
- Regularly Backup Data:
- Implement regular data backups to ensure you have clean copies of critical information. This facilitates data recovery in case of data diddling incidents or other cybersecurity threats.
- Implement Multi-Factor Authentication (MFA):
- Enable multi-factor authentication to add an extra layer of protection for user accounts. Even if login credentials are compromised, MFA helps prevent unauthorized access.
- Establish Incident Response Plans:
- Develop and regularly test incident response plans to ensure a swift and effective response to suspected data diddling incidents. This includes isolating affected systems and conducting forensic analysis.
By combining these prevention methods, organizations can significantly reduce the risk of falling victim to data diddling and enhance the overall integrity of their data. Regularly reassessing and updating security measures based on emerging threats is also crucial for maintaining a robust defense against data manipulation.