Data breaches cause significant damage to the trust and reputation of a business. They can also be expensive.
Before implementing a DLP solution, CISOs should prioritize the data most important to protect. It will help them to focus their efforts on a limited number of processes. They should also consider using advanced detection techniques such as fingerprinting, watermarking, and ML.
1. Detecting Unauthorized Access
With breaches involving millions of records becoming more frequent, protecting sensitive data has never been more critical. Adversaries from nation-states, cybercriminals, and malicious insiders are targeting businesses for corporate espionage, financial gain, or other motivations.
The first step to protecting data is detecting unauthorized access to it. DLP solutions can scan your entire digital estate to identify and monitor data for sensitive information. They can even see data leaving your network via email, being copied to USB drives, and more.
Ideally, you will start with a risk assessment to understand what information is critical, where it lives, and how it moves. It gives you a map of your information and provides clear guidance for what you need to protect.
Once you know your most valuable information, it’s time to set policies. This process can consider sensitivity, regulatory requirements (like GDPR and California’s CCPA), compliance concerns, or other business considerations. It’s also best to work with other departments and business units to ensure they know the DLP policies and how they might be impacted.
Once your policies are in place, you can implement your DLP solution. You can choose to use an on-premises solution, a cloud-based solution, or a hybrid of the two. To make the best decision, it’s essential to consider the size of your organization and weigh all the options available.
2. Detecting Malware
Data loss prevention employs a range of security measures to ensure the safety of sensitive data, including firewalls, intrusion detection systems, and antivirus software. These tools work together to identify and prevent malware, thereby minimizing the risk of potential data breaches.
When malware gets into your network, it can be used to access sensitive information or encrypt it, leaving you without the information you need to do business. DLP software can detect this type of malware, removing it before it can cause any damage.
In addition to protecting against malicious attacks, DLP can help protect your organization from accidental data exposures. Employees often inadvertently send sensitive information to unauthorized recipients or lose files on personal devices. Data loss prevention can detect these unauthorized actions, monitoring where and when sensitive data leaves the company, whether through email or a file-sharing app.
Many companies use Data Loss Prevention (DLP) solutions to comply with industry regulations, including GDPR, HIPAA, and PCI DSS. Implementing an effective DLP solution can prevent data breaches, tampering, and other unwanted behavior that could lead to non-compliance penalties.
The solutions also help you create data handling policies, establishing the appropriate level of security for each type of sensitive information within your organization. The result is a complete inventory of your information and the ability to apply relevant security standards for each, reducing the risk of accidental or malicious data leakage.
3. Detecting Phishing
The average business loses $15 million a year from phishing attacks. A DLP solution can detect these phishing attempts and protect the organization’s reputation, financial security, and personal data.
Many DLP solutions start by identifying critical data and creating policies, strategies, infrastructure, training, and tools to prevent attackers from stealing the organization’s information. Depending on the business needs, these systems can be on-premises or cloud-based.
Some solutions use regular expression matching to find data strings indicative of sensitive information, such as 16-digit credit card numbers in emails or 9-digit phone numbers on documents. Other DLP systems utilize machine learning, statistical analysis, and other techniques to recognize patterns and learn what constitutes sensitive information based on context.
Another vital function of DLP is to monitor data in motion as the data leaves the organization’s network. It includes when it is attached to emails, shared with customers and partners, or moved to other devices. Data is often vulnerable when it moves between networks, servers, and endpoints.
DLP can detect these movements and create policies to control access, prevent modification, encrypt, or otherwise remediate the information before it leaves the organization’s network. DLP can also identify and prevent data exfiltration from insider threats such as accidental or malicious forwarding of confidential information to outsiders or from external attacks like ransomware.
4. Detecting Fraud
Whether disgruntled or malicious, data thieves seek to steal your customers’ personal information for financial gain or corporate espionage. DLP can detect phishing attacks and stop data in transit from leaving your network, protecting confidential customer information and sensitive business intelligence.
Global privacy and cybersecurity regulations are constantly changing. DLP can help you comply with GDPR and NYDFS cyber protection requirements and monitor for emerging threats to your system infrastructure, ensuring that your data remains safe.
5. Insider Threats
Over half of all security breaches are caused by internal issues, such as a disgruntled employee seeking revenge or a malicious one selling customer information. DLP can identify these situations and stop sensitive information from being shared with outsiders by logging in for auditing, displaying a warning to employees who could unintentionally share information, or even actively blocking the transmission of data out of your network.
Final Words
Using pattern-matching, DLP solutions detect personal information, such as credit cards and Social Security numbers. Using machine learning, statistical analysis, and other techniques, these solutions can “fingerprint” your sensitive data and watch for attempts to move it from place to place. Reduces your security teams’ time investigating false positives, enabling them to focus on the most dangerous traffic.