The Importance of DLP Software for Regulatory Compliance

A DLP solution can help ensure that sensitive data is not exposed to unauthorized users. This can help mitigate the risk of financial losses due to regulatory non-compliance fines and prevent loss of intellectual property. Every IT staff member must be involved in DLP deployments to understand changes, support questions, and resolve issues quickly.

Enforcing Policies

Protecting data from unauthorized exfiltration requires policy enforcement, which is the core of DLP software. DLP tools monitor data movement and identify and classify sensitive information according to standards like encryption, security clearance, and data retention policies. DLP solutions can also automate the classification process and use advanced anomaly detection to identify patterns of behavior that might indicate a malicious intent or compliance violation. These features can be used to create more targeted and effective policies for protecting data, enabling InfoSec teams to concentrate on more critical tasks. Depending on the DLP tool in use, data might be pre-classified or dynamically classified based on its sensitivity and the impact its loss would have on your business. For example, credit card numbers, financial statements, wire transfer information, and customer data are high-risk categories for cybercriminals. DLP tools can identify these items and encrypt, quarantine, or otherwise restrict access to them to prevent their release. Other types of data, such as personal information — names, addresses, ages (which can reveal an individual’s identity when aggregated in small groups), phone numbers, and more — are privacy risks that DLP can help mitigate by transforming, masking, or deleting them before egress. Depending on the DLP solution, this can be done while the data is at rest, in motion, or transit.

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Identifying Sensitive Data

DLP solutions scan files, applications, emails, and other data sources to detect if sensitive information is leaving the organization. They can be customized to block that data from leaving the network, sent via instant messaging, copied to USB drives, or stored in inappropriate locations. They can also help ensure compliance for personal information that needs to be protected under regulations such as GDPR or HIPAA. Using an all-in-one solution that covers multiple states is best since there are many different ways in which data exits the organization. It’s essential to understand how these flows occur and to identify all potential risky transmission paths. This will give your DLP program a better chance to prevent leaks and breaches. Once you’ve identified the data your DLP solution will detect, you must create policies. The rules determine how the solution will respond if a policy violation is detected. This can include encrypting data before sending it over the Internet. It can also involve blocking access to a specific application or device, or it may alert the security team of an incident. It’s essential to minimize false negatives (failing to spot sensitive data) and false positives (alerting users about unintended, harmful actions). To achieve this, teams should regularly evaluate new capabilities and perform adversary emulation exercises to ensure the solution works as intended.

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Encrypting Sensitive Data

When a DLP solution identifies sensitive data, it needs to take action to protect it. For example, it might encrypt files before transmitting them over public networks or limit access to them on unsecured company-owned devices. It may also deny access to high-risk data in a secure area or restrict users from printing on a particular printer. The actions taken depend on the policy the DLP tool detects, the user involved, and other factors. A DLP tool can classify and tag sensitive information for easy identification and monitoring using a library of predefined policies and customizable rules. This information can be used to enforce policies and alert users to potential breaches. DLP solutions that include Bayesian analysis can also identify patterns in user behavior, detecting when they violate a policy or are at risk of breaching sensitive information. Educating employees about data security and why DLP is being used is essential to the success of any DLP program. The most effective programs are not just technical; they address cybersecurity concerns while balancing them with an employee’s need for efficiency. Organizations can improve employee buy-in by providing training materials, holding regular seminars, and including employees in decision-making. This approach reduces the number of unintended violations and increases the likelihood that employees will take DLP alerts seriously.

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Monitoring Sensitive Data

DLP software is only as effective as the policies it enforces. To create effective DLP policies, organizations should consult leaders from engineering, operations, legal, marketing, and other departments within their company. These leaders should help set how sensitive data is classified and what responsibilities internal and external users have with that data. Getting these key stakeholders on board will ensure that DLP is used appropriately and doesn’t hinder productivity. Creating and deploying policies is one thing, but keeping them up-to-date is another. As new threats and attack strategies surface, information security teams must regularly review their DLP solution’s configuration and features. This process should include regular adversary emulation exercises and a robust auditing system to validate that the DLP tool works as intended. Some DLP solutions provide continuous discovery and classification capabilities, ensuring that sensitive data is identified as it moves across the organization. These tools use either context or content awareness to identify if a file contains sensitive data. Context analysis looks at a document’s metadata or other properties to determine its exposure, while content awareness examines its contents. The most modern DLP solutions combine these techniques and rely on machine learning and behavioral analytics to identify abnormal behavior that could signal an attempt to breach data security. The solution can then flag and alert users or prevent them from sending the data.

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