Imagine how you would feel if you found that your social security number and other vital personal information were available online. Compromised in a security breach carried out by an anonymous hacking group.
Feelings of violation and rage would be both warranted and expected.
This is the sort of scenario that can be prevented by Data Loss Prevention. Data Loss Prevention helps ensure that bad actors can’t get to your private information. It also ensures that they can’t release critical/sensitive data related to your business.
Data Loss Prevention, or DLP, refers to a collection of strategies and tools that monitor and protect business and personal data from unauthorized access. The business Spanning Cloud Apps states that Data Loss Prevention technologies protect your data when it’s in three states: in use, in motion, and at rest. By implementing DLP and Data Loss Prevention best practices, your data is protected when you or your employees are using it as well as when you’re moving it internally or sending it externally. It’s also protected when it’s stored on file servers or in a database. Additionally, DLP software can stop users from copying a company's data outside a company's network.
DLP software has traditionally been designed using static rules. However, the advancement of intelligent technologies has raised the bar. Machine learning DLP can detect and prevent data breaches by recognizing patterns of activity that occur prior to a breach.
The foundation of DLP software is Content Inspection. It employs a variety of approaches to detect policy violations.
Content Inspection is grounded in rule-based expressions, identified by data loss protection software, resulting in further action. For example, we know that credit cards have 16-digits. With this information, a company could create a rule in the DLP software to block, or automate encryption on emails containing a 3-digit security code or expiration date.
Another approach used in DLP is file matching or data fingerprinting. This identifies and tracks sensitive information so that it can be protected properly. It’s used primarily when employees are working with large amounts of sensitive data such as medical forms or tax documents. It provides a scalable technique for identifying, monitoring, and applying protective controls to data as it moves across the corporate network.
Data Loss Prevention solutions also leverage conceptual / lexicon analysis to inspect content. This employs a set of dictionaries or other lists as well as rules to detect undesirable conduct. This conduct could be something like specific internet searches or the sharing of trade secrets with others outside the network.
Finally, advanced statistical analysis techniques can also be used to analyze content. Machine learning can use statistics to protect certain pieces of information. When a machine learns how data should be organized it is continuously on the lookout for data that does not fit the pattern.
There are three types of DLP, each of the three uses a different approach to deliver protection.
This type of data protection software places a secure perimeter around the monitored data on the network. It follows the data as it flows across the company's network.
For example, if a user tries to email sensitive information while on the company's network the network DLP security will audit the email and encrypt, block, or quarantine dependent on the software settings. It can also alert the administrator to attempted email transmission, making it a very useful option.
However, it does have drawbacks. The biggest being that the solutions are only effective when connected to a network. Devices away from the network can’t use this option.
On the flip side, an endpoint DLP does not work on a network where data is in transit. Instead, it’s installed on individual devices that act as network endpoints. Endpoint DLP security keeps track of data as it goes to and from these endpoints. No matter where they are or how they link to the network or the internet. It can even identify when vital data is saved on devices that don’t use encryption.
While Endpoint DLP offers great protection, it also requires more management. Endpoint DLP security software must be installed on each device. It's important to consider the time and effort it takes to manage and maintain an Endpoint DLP solution. With this option it’s also best to take into consideration the location of your employees and the workload of your IT staff.
Cloud DLP applies rules and policies on a subset of cloud accounts, similar to an Endpoint DLP solution. Unlike Network DLP, it does not create a perimeter around a standard on-premises network. Rather, it works with cloud-based applications such as Office 365 and Google's G Suite (and many others).
This provides your employees with the convenience and security that comes with cloud applications and storage without the risk of data loss or breach.
Cleaning up after a data breach can be expensive monetarily. Companies that provide free credit monitoring to customers whose information was compromised can spend millions on this service. Victims can also pursue legal action against organizations that put their data at risk, which may be financially catastrophic depending on the scope of the breach.
The damage to a company's reputation can also have a lasting impact. Problems created by the failure to comply with information privacy rules, such as HIPAA, the Fair and Accurate Credit Transactions Act (FACTA), and California's Online Privacy Protection Act, can overshadow both financial expenses and reputational damage (OPPA).
An ever-changing technological landscape requires companies to be wary of cybercriminals targeting sensitive company and customer data. Data Loss Prevention options offer protection for your company. Threats come from many different angles, so it’s important to choose the option that works best for you.
Managed Service Providers, like LammTech, are well versed in DLP solutions and can help you make the choice that will protect your business and your customers!