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5 reasons why organizations are switching

1. Manage by exception

DASB manages by exception

DASB persistently and transparently protects data, with no impact to end-user experience, applications, and business workflows. DASB flips the traditional data protection model from one of opting into the least amount of data to protect, to an expansive, opt-out model. This opt-out model enables organizations to protect any and all data and manage exceptions around collaboration. 

DLP manages by rule

DLP requires rules to be written for every scenario.  Whether the scenarios are trying to identify every possible exfiltration pathway or map to acceptable business use, these rules need to be continuously tuned to decrease alerts, false positives and false negatives.  

2. Identify data by DNA

DASB expands its protection through dDNA matching 

DASB’s patented similarity detection engine understands the DNA of the data (dDNA) and looks for a match to dDNA that is already protected. If there is a match, MagicDerivative applies protection to this data automatically, with the same access controls as the originally protected data. This means that even if you have not discovered or classified all your sensitive data, or if your colleagues create or import new sensitive data down the road, DASB will automatically recognize this “unknown” data as sensitive and protect it.

DLP’s data identification is like using a fingerprint

DLP might encounter this telephone number (819661820893) and identify it as a credit card number, a false positive. An outgoing email attachment with this telephone number might be blocked causing a slowdown in the business where none is warranted. This interference with normal business operations is one of many major downsides of DLP. The more aggressively the security team adds and updates rules, the more often false positives occur. Employees are measured on their productivity. When security tools slow them down they complain and try anything they can to circumvent the blocker, DLP. DLP also fails to detect sensitive information that has been slightly altered, allowing it to pass freely as a false negative. For credit cards, a classic exfiltration bypass method is to spell out the credit card number (“eight one nine six…”), change the credit card number to an unreadable font like Wingdings, or re-write it as Roman numerals. It is easy to think up ways to get past DLP’s pattern matching. 

3. Protect First

DASB protects any and all data

DASB protects any data transparently. This allows for organizations to protect data first and then work on discovery and classification. DASB’s methodology for discovery and classification enables organizations to identify and administer the appropriate access controls to unknown data. This includes all the information your employees are creating every day and all the unknown data stored in location (on-prem, cloud, on endpoints, etc.) across your enterprise. 

DLP requires tedious discovery and classification

DLP’s obtrusive nature requires discovery and classification as a necessary crutch to achieve even the most basic protection scenarios. Manual classification can depend on every employee in the company filling out a small form every time they are about to send an email or save a file, a major drain on employee time. Worse, your colleagues are not security professionals, and their incentive is to get their work done, so the accuracy of their classification is in doubt. Insiders are known to be the largest threat vector, so giving employees the power to classify whether data is sensitive or not is a critical flaw. 

Discovery is known to be highly ineffective as discovery tools are not equipped for the volume of data and the varied locations (public or private cloud, on-prem) in which this data is stored . Automated discovery is also highly error-prone, leading to the wrong policies applied to the wrong data.

4. Expansive Protection

DASB data protection is expansive

DASB takes an expansive approach to data protection. We recognize that most, if not all, enterprise data contains sensitive or valuable information and this data should not be allowed to leak. DASB continuously discovers, classifies and protects previously unknown data. DASB achieves zero-trust, persistent protection that is completely transparent to end users. DASB protects any and all data without impact to the end-user experience. 

DLP data protection is reductive

Contrary to DASB, DLP’s approach to data protection is reductive. DLP depends on discovering and classifying data, with the goal of opting into only the smallest subset of data to protect. By default, DLP allows a file to flow freely unless it has been specifically identified as sensitive and a rule exists that can dictate how users can interact with that file.  This is an ongoing, tremendously time consuming, never-ending effort for security teams. It is nearly impossible to devise every possible rule to block exfiltration pathways, while aligning with the business and acceptable business use cases. Managing by rules is also a huge burden on employees, as more and more restrictions are imposed on their daily workflows. Given the amount of effort required of the security team to devise rules that detect sensitive data, and the overhead incurred by employees classifying their own data, using only prescribed applications and file types with workflow pop-ups, errors and overhead along the way, the DLP approach ends up being to opt-in to the least amount of data to protect as possible.

5. Time to Value in Hours 

DASB is implemented in hours

With DASB, deploy the agent, target a location, and you are transparently protecting data. DASB is implemented enterprise-wide, or in a phased approach, selecting the most important use cases first (source code, CRM, trade secrets, finance, PCI/PHI, etc.) and protecting all data related to those use cases.  DASB imposes no limits on applications, versions, file types, file sizes, repositories, developer tools, workflows, or anything else in the environment, no matter how complex or enterprise specific. 

DLP takes months, if not years to implement

DLP requires a comprehensive discovery and classification program, with buy-in and assistance from the business before even starting to write rules. As the discovery and classification program is continuous and manually conducted, rules need to be written, false positives and false negatives need to be constantly tuned. Once the discovery and classification programs are underway and tuning progress has been made, we are now able to move to monitor or test mode to see how the DLP program will impact end-user experience. Once the business and security sign off on acceptable impact to the business, and staff have been trained on the manual classification and data usage policies, DLP might be ready to start protecting data. 

DLP is the old paradigm. DASB is the New New. Based on the Zero Trust philosophy, DASB allows all data to be protected transparently, without impacting workflows or applications. 

Download our whitepaper, The Rise of DASB, to learn how to protect your organization’s data against breaches and insider threats.