Palantir Verus

ver·i·fy [ver-uh-fahy] to ascertain the truth or correctness of, as by examination, research, or comparison
Origin: 1275-1325: Latin verus

Data abuse is a very real threat. It is a threat both to those organizations entrusted to care for data as well as those whose privacy and civil liberties might be undermined through data misuse. Simply trusting that data will not be misused is not sufficient, it must be possible to verify proper usage and identify possible bad actors. With the introduction of Palantir Verus, we offer a complete framework for organizations to ensure the verification of data events, record utilization, and data security across the enterprise. By tracking the pedigree and lineage of all data, Palantir Verus enables secure auditing and comprehensive audit analysis of all enterprise data usage.

In effect, Palantir Verus turns big data tools on themselves, focusing sophisticated analysis techniques on the problem of auditing data usage. Palantir Verus can secure data assets while simultaneously verifying that data is used only for approved purposes. This framework enables organizations to: monitor data usage in existing systems; monitor across the enterprise; and apply powerful algorithmic methods to detect abuse.

Built in consultation with thought leaders in both the security and privacy spaces, Palantir Verus writes a new equation in the old “security versus privacy” algebra, removing the need to subtract from one to get the other. Instead, through Palantir Verus it is now possible to protect both our need for security and greater analytical capabilities, and our right to privacy.

Building Blocks of Palantir Verus

Sophisticated audit analysis. Simply tracking data usage is no longer sufficient to ensure data is being used appropriately. Rich analytic capabilities, powerful algorithmic approaches, and big data techniques are used to improve analytic effectiveness, ensure accountability, protect privacy, and prevent abuse.

  • Apply powerful algorithmic approaches to the identification of data abuse
  • Transform this detailed tracking data into usable information that fuels effective oversight and ensures a rapid response to misuse
  • Draw upon Palantir’s suite of intuitive visualization capabilities to make this data accessible and understandable to even non-technical supervisors and other responsible oversight authorities
  • Understand user activities in their proper context using screen capture video audit trails that allow you to see every detail of an investigation from the originating analyst’s perspective
  • Track data use in real time so you can mitigate potential issues before they lead to privacy violations, civil liberties infringement, data breach, or other liability
  • Gain a new understanding of how data is used that can better inform data management policies, facilitate the development of privacy and civil liberties protections, and improve overall analytic effectiveness

Detailed data tracking. Effectively manage all enterprise data among thousands of users as the pedigree and lineage of each piece of data is tracked from import to analysis to mission success.

  • Provide persistent sourcing and comprehensive metadata that gives data stewards the information they need to make appropriate data handling decisions
  • Generate complete, detailed histories of user activities enabling step-by-step review of the analytic process, providing an unparalleled record of how data is being used
  • Integrate record-keeping capabilities directly into the system so that reporting documentation and other supplementary information are stored all in the same place
  • Evaluate information accuracy and make better, more informed decisions with the aid of a complete, detailed data pedigree

Fine-grained data security and control. Granularly manage data access while facilitating scalable collaboration at the person-to-person, team-to-team, and organization-to-organization levels.

  • Build sophisticated access control regimes that manage data on a point-by-point basis, allowing precise sharing frameworks that comport with virtually any rule set in any jurisdiction in the world
  • Reject traditional “yes/no” access regimes and utilize sophisticated role-based and temporal sharing mechanisms that allow dynamic and nuanced sharing decisions to help you respond to ever-changing collaboration needs
  • Set up precision filters and construct complex search queries that ensure that analysts are accessing only the information relevant and responsive to their mission
  • Harmonize security protocols and data ontologies while sharing data at scale between enterprises

Flexible Data Retention and Management. Implement flexible data management practices that maintain valuable data while complying with purging and retention policies.

  • Use detailed metadata and data source tethering to comply with data retention schedules set by law and policy
  • Adjust data retention policies on the fly to comply with ever-evolving state and federal regulations that require data purging or to meet new mission imperatives
  • Document destruction of information to enable verification that data purging requirements have been met
  • Establish workflows that facilitate collaborative retention decisions by giving analysts the chance to request the deletion of irrelevant information and/or the preservation of data scheduled for destruction
  • Implement multi-step retention programs that gradually restrict data access based on temporal or other criteria
  • Delete unnecessary information with confidence, knowing that this deletion will be reflected in all derivative utilization of the data throughout the enterprise

Conclusion

As analytic capabilities continue to grow, we are often left with the impression that each technological advancement comes with an equal reduction in privacy. This simple zero-sum algebra does not take into account the fact that technology can actually serve as a lever to ensure privacy, to protect civil liberties, and to ultimately restore trust-through-verification in those institutions that are sworn to protect us. Palantir has long built technologies for just these purposes and has now developed the comprehensive Verus framework to deliver this capability. For the first time, big data analytics are being applied at the enterprise level to the problem of data safeguards and privacy protection.

The Palantir Verus framework offers a suite of capabilities that build confidence in the integrity of data analytics at all levels of the analysis enterprise. Users can be confident that they are handling data appropriately while maximizing collaboration. Organizations can be confident that they are limiting their exposure to liability while still effectively accomplishing their mission. The public can be confident that government and commercial services are acting lawfully and implementing forward-looking measures to protect the privacy and security of their data. Finally, Palantir Verus reflects the overarching Palantir philosophy that humans are the best analysts and the best decision-makers. Even the most powerful algorithmic approaches must be informed by humans. Technology alone does not protect privacy, preserve civil liberties, and guarantee data security—it instead helps to drive and implement the human-created policy that meets these goals.