Data Privacy empowers financial firms to unlock the value in sensitive data
Privitar Data Privacy is an APN Partner solution that accelerates deployment on AWS
Privitar enables financial companies worldwide to safely and efficiently unlock maximum value from their sensitive data without compromising data privacy. Data transformed by Privitar is utilized in a wide range of use cases powered by big data and analytics platforms that run on the data lake. Privitar’s comprehensive data privacy capabilities enable organisation-wide access, use, and distribution of data. Using Privitar, financial companies eliminate slow, manual provisioning and enable cloud-based analytics, machine learning, and other services to quickly and safely access insights from even the most sensitive datasets.
Privitar is an AWS Data and Analytics Competency Partner.
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Safely take full advantage of your sensitive data assets, preserving analytical utility to enable insights from previously unavailable data.
Centrally manage compliance with data privacy regulations such as GDPR and CCPA, and action privacy policies without the need for custom scripting or coding.
Protect data with the most comprehensive and cutting edge range of privacy enhancing techniques, significantly mitigating the risk inherent in sensitive data.
Privitar delivers data privacy at enterprise scale enabling safe use of data. Companies with Privitar on AWS benefit from the modern data privacy features:
- Centralized privacy governance powers the de-identification of data at scale on-premises, enabling broader use
- Privacy policies are implemented across all environments drawing on a full range of data privacy techniques, including masking, tokenization, and generalization applied consistently and at scale
- Users can manage the data privacy life cycle from ingest to usage and distribution, enabling data access at the speed of business
- Deters insider threats and accelerates investigations by adding watermarks and metadata to dedicated data releases (Protected Data Domains)
- Enables Analytics and Machine Learning use cases including Customer 360, AML, and Test & Dev data in compliance with data regulations like GDPR and CCPA.
Case study: Customer analytics at a global bank
A global bank wanted to combine customer data from various sources to run predictive models in AWS to provide for different areas of their business financial crime alerts, marketing next best actions for specific customers, and other results. The sensitivity of the data was blocking the bank’s ability to move it into the cloud.
With a highly complex data infrastructure and disparate user groups, the bank needed the ability to apply consistent, scalable, and repeatable privacy policies across the organization to produce de-identified datasets on demand. With Privitar Publisher, reusable policies approved by information security are centrally controlled but applied to the data in motion whenever needed.
The bank can now score de-identified behavioral profiles against trained models in a cloud based analytics engine. A Publisher feature enables the bank to re-identify data in controlled conditions, empowering intelligent, personalized customer interactions and financial crime teams to take action against suspicious accounts.
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