The reach of a human being knows no real end, and yet we still don’t do anything better than growing on a consistent basis. We say this because the stated reality has already fetched the world some huge milestones, with technology appearing as a major member of the group. The reason why technology’s credentials are so anomalous is purposed around its skill-set, which was unprecedented enough to realize all the possibilities for us that we couldn’t have imagined otherwise. Nevertheless, a closer look should be able to reveal how the whole runner was also very much inspired by the way we applied those skills across a real world environment. The latter component was, in fact, what gave the creation a spectrum-wide presence and made it the ultimate centerpiece of every horizon. Now, having such a powerful tool run the show did expand our experience in many different directions, but even after reaching so far ahead, this prodigious concept called technology will somehow keep on delivering the right goods. The same has grown to become a lot more evident in recent times, and assuming one new GRC-themed development pans out just like we envision, it will only propel that trend towards greater heights over the near future and beyond.
Privacera, an AI and data security governance company, has officially completed an integration with Amazon Bedrock and Amazon SageMaker for its proprietary Privacera AI Governance (PAIG) solution. Talk about PAIG a little bit first, it is something which comes decked up with an ability to govern and protect sensitive data within foundation models and generative AI applications. Under the new development, it will enhance that effort by leveraging the power of Amazon Bedrock, a fully-managed service which makes foundation models from leading AI companies accessible. Apart from that, the solution will also bank upon Amazon SageMaker, a cloud-based machine-learning platform that helps developers create, train, and deploy machine learning (ML) models on the cloud, to support open-source and proprietary foundation models and workflows. Moving on to how exactly the integration in question improves the given picture, it brings to the fore dedicated security and governance capabilities across the entire lifecycle of generative AI applications, right from discovery, through training and deployment, to continuous monitoring. But what does that mean on an actionable note? Well, we begin with the fact that PAIG will now have a refreshed framework to prevent sensitive data leaks. This it will do using a newly-acquired ability to define governance and security policies, an ability deeply rooted in its natural and easy-to-comprehend language. Once defined, the product can effectively enforce those policies across any generative AI application or model. Next up, we must acknowledge how the integration will help PAIG big time when it comes to detecting and filtering out any potential avenues for risk and abuse. You see, moving forward, the solution will boast the required knowhow to analyze user injected model inputs and outputs before blocking or masking data that could put the model or model users on uncertain grounds. Complimenting the stated feature is a chance to observe and trace things in a more efficient manner. This means PAIG can now monitor and analyze user interactions with the AI models and provide dashboards that unlock visibility across all generative AI applications, and models. Here, we cover type of requests made, sensitive data identified, and actions taken to protect sensitive data. Furthermore, PAIG will also deliver at your disposal an audit trail to track individual user activities with detailed information of individual requests and specific security applied.
“Every data-driven organization today is looking for scalable strategies to leverage generative AI applications in a secure, fully-governed, and transparent manner. Highly secure, easy-to-apply, consistent, and automatic enforcement of security and governance policies is paramount to scale the next generation of AI-powered applications,” said Balaji Ganesan, co-founder and CEO of Privacera. “Today, we are thrilled to announce the integration of PAIG with Amazon Bedrock and Amazon SageMaker. It’s a testament to our commitment to AWS and to seamlessly integrate with AWS AI and ML services to help enterprises address critical security, governance, and compliance requirements.”
The move builds upon PAIG’s existing integrations with 20 other AWS services, including data and analytics services, like Amazon Athena, Amazon EMR, Amazon OpenSearch Service, Amazon Redshift, Amazon Relational Database Services (Amazon RDS), Amazon Simple Storage Service (Amazon S3), third-party services such as Databricks and Snowflake, and AWS Lake Formation etc.
Founded in 2016, Privacera’s excellence in providing security, data privacy, governance solutions, has helped it reach a point where it now serves Fortune 500 clients across sectors like finance, insurance, life sciences, retail, media, consumer, and government etc. Having achieved AWS Data and Analytics Competency Status, the company also partners with leading data sources, including AWS, Snowflake, Databricks, Azure and Google. In case you are still not convinced about its credentials, then we must acknowledge the fact that has been recognized as a leader in the 2023 GigaOm Radar for Data Governance; named a 2022 CISO Choice Awards Finalist; and has received the 2022 Digital Innovator Award. Beyond that, Privacera was also a “Sample Vendor” for data security platforms in the Gartner® Hype Cycle™ for Data Security, 2023.