In the era of digital transformation, data privacy and security have become paramount. As data moves through different stages - at rest, in transit, and in use - it becomes vulnerable to various threats. This post will delve into these stages, the associated risks, and the methods used to mitigate these risks. We'll discuss each stage in two separate sections: privacy and security, to provide a clear understanding of the techniques involved.
Ensuring entitlements for AI models is crucial for model providers in the commercial space. The lack of secure entitlements poses risks such as unauthorized access, undocumented usage, and intellectual property infringement. Enkrypt AI provides license enforcement, MRM technologies, and transparent audit trails to help secure entitlements and track the model supply chain, fostering innovation and trust in the Enterprise AI ecosystem.
In the rapidly evolving landscape of machine learning (ML), on-premises and VPC deployments have become preferred choices for enterprises. The reasons? Enhanced data sovereignty, compliance, and security. However, this shift poses challenges, especially concerning model metering and Intellectual Property (IP) infringement detection.
On-prem and VPC ML deployments offer enterprises the autonomy they seek. By hosting ML models within their infrastructure or private cloud environments, they retain full control over data pipelines, ensuring data privacy and meeting stringent regulatory requirements. This approach mitigates the risks associated with data transit and external breaches.
In this dynamic landscape, solutions that address these challenges not only ensure security and compliance but also unlock new revenue streams. By facilitating secure on-prem deployments and offering robust metering capabilities, businesses can tap into previously inaccessible markets. This means more deployments, greater reach, and ultimately, more revenue.
For ML developers and providers, metering is paramount:
With metering in place, the next concern is security:
To counter these challenges, advanced model security protocols are paramount:
Enkrypt AI stands at the forefront of addressing these challenges. Enkrypt AI's suite of tools prioritizes:
The trajectory for on-prem and VPC ML deployments is clear: as the demand for data privacy and security grows, so will the need for advanced model metering and infringement detection solutions. With pioneers like Enkrypt AI leading the charge, the ML community is poised to navigate these challenges, unlocking new revenue streams in the process.