Enabling Secure AI

Privacy Enhancing Technologies (PETs) are here and ready to enable Secure AI capabilities to enhance decision making, protect privacy, and combat broader legal, societal, and national security risks.
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PETs-Powered Secure AI

Encrypted 
Evaluation/Inference
Encrypted Model Training/ Federated Learning
Encrypted 
Model Validation
We are firmly in the AI era. Driven by data, the impact these technologies have on our lives, businesses, and society at large will only continue to grow. It’s up to us to harness this potential in a responsible way that does not sacrifice our values — Privacy Enhancing Technologies can answer the challenge today. A family of technologies uniquely equipped to enable, enhance, and preserve the privacy of data throughout its lifecycle, PETs secure the usage of data and allow users to capitalize on the power of AI while mitigating risks and prioritizing protection.
Enveil’s ZeroReveal Machine Learning solutions extend the boundary of trusted compute by delivering Secure AI across boundaries and silos of disparate, decentralized datasets. ZeroReveal ML delivers Privacy Preserving Machine Learning (PPML) and encrypted federated learning to enable advanced decisioning in a secure and private capacity.

Why it matters?

To deliver the best outcomes, AI/ML capabilities need to be trained, enriched, and leveraged over a broad, diverse range of data sources.
When an ML model is trained on new and disparate datasets, it becomes smarter over time, resulting in increasingly accurate and valuable insights that were previously inaccessible. However, these models encode the data over which they were trained. When the data sources contain sensitive information, using these models raises significant concerns as adversarial ML attacks designed to glean the sensitive information are low-hanging fruit. This makes any vulnerability relating to the model itself while it’s being leveraged or trained a significant liability.

The AI functionality that promised to deliver business-enhancing, actionable insights now substantially increases the organization’s risk profile.
The Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence recently issued by the White House is a robust directive aimed at regulating the AI landscape. It provides a framework for building an AI-powered world where risks are acknowledged, privacy is respected, and security is foundational. PETs are identified as a key enabler of Secure AI capabilities, and the Executive Order specifically outlines directives to advance and drive the utilization of these transformative technologies for AI use cases.
Enveil’s ZeroReveal Machine Learning solutions exemplify how PETs can be used to deliver Secure AI capabilities. Organizations can securely enrich existing ML models by expanding the pool of available data sources for training and deployments options. Encrypted models can be leveraged across jurisdictional, third-party, and organizational boundaries, even when using highly sensitive or proprietary models, allowing organizations to securely and privately derive insights and improve outcomes.
The ability to securely train and evaluate ML models against data sources across silos and boundaries opens the door to a number of use cases where enhanced risk-based decision making drives positive outcomes for business and mission users.

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To learn more about the expanded value unlocked by Enveil, please schedule a meeting.
Enveil is a pioneering Privacy Enhancing Technology company protecting Data in Use. Enveil’s business-enabling and privacy-preserving capabilities change the paradigm of how and where organizations can leverage data to unlock value. Defining the transformative category of Privacy Enhancing Technologies (PETs), Enveil’s award-winning ZeroReveal® solutions for secure data usage, sharing, collaboration, and monetization protect data while it's being used or processed. Customers can extract insights, cross-match, search, and analyze data assets at scale without ever revealing the content of the search itself, compromising the security or ownership of the underlying data, or exposing their interests and intent.
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