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Lead AI Innovation
Without Data
Privacy Risks

Use the PVML privacy-first data infrastructure to effortlessly build secure and scalable AI. Safely expose PII to AI, enable Secure RAG, ensure full compliance, and prevent vendor lock-in – all while maximizing the full potential of AI-driven innovation.
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Permission Impossible

AI is Reshaping the Rules
of Data Access and Analytics

Data Leakage

Data Leakage

Traditional permissions management and governance solutions weren’t built for AI-driven data flows, leading to security gaps and unauthorized access risks.
PII Exposure to AI

PII Exposure to AI

Legacy systems lack the safeguards needed to protect sensitive data in AI workflows, increasing the risk of personal data exposure, regulatory violations, and policy breaches.
Unsupervised Outputs

Unsupervised Outputs

AI is no longer limited to data professionals—now, anyone can generate insights. Without proper supervision and explainability, unchecked AI responses can lead to misinformation, compliance failures, and severe business consequences.
Dynamic and Complex Landscape

Dynamic and Complex Landscape

The AI application and model layer is evolving at an unprecedented pace, outpacing legacy workflows. Teams struggle to adapt and scale fast enough, leading to vendor lock-in and stalled innovation.


The Privacy-First
Data Infrastructure
for Secure & Scalable AI

A unified platform that transforms enterprise data into a
secure and flexible foundation for AI and analytics. No
matter where your data is stored or how it s accessed
(BI, API, AI) PVML streamlines permissions management,
monitoring, and best-in-class privacy enforcement –
empowering enterprises to unlock AI s full potential with
complete security and control.

Explore the infrastructure
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Agnostic & Flexible
Architecture

With a 3-way agnostic architecture, PVML empowers enterprises to connect any data source, use any access method (AI, BI, or API), and swap AI models freely eliminating vendor lock-in and enabling scalable and flexible AI adoption. All without installation, data movement, duplication, or workflow modifications.

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Differential Privacy Data
Protection Engine

PVMLs proprietary Privacy Engine leverages the mathematical framework of Differential Privacy (DP) to enforce permissions and privacy policies at the computation level in real time. As the only privacy-preserving technology recognized by AI data regulations, with PVML, enterprises can securely extract AI driven insights, unify access policies, and maintain compliance – without compromising privacy or scalability.

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GenAI Optimization
Module

PVML provides a secure, structured, and efficient AI pipeline, featuring an automatically generated Semantic and Behavioral Layer to improve model context, enable secure RAG integration, model flexibility, and builtin auditability. With PVML, enterprises can scale AI confidently while maintaining privacy, security, and control.

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Security and Compliance

PVML provides a secure foundation that allows you to push the boundaries. We undergo strict external audits to ensure our solution adheres to the highest standards of privacy and security.
Security and Compliance

Future Proof Your AI Innovation with PVML

Privacy
Protection &
Compliance

Privacy Protection & Regulatory Compliance – Maintain complete control over permissions, access, and auditability while ensuring compliance with the highest data privacy standards.

Improved Efficiency

Reduce costs and accelerate time-to-value by streamlining AI and data access without unnecessary data duplication or manual configurations.

Flexibility to Innovate

Stay ahead of the curve and avoid vendor lock-in by seamlessly adopting new AI models, tools, and data sources.

Visibility & Control 

Manage all data access, permissions, and privacy policies from a single, centralized system for complete governance, audit and security.

Optimized AI

I accuracy and reliability
with contextualized data, Secure
RAG, intelligent prompt
engineering and complete audit.

Broader Data Access

Unlock previously restricted data, safely expand AI access across
teams, and enable secure third-party collaboration.Information (PII).

Use Cases

Analyze Data with AI

Unlocking access with AI requires strong privacy
capabilities, ability to analyze live data and guarantees
that results are trustworthy and based on the data.

With PVML, you can enforce permissions on live chats,
empower your users to analyze data in real time using
free text and see how results were generated for explainability.

Read about our case study featuring a fintech company
that sped up time to insight by giving employees access
to a live chat with their data.

Learn more

Anonymization

Sharing data between business units poses challenges
due to different data-owners, multiple data sources, and
various security concerns.

With the integration of PVML’s data access platform, all
data sources can be centralized, promoting collaboration
across business units without compromising privacy.

Read about our case study featuring an insurance company
that enhanced the quality and speed of business insights by
unlocking internal collaboration.

Learn more

Monetization

Monetizing data requires strong privacy guarantees to
ensure trust and compliance, but also convenient ways
for the 3rd parties to extract value from this data.

Our platform allows companies to monetize insights
derived from data without risking customers’ privacy,
alongside both AI-based options to analyze the data.

Read about our case study with a telecom company
seeking for privacy-preserving ways of sharing insights
from data with non-technical 3rd parties.

Learn more
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    See PVML In
    Action?

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Latest blog posts

Explore Our Recent Insights and Updates.

  • Differential Privacy VS Data Masking: The Evolution of Privacy-preserving Methods
    Data Privacy

    Differential Privacy VS Data Masking: The Evolution of Privacy-preserving Methods

    With the advent of artificial intelligence, companies are increasingly turning to advanced privacy-preserving methods to safeguard their data. Two prominent techniques in this arena...
    9 min read
  • Building a Successful Data Platform Strategy Today
    Data Privacy

    Building a Successful Data Platform Strategy Today

    The ability to collect, process, and derive insights from vast amounts of information is a necessity for businesses looking to stay competitive today (see...
    9 min read
  • Essential Features of an Enterprise Data Platform for Optimized Performance
    Data Privacy

    Essential Features of an Enterprise Data Platform for Optimized Performance

    As data volumes continue to grow exponentially, organizations are increasingly relying on enterprise data platforms (EDP) to manage, analyze, and derive value from their...
    11 min read
  • Data Access Management in the AI Era
    Data Privacy

    Data Access Management in the AI Era

    As AI technologies, particularly generative AI, continue to advance and find more applications across various industries, the demand for high-quality, diverse datasets is likely...
    10 min read
  • The European Data Act: A New Era for Privacy Regulation
    Data Privacy

    The European Data Act: A New Era for Privacy Regulation

    The rapid advancement of technology, with the proliferation of smart devices, social media platforms, and cloud-based services, has brought about a myriad of challenges...
    10 min read
  • Why Your Financial Data Infrastructure is Critical
    Data Privacy

    Why Your Financial Data Infrastructure is Critical

    The term “financial data” covers everything from daily transactions to complex data like market movements, economic indicators, and company performance. Organizations actively collect and...
    5 min read
  • A Comprehensive Explanation of The Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
    Technology

    A Comprehensive Explanation of The Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence

    AI, or artificial intelligence, has changed how we interact with technology. This shift has come in all shapes and sizes since the early releases...
    6 min read
  • Effective Strategies for Access Control and Permission Management in Sensitive Environments
    Data PrivacyTechnology

    Effective Strategies for Access Control and Permission Management in Sensitive Environments

    Consider this scenario: A financial institution has not implemented access control. Employees at all levels, including interns and administrative staff, have unrestricted access to clients’...
    6 min read
  • What is Data Access Governance, and Why Is it so Important?
    Data Privacy

    What is Data Access Governance, and Why Is it so Important?

    Organizations are amassing vast amounts of information, including sensitive and confidential data. Effective data management has become a critical concern, not only for regulatory...
    9 min read
  • Data Infrastructure: Essential Tips and Best Practices
    Data Privacy

    Data Infrastructure: Essential Tips and Best Practices

    Building a modern data infrastructure is not just a technological necessity but a strategic imperative for organizations that want to unlock new opportunities, drive...
    9 min read

Frequently Asked Questions

Everything you need to know.

TL;DR: We allow analytics and ML to be applied on sensitive data, providing mathematically guaranteed private outputs by introducing randomization to the computation.

Differential privacy (DP) is a set of systems and practices that help keep the data of individuals safe and private. Differential Privacy offers the strongest possible privacy protection available today, with a mathematical guarantee to back up each algorithm. Differential privacy is achieved by introducing statistical noise. The noise is significant enough to protect the privacy of any individual in the data, but small enough that it will not impact the accuracy of analytics and machine learning methods applied on the data.

PVML offers proprietary Differential Privacy technology to exract useful insights and train AI models using datasets containing sensitive information. Our algorithms are performed on the analysis itself, on-the-fly, so that the outputs are privacy-preserving and can be safely used or shared by the user or third-party.

Learn more about how we use Differential Privacy

TL;DR: As opposed to Homomorphic Encryption, Differential Privacy has no overhead in computation and memory cost, and it also guarantees privacy at the output level, preventing reverse engineering and attribute inference attacks.

Homomorphic Encryption allows computation directly on encrypted data, however – it isn’t efficient. Because Homomorphic Encryption comes with a large performance overhead, computations that are already costly to do on unencrypted data probably aren’t feasible on encrypted data. Moreover, although the data is unreadable, the computations performed on it remain the same, including the outputs. When outputs are returned in perfect accuracy, the privacy of individuals in the data cannot be guaranteed, and the dataset remains vulnerable to re-identification attacks where sensitive raw data may be extracted in reverse engineering and attribute inference attacks.

Read more about Differential Privacy

TL;DR: PVML prioritizes applicable algorithmic capabilities, beyond what science can currently provide in the field of Differential Privacy.

PVML incorporates beyond state-of-the-art research objectives along with software engineering and applied machine learning in order to provide the most efficient Differential Privacy algorithms that produce privacy-preserving results with higher accuracy than existing Differential Privacy solutions. Applicability is our first priority, ensuring that our Differential Privacy algorithms can be seamlessly integrated into a wide range of applications and systems, and without changing the methods, tools or languages you use to interact with data. Whether you are in healthcare, finance, telecommunications, or any other industry, our cutting-edge solutions are designed to safeguard sensitive information while maintaining the utility and integrity of your data. Our commitment to applicability extends to easy deployment, scalability, and adaptability, allowing organizations of all sizes to benefit from state-of-the-art privacy protection without compromising performance.

Read more about our Differential Privacy technology

TL;DR: PVML has been verified by legal and technological experts in the privacy field.

The legislation mandates companies to design their products and processes with privacy in mind, meaning that a company is responsible for ensuring and maintaining the privacy of the personal data it handles. We work alongside a legal team and various security and privacy experts who provide guidance and validation throughout our development process, thereby ensuring that our Differential Privacy algorithms and overall approach maintain individuals’ privacy in accordance with various privacy regulations. Furthermore, we undergo rigorous external audits to ensure that our solution adheres to the highest standards of privacy and security and is SOC2 compliant.

Read more about Differential Privacy

TL;DR: Yes, anonymization is an outdated technique that leaves expensive data value on the table and fails to guarantee privacy, especially in the current age of AI.
Yes! Even when removing personally identifiable information (PIIs), the resulting records often include unique combinations of variables and features that might be linked to other publicly available information in order to re-identify specific people or leak sensitive information. In practice, as long as useful information about individuals is included in the data, it is vulnerable to re-identification attacks (and therefore, not anonymous).

Moreover, as we transition into an era where data is not only accessed by people but increasingly by advanced AI systems, the risks escalate. AI, being smarter, faster, and exposed to a wealth of information, introduces new challenges to traditional anonymization methods. These intelligent systems can perform intricate attribute inferencing, extracting nuanced insights and patterns that may not be readily apparent to human users. This capability, if exploited by human users, poses significant risks of intentional misuse. Moreover, there’s a potential for unintentional mistakes by AI, leading to inadvertent exposure of sensitive information, further amplifying the challenges in safeguarding data integrity and privacy.

Therefore, the evolving landscape of technology requires a comprehensive approach to anonymization to safeguard against risks posed by both human and AI access. PVML’s data protection technology is grounded in mathematics and engineered for the age of AI, ensuring heightened protection against data vulnerabilities and privacy breaches regardless of whether data is accessed by human users, applications, or AI models.

Read more about the downfall of anonymization on our blog

TL;DR: No.
Your sensitive data stays wherever it is located (on-premise / on-cloud) and our platform does not require any duplication or modification of the data.

Read more about our deployment and architecture

PVML. Data Peace
Of Mind.

Experience the freedom of real-time
analytics and the power of data
sharing, all while ensuring
unparalleled privacy.
Book a Demo
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