Responsible Al

PERAI for AI Workbench

About OWASP

The Open Web Application Security Project (OWASP) is a globally recognized non-profit organization committed to improving software security. Through a range of resources, tools, and community support, OWASP helps developers & organizations build secure applications. As the field of machine learning (ML) grows, so does the need for robust security measures to protect ML systems from unique threats. OWASP extends its mission to include the security of ML applications, providing guidelines and frameworks to help mitigate risks and ensure the safe deployment of these advanced technologies.

In the realm of machine learning, integrating data privacy, data protection, responsible AI, and security is crucial. These elements must function synergistically, guided by principles of Privacy by Design and Responsible AI, to effectively mitigate the myriad of potential attacks on machine learning models.

OWASP’s Recommended Mitigation Strategies along the ML Lifecycle

To safeguard machine learning models against various security threats, OWASP has developed a comprehensive set of guidelines and strategies. These recommendations are designed to address vulnerabilities at different stages of the ML lifecycle, ensuring robust and secure deployment of ML systems. Below, we delve into the specific mitigation strategies OWASP suggests for each stage.

Data Collection & Pre-Processing

Threats:

OWASP Recommendations:

AI Supply Chain Attack:
‍Compromising components or processes in the data supply chain, such as pre-trained models or data libraries.
Ensure secure data collection and verify third-party models.
Input Manipulation Attack: Feeding crafted inputs into data collection to corrupt the model’s learning.
Implement strict data validation and sanitization.
Data Poisoning: Injecting malicious data into the
dataset to corrupt the model from the start.
Use outlier detection to exclude malicious data.
Data Privacy Breaches: Exposing sensitive data during collection or
storage, leading to unauthorized access.
Apply encryption and masking to protect sensitive data.
Model Training & Evaluation

Threats:

OWASP Recommendations:

Model Poisoning: Introducing malicious data points during training to alter the model's behaviour.
Employ adversarial training to detect poisoned data.
Transfer Learning Attack: Exploiting vulnerabilities in pre-trained models to introduce malicious behaviour in new models.
Ensure thorough vetting of pre-trained models.
Adversarial Testing: Using malicious inputs during evaluation to expose model weaknesses.
Use adversarial examples to test model robustness.
Hyperparameter Manipulation: Tampering with training configurations to degrade model performance or introduce vulnerabilities.
Monitor and validate hyperparameter settings.
Model Deployment & Inference

Threats:

OWASP Recommendations:

Adversarial Attacks: Crafting inputs to deceive the model into making incorrect predictions.
Implement input validation and anomaly detection.
Evasion Attacks: Designing inputs to bypass security measures and produce harmful outputs.
Use anomaly detection to spot evasion attempts.
Membership Inference Attack: Determining if a specific data point was part of the training dataset, exposing sensitive information.
Add noise to data and queries to protect privacy.
Model Theft: Extracting a model’s functionality or intellectual property without access to its training data.
Apply differential privacy to query responses.
Output Integrity Attack: Manipulating the model’s outputs to produce incorrect or harmful results.
Use masking and redaction to ensure output integrity.
Model Maintenance / Common threats across the ML Lifecycle

Threats:

OWASP Recommendations:

Model Inversion: Inferring sensitive training data from the model’s outputs.
Use differential privacy to protect data outputs.
Model Extraction: Duplicating the model’s functionality without access to the original training data.
Use federated learning to minimize data exposure.
Model Skewing: Introducing biases or manipulating data to skew the model's learning.
Implement bias detection tools during training.
PERAI's Approach to Addressing OWASP Guidelines and Mitigating ML Attacks

PERAI is continually advancing to address the myriad of security and privacy challenges in the machine learning lifecycle. Currently, PERAI integrates foundational principles of Privacy by Design and Responsible AI, leveraging Privacy Threat Modeling (PTM) and Privacy Enhancing Technologies (PETs) to mitigate key threats. While several critical aspects have already been implemented, such as data validation, sanitization, and differential privacy, some OWASP recommendations are still in the process of being fully integrated. However, as the PERAI industry matures, it is committed to fully incorporating OWASP guidelines. This future development will ensure comprehensive protection and privacy throughout the machine learning process.

Getting Started with PERAI

Begin your journey with Privacy Enhancing and Responsible AI (PERAI) Technologies to strategically differentiate your organization, ensure regulatory compliance, and unlock the full potential of data in the Data & AI era.

References:

Note: Please note that the information provided in this blog reflects the features and capabilities of Privasapien products as of the date of posting. These products are subject to continuous upgrades and improvements over time to ensure compliance with evolving privacy regulations and to enhance data protection measures.

Responsible Al

PERAI for AI Workbench

PERAI for AI Workbench

Introduction

In today’s digital era, personal data is collected, stored, and processed at unprecedented rates. From social media interactions to online shopping, your personal information is constantly being gathered. To safeguard this data, the European Union implemented the General Data Protection Regulation (GDPR) on May 25, 2018. This comprehensive data protection law sets the standard for data privacy, affecting businesses worldwide. Understanding GDPR is crucial for both individuals and businesses to ensure compliance and protect personal data.

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What is GDPR?

GDPR stands for General Data Protection Regulation. It was introduced to give individuals more control over their personal data and to hold businesses accountable for their data practices. GDPR is considered the strictest data protection regime globally, applicable to both private and government entities, whether within the EU or beyond. It specifically addresses the handling of personal data, with anonymized data falling outside its scope.

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Definition of Personal Data

Under GDPR, personal data is defined as any information relating to an individual who can be directly or indirectly identified. This broad definition includes names, email addresses, meta data and location data, among others.‍

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Importance of GDPR Compliance

Failing to comply with GDPR can lead to severe penalties, including fines of up to 20 million Euros or 4% of global turnover for major violations. Compliance is also a key customer requirement for B2B companies, as non-compliance could result in lost business opportunities. Additionally, GDPR compliance can serve as a brand differentiator, as consumers increasingly value data privacy.

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The 7 Principles of GDPR

GDPR is built on seven core principles that guide its comprehensive legislation:

1. Lawfulness, Fairness, and Transparency:

• Lawfulness: Establish a legal basis for processing data, such as consent, contract, legal obligation, protection of vital interests, public task, or legitimate interests.

• Fairness: Ensure data processing is done in ways individuals would reasonably expect, adhering to promises made during data collection.

• Transparency: Provide clear and intelligible notices to users, enabling them to make informed decisions.

2. Purpose Limitation: Clearly specify the purposes for data processing at the time of collection and limit processing to these purposes. If new purposes arise, obtain user consent, or conduct a compatibility test.

3. Data Minimization: Collect only the minimum necessary data to fulfil the stated purpose, reducing the risk and burden of managing excessive data.

4. Accuracy: Maintain accurate and up-to-date data, regularly checking for and rectifying inaccuracies.

5. Storage Limitation: Retain data only as long as necessary for the specified purposes, with clear retention policies and procedures for data deletion or anonymization.

6. Integrity and Confidentiality (Security): Implement appropriate security measures to protect data from unauthorized access, loss, or damage.

7. Accountability: Demonstrate compliance with GDPR principles through documentation and proactive measures, ensuring responsibility at every stage of data processing.

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Rights of Individuals under GDPR

GDPR grants individuals several rights over their data, including:

• Right to be informed

• Right of access

• Right to rectification

• Right to erasure (Right to be forgotten)

• Right to restrict processing

• Right to data portability

• Right to object

• Rights related to automated decision-making and profiling

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Recent Developments and Trends in GDPR

As of 2024, GDPR enforcement continues to intensify, with supervisory authorities across Europe imposing record fines. In the past year alone, fines have totalled EUR 1.78 billion, marking a 14% increase from the previous year. Major tech companies like Meta have faced significant penalties, emphasizing the ongoing scrutiny of big tech and social media platforms.

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Key trends to watch in 2024 include the increasing focus on AI and data privacy, the regulation of biometric data, and the evolving landscape of data sovereignty and localization. The European Commission’s new GDPR Procedural Regulation aims to streamline cooperation between national data protection authorities, enhancing the efficiency and consistency of GDPR enforcement across the EU.

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Conclusion

GDPR is a comprehensive and complex regulation designed to protect personal data and uphold individuals’ rights. For businesses, it means implementing robust data protection measures and maintaining transparency and accountability. Compliance not only avoids hefty fines but also builds trust with customers, positioning your brand as a privacy-conscious entity. Embrace GDPR as a fundamental aspect of your business operations to ensure data protection and foster long-term customer relationships.

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How Privasapien PERAI Platform Adds Value

Privasapien PERAI platform significantly enhances GDPR compliance efforts by providing advanced privacy risk assessments and management tools. The platform’s AI-powered solutions offer dynamic privacy threat modelling, expert-grade anonymization, and state-of-the-art encryption to ensure data protection while enabling business insights. Additionally, PERAI emphasizes responsible AI practices, ensuring AI models comply with data protection regulations, maintain transparency, mitigate biases, and uphold ethical standards. Integrating PERAI into your operations helps you stay compliant, protect customer data, and build trust with your clients.

Reference link: https://gdpr-info.eu/

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PERAI for AI Workbench

Understanding privacy risk with Privacy Threat Modelling (PTM)
and implementing privacy controls with Privacy Enhancing Technologies (PETs)

"Privacy by Design is proactive, not reactive. It prevents privacy issues before they arise, aiming to avoid risks rather than remedy them post-incident. Essentially, it ensures privacy measures are in place from the start."

In the rapidly evolving digital landscape, the stakes for data protection are exceedingly high. For breaches, the GDPR allows for fines of up to 4% of an organization's annual global turnover or €20 million (whichever is higher). In addition, Recent studies reveal that the average cost of a data breach globally is approximately $4.35 million, and breaches have been reported to occur at a rate of one every 39 seconds.

GDPR fines have demonstrated the severe consequences of non-compliance. In July 2019, British Airways faced a potential £183 million fine for a breach affecting 500,000 customers. In January 2019, Google was fined €50 million by France's CNIL for lack of transparency in ad personalization. More recently, in May 2023, Meta was fined a record €1.2 billion by the Irish Data Protection Commission for inadequate protection of European user data against U.S. surveillance. These incidents not only pose risks of substantial financial loss but also lead to severe reputational damage and eroding public trust.

The U.S. Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence places a significant emphasis on Privacy-Enhancing Technologies (PETs). These technologies are aimed at reducing privacy risks in data processing, and the directive encourages federal agencies to adopt these tools to protect consumer privacy in the context of AI development. This approach underscores the U.S. government's commitment to safeguarding privacy while fostering AI innovation.

For regulators, analysts, and data-centric organizations, adopting a proactive approach to data privacy is not just a prudent measure, but an imperative one. In the digital age, the balance to be struck is between privacy and utility, not positioning them as opposing forces. This perspective encourages the integration of robust privacy measures that enhance, rather than hinder, the power of data analysis, ensuring that data protection is built into the system from the ground up and embedded in the design process, Hence, Privacy by Design.

Privacy by Design (PbD) can evolve from a conceptual guideline to a concrete implementation within data ecosystems using Privacy Threat Modelling (PTM) & Privacy Enhancing Technologies (PET). PTMs allow for the translation of abstract privacy principles into auditable, repeatable actions that can be methodically applied to data. This ensures that privacy measures are consistently implemented and are not merely theoretical. PETs complement this by offering automatic, mathematical methods to secure data through technologies such as differential privacy, expert determination anonymization, federated learning, secure multi-party compute etc.

Decoding Privacy by Design: A Global Standard and Regulations Overview

Regulation/standards
Key Quote from GDPR
General Data Protection Regulation (GDPR) - EU

The European Union’s GDPR was one of the first major legislations to embed Privacy by Design into its text. Article 25 of GDPR explicitly mandates that data protection measures should be designed into the development of business processes for products & services.

"Data protection by design and by default requires the controller to implement appropriate technical and organisational measures and necessary safeguards, designed to implement data-protection principles in an effective manner and to integrate the necessary safeguards into the processing."
ISO/TR 31700:2023:

This standard offers a focused guideline on Privacy by Design specifically for consumer goods & services.

"Privacy by design refers to design methodologies in which privacy is considered and integrated into the initial design stage and throughout the complete lifecycle of products, processes, or services."
ISO 29100: Privacy Framework:

Privacy Framework ISO 29100 provides a framework for privacy that assists organizations in effectively managing and protecting personal data.

"ISO 29100 establishes a set of privacy principles that guide the collection, use, and handling of personal data, emphasizing the importance of managing privacy risks effectively."
ISO/IEC 20889: Privacy Enhancing Data De-Identification Techniques

This standard details methods to de-identify personal data effectively, ensuring that the risks associated with personal data processing are minimized.

"ISO/IEC 20889 provides specific guidelines for de-identification techniques, aiming to protect individual privacy without compromising the utility of the data."

Framework: From Design to Privacy Implementation

A robust implementation framework is essential for transitioning from the initial design phase to full operational deployment of PbD. ISO 29100 forms a robust blueprint for organizations aiming to adopt PbD, providing clear directions for embedding privacy throughout their operational and data handling practices. This framework involves several key stages:

Privacy Risk Assessment with Privacy Threat modelling
and PET based mitigatory recommendation

As explained earlier, PTMs allow for the translation of abstract privacy principles into auditable, repeatable actions that can be methodically applied to data. Privacy risk assessment is a crucial process for identifying, analysing, and mitigating potential threats to the confidentiality, integrity, and availability of personal data.

Process

  • Privacy Threat Modelling based risk assessment: Utilizing advanced privacy attack simulation techniques to analyse risk in data flows, system architectures, and potential attack vectors.
  • PET-based Mitigatory Recommendations: Implementing appropriate Privacy-Enhancing Technologies (PETs) depending upon the type of data or insight flow requirement to mitigate identified risks.
  • Integration of PTM and PET with business ecosystem:  Integrate PTM tools with data sources and data flows, connect DPIA process with PTM to make it augmented DPIA, integrate the results into data pipelines to make it DevPrivacyOps, configuring PET in collaboration with business teams, verifying PET effectiveness with PTM, sharing output for teams to follow.
  • Methodologies like LINDDUN and MITRE are instrumental in providing a globally uniform approach to identifying and mitigating privacy risk.

Privacy Controls: Leveraging PETs for Data Protection

PETs encompass a diverse range of technologies and methodologies designed to enhance privacy throughout the data lifecycle, from collection and storage to processing and sharing. In this section, we explore the integration of PETs into privacy controls, focusing on key standards and guidelines such asISO 31700: 2023,  ISO 29100: 2024 and ISO 20889: 2018. These standards provide frameworks for implementing effective privacy controls and aligning with global best practices in data protection.

PET
Expected Functionality
Cryptographic Protection
Ensures confidentiality and integrity of sensitive data through encryption techniques.
Anonymous Data Transformation
Anonymizes personally identifiable information (PII) in datasets to preserve privacy while maintaining data utility.
Access Governance
Regulates access to sensitive information based on user roles and permissions, ensuring data privacy and compliance.
Tokenization Solutions
Replaces sensitive data elements with unique tokens to minimize the risk of data exposure and unauthorized access.
Masking Techniques
Conceals sensitive information in datasets, protecting privacy during data processing, testing, and sharing.
Data Obfuscation Methods
Obscures sensitive data elements to maintain data integrity while safeguarding privacy.
Homomorphic Encryption Solutions
Enables secure computation on encrypted data, ensuring privacy-preserving data processing.
Differential Privacy Measures
Adds statistical noise to query responses to preserve individual privacy during data analysis.
De-identification Strategies
Removes direct and indirect identifiers from datasets to prevent re-identification and protect individual privacy.
Privacy-Preserving Analytics
Extracts insights from data while ensuring privacy and confidentiality through privacy-preserving techniques.

Privacy Controls: Leveraging PETs for Data Protection

At PrivaSapien, we enhance & refine the enterprise level data privacy management. Our advanced solutions in Privacy Threat Modeling (PTM), Privacy Enhancing Technologies (PETs), and responsible AI governance set robust safeguards, allowing organizations to secure and fully leverage their data.

Category
Product
Description
Privacy Threat Modeling (PTM)
AI-powered tool that performs dynamic assessments of privacy risks, visualizing potential threats and helping organizations mitigate risks proactively.
Nebula is a data analysis tool that scans and summarizes Personally Identifiable Information (PII) in unstructured data within a database. It helps organizations improve data security and compliance by providing insights into PII distribution across various file types.
Facilitates mapping, analysis, and documentation of DPIA activities by augmenting with Privacy threat modeling , ensuring GDPR compliance and promoting informed privacy decision-making.
Privacy Enhancing
Technologies (PETs)
Advanced data anonymization including expert grade statistical anonymization with mathematical proof that ensures sensitive data can be used for analytics without compromising individual privacy
Features state-of-the-art encryption and decryption capabilities, securing data at the most granular level with customizable key generation strategies, cryptographic data sharing, API based purpose centric de-identification and data minimisation for cross border transfers
Employs advanced differential privacy techniques to protect individual data points during analysis, ensuring data confidentiality in analytics.
Generates synthetic data that mirrors real-world datasets but contains no real personal information, allowing for safe use in testing and development environments
Privacy Threat Modeling (PTM)
RAGAM is a data management solution that offers encryption and tokenization to protect unstructured data. It automatically encrypts data used for model training, supports decryption with proper permissions, and includes data redaction features. RAGAM integrates with external services like perplexity.ai, allowing encrypted or tokenized data to be processed securely, thus protecting sensitive information during data workflows and minimizing the risk of unauthorized access.
Responsible AI
Privacy Risk and Generation AI Governance specially tailored for Large Language Models (LLMs). Organizations can safeguard their data, navigate complex risks, and ensure responsible AI practices with ease, integrating user safety, AI model security and LLM governance as per various emerging AI regulatory requirements
RAGAM is a data management solution that offers encryption and tokenization to protect unstructured data. It automatically encrypts data used for model training, supports decryption with proper permissions, and includes data redaction features. RAGAM integrates with external services like perplexity.ai, allowing encrypted or tokenized data to be processed securely, thus protecting sensitive information during data workflows and minimizing the risk of unauthorized access.

References

  1. https://www.sciencedirect.com/science/article/abs/pii/S0267364917302054
  2. https://gdpr.eu/fines/
  3. https://www.ibm.com/topics/data-privacy#:~:text=Violators%20can%20be%20fined%20up,Digital%20
    Personal%20Data%20Protection%20Act
  4. https://linddun.org/
  5. https://www.crowdstrike.com/cybersecurity-101/mitre-attack-framework/
  1. https://www.sciencedirect.com/science
    /article/abs/pii/S0267364917302054
  2. https://gdpr.eu/fines/
  3. https://www.ibm.com/topics/data-privacy#:~:text=Violators%20can%
    20be%20fined%20up,Digital%20
    Personal%20Data%20Protection%20Act
  4. https://linddun.org/
  5. https://www.crowdstrike.com/
    cybersecurity-101/mitre-attack-framework/
Introducing the PERAI for AI Workbench: Revolutionizing AI with Privacy at its Core. Our comprehensive platform ensures robust data protection, regulatory compliance, and responsible AI deployment.

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Understanding AI Workbenches

An AI workbench is a platform or toolset designed to support the development, deployment, and management of artificial intelligence (AI) and machine learning (ML) applications. These platforms typically provide various functionalities and services that assist data scientists, developers, and engineers in buildin gand deploying AI models efficiently. AI workbench is a comprehensive solution that enables businesses to unlock the potential of AI by tailoring advanced analytics and machine learning models to their unique data, people, and business needs. AI Workbenches are provided as a package by different Cloud providers such as AWS, GCP etc. It broadly delivers a comprehensive set of services including simplified data access, data visualization, cost-effective infrastructure management, enterprise security features, integration with Data Lake and Spark, deep Git integration, seamless CI/CD capabilities, and a user-friendly notebook viewer for sharing outputs.

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Various Components:

While the specific names and functionalities of some of the components mentioned below may vary depending on the AI workbench platform, they all provide a set of tools and services that data scientists and developers can use to build, test, deploy and manage machine learning models. They include:

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Transforming AI workbench to Responsible AI Workbench with Privacy by Design:

Addition of Privacy Preservation Machine learning and Privacy preserved processing layer in AI Workbench.

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AI Workbench provides a comprehensive platform for data analysis, machine learning (ML) development, and deployment. With its wide range of features, it is essential to incorporate privacy-enhancing technologies and responsible AI practices to ensure privacy by design, ethical AI development, and trustworthy AI systems. Here are a the technologies, processes and practices that can be inculcated in this layer:

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  • Privacy Threat Modelling & DPIA : Assessing potential risks to individuals' privacy within a AI system or application. It entails identifying and analysing potential vulnerabilities, threats, and attack vectors that could compromise user privacy and mapping to the regulation (LINDUNN, NIST Privacy Framework, MITRE, || ISO/TR 31700-2)
  • ROPA : Establish clear guidelines regarding the rights of individuals, obligations of the organization, and permissions required for data usage. ROPA helps ensure that privacy rights are respected throughout the AI lifecycle. (ISO 42001 A.6.2 || EO on Trustworthy AI – Sec 7.2 (z))
  • Data Minimization & Purpose Limitation : Adopt data minimization strategies to reduce the collection and retention of unnecessary personal data and sharing data for relevant purpose and context only. Minimizing data exposure minimizes privacy risks towards AI. (ISO 31700- 6.2 || EO on Trustworthy AI – Sec 1 (e)
  • Synthetic Data : Utilize synthetic data generation techniques to create representative datasets for model development without exposing real user information. Pseudonymization techniques can further anonymize data while retaining its utility for analysis. . EO on Trustworthy AI – Sec 3 (z)
  • Data Pseudonymization : Data pseudonymization in AI workbenches ensures privacy, compliance, and fairness by anonymizing data for responsible AI, reducing risks, promoting ethical practices, and enabling secure collaboration. EO on Trustworthy AI – Sec 3 (z)
  • Data Anonymization : Data anonymization, vital for responsible AI, as it safeguards privacy by preventing re-identification of individuals. Techniques like k-anonymity, t-closeness, and local differential privacy ensure data utility while dropping PII. Anonymized data sets can be used for3 model building and ensure privacy preservation in MLOps (ISO/TR 31700 Section 7.2 || ISO 42001 B.3.3 || NIST AI RMF 3.6)
  • Consent & Data Subject rights : Prioritize obtaining informed consent from data subjects for data processing activities. Ensure that individuals have the right to access, rectify, and erase their personal data as per data subject rights regulations. ( NIST AI RMF AI - Risk and Trustworthiness Section 3.6 || ISO 42001 – Data for AI System B.7.3 )
  • Data Preparation & Feature Engineering : When dealing with sensitive personal data, such as healthcare records or financial information, it is crucial to anonymize or pseudonymize the data before using it for analysis or model training Article 29 Working Party, NIST SP 800-188 from ISO/TS 25237:2008)
  • Differential Privacy : It is a technique that adds controlled noise to data to protect individual privacy while allowing for meaningful analysis. (National Strategy To Advance Privacy-Preserving Data Sharing And Analytics, NIST.SP.800-22)
  • Synthetic Data Generation : Synthetic data can be generated to mimic the real data's statistical properties while protecting actual identities and confidential information. This allows the development of models in healthcare or finance without compromising patient confidentiality or financial security. (National Strategy To Advance Privacy-Preserving Data Sharing And Analytics)
  • Federated Learning: Multiple devices or edge nodes jointly train a shared model while keeping their data decentralized and private. This approach involves aggregating local model updates or gradients from individual devices, allowing the global model to learn from diverse data sources without directly accessing raw data. By preserving data privacy and minimizing data transmission, federated learning facilitates scalable and efficient model training across distributed environments. (National Strategy To Advance Privacy-Preserving Data Sharing And Analytics). Utilize federated learning to train AI models across distributed environments while preserving data privacy. This approach allows for collaborative model training without centralizing sensitive data.
  • Model Inference & Deployment : Model inference and deployment involve utilizing techniques such as risk detection, risk summarization, synthetic prompt engineering, and risk-based query control to enhance the effectiveness and efficiency of deploying AI models in real-world applications.(NIST Adversarial Machine Learning || NIST AI Risk Management Framework - Controls)
  • RAG Models Risk assessment : RAG models to be continuously assess and manage privacy risks associated with the AI systems. This involves monitoring for privacy threats, User safety, Mitigating vulnerabilities, updating risk assessments, and ensuring compliance with Privacy and AI regulations across verticals and globally.

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PERAI: The Perfect Blend of Privacy and Responsible AI for Innovation

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PrivaSapien’s PERAI (Privacy enhancing and responsible AI) technologies brings together a set of tools that seamlessly integrate privacy into every layer of AI development, making it a perfect fit for companies looking to innovate in AI responsibly.

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PrivaSapien’s PERAI (Privacy enhancing and responsible AI) technologies brings together a set of tools that seamlessly integrate privacy into every layer of AI development, making it a perfect fit for companies looking to innovate in AI responsibly.

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Here’s why:

Regulations at the Heart: At a time when privacy regulations are tightening up, PrivaSapien offerings are built with these regulations in mind. This means firms can stay ahead of the curve, not just meeting today's standards but ready for tomorrow's too. It isn't about just adding a privacy layer; it's embedding privacy and responsible AI into its AI workbench from the ground up. It's about giving firms the tools to innovate freely while building trust with clients and users by showing a real commitment to protecting privacy & responsible AI.

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In simple terms, with PERAI, firms get to lead in AI, safe in the knowledge that it's doing right by its users' privacy. That's not just good for business; it's essential in today's world where privacy matters more than ever.

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  • Privacy X-Ray (Privacy Threat Modelling, Attack Simulation, Mitigatory recommendation). Privacy X- ray helps organizations visualize privacy risks and provides actionable insights for mitigating data risks. Empowers DPO, application and analytics teams to do automated & mathematical privacy risk assessment based on which DPIA can be done.
  • PrescripTron (Augmented Data Protection Impact Assessment (DPIA). Prescriptron facilitates augmented DPIA for enterprises, ensuring necessary and proportionate data processing. It aids in responsible data management by assessing privacy intrusion risks and enables proactive identification and mitigation of potential threats, enhancing overall data security and compliance with regulations.
  • EventHorizon (Anonymization). Event Horizon goes beyond protecting individual identities and allows responsible data collaboration. Employs context-based anonymization to safeguard sensitive information while maintaining data utility.
  • CryptoSphere (Pseudonymization). CryptoSphere helps organization towards Privacy Loss Prevention and in pseudonymized data collaboration in complex business ecosystems enabling data minimization.
  • DataTwin (Synthetic Data). Data Twin creates synthetic representations of a data ecosystem. Helps organizations perform extensive simulations without exposing sensitive data to drive decision and innovation.DifferentialInsight (Privacy Preserved Insight Sharing).
  • DifferentialInsight (Privacy Preserved Insight Sharing). DifferentialInsight employs differential privacy and establishes a privacy budget. Users can query the database within the allocated budget. Privacy is maintained by adjusting the budget in accordance with privacy requirements, restricting queries if the privacy threshold is at risk.
  • PrivaGPT (Privacy Preserved LLMOps & MLOps and AI Governance). PrivaGPT is A revolutionary AI Governance product, which can enable organization in building secure, trustworthy &responsible AI ecosystem by empowering end user safety, model security and privacy preserved data for training, by understanding the risk at prompt/ response level and using privacy preserving synthetic prompt engineering.
  • RAGAM: Retrieval Augmented Generation (RAG) is an AI framework for retrieving facts from and external knowledge base to enable large language models (LLMs) to include external context in its answers without being trained on it. But while exposing data to LLMs through RAG, organizations may expose sensitive data which may lead to breaches. RAGAM is RAG with Assessment and Mitigation, enabling privacy & security by design approach to RAG empowered with advanced attribute and context identification, pseudonymization (Cryptographic & Tokenized) and context based de - identification by authorized entities.
  • Nebula is one of our AI based intelligent solution for managing privacy in unstructured data. It has seamless scanning capabilities which can unveils hidden insights within data repositories. It can scan diverse file types, providing comprehensive summaries of personally identifiable information (PII) presence, Predefined sensitive internal information and showcases them in consumable form for businesses to aid decision making.

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Responsible Al

PERAI for AI Workbench

In this Article
Navigating US – FTC regulations with PTM, PETsFTC privacy & Security requirementsFTC Safeguards rule interpretation: 3Ps – People, Process & PETsGetting Started: Data privacy with Privasapien PET Solutions
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In an era where data breaches & privacy concerns are at the forefront, businesses must prioritize the protection of consumer information. The US Federal Trade Commission (FTC) plays a pivotal role in enforcing data privacy laws and ensuring that companies adhere to stringent standards. To navigate these regulations effectively, businesses can leverage Privacy Threat Modeling (PTM) & Privacy Enhancing Technologies (PETs) to safeguard sensitive information and ensure compliance.

Privacy Threat Modeling (PTM) provides a structured approach to identifying and addressing potential privacy risks, enabling organizations to proactively manage threats to consumer data. Similarly, Privacy Enhancing Technologies (PETs) encompass a range of tools and techniques designed to protect personal data and maintain privacy. These technologies, when implemented correctly, can help businesses meet FTC requirements and mitigate the risk of data breaches.

FTC privacy & Security requirements

The Federal Trade Commission (FTC) expects businesses to prioritize the protection of consumer data through the following key aspects:

  • Implement Robust Security Measures
  • Ensure Transparency
  • Proactive Risk Management through Privacy Threat Modeling (PTM)
    o Leverage technologies like data anonymization, tokenization, and differential privacy to enhance data security and ensure privacy while allowing for data utility.
  • Utilize Privacy Enhancing Technologies
    o Leverage technologies like data anonymization, tokenization, and differential privacy to enhance data security and ensure privacy while allowing for data utility.

Act/Rule (Description)

Brief about Requirements

COPPA
Children’s Online PrivacyProtection Act
  • Gives parents control over information websites collect from kids.
  • Additional protections and streamlined procedures for compliance.
  • Safe Harbor Program, parental consent methods.
Health Privacy
‍Governed by the FTC Act and Health Breach Notification Rule
  • Honor privacy promises.
  • Maintain appropriate security.
  • Notify affected parties and the FTC in case of a breach.
Consumer Privacy
Ensures businesses comply with their privacy policies and are transparent about data practices
  • Honor privacy policies.
  • Clear communication of data usage practices.
  • Avoid deceptive or unfair claims.
Fair Credit Reporting Act (FCRA)
  • Compliance with FCRA requirements.
  • Responsibilities for using, reporting, and disposing of information in consumer and credit reports.
Data Security
Applies to financial institutions providing financial products or services
  • Implement a sound security plan.
  • Collect only necessary data.
  • Keep data safe and dispose of it securely.
  • Utilize FTC resources.
Gramm-Leach-Bliley Act
Applies to financial institutions providing financial products or services
  • Explain information-sharing practices to customers.
  • Safeguard sensitive customer data.
Red Flags Rule
Part of the Fair Credit Reporting Act’s Identity Theft Rules
  • Implement a written Identity Theft Prevention Program.
  • Detect, prevent, and mitigate identity theft.
EU-U.S. Data PrivacyFramework (DPF)
  • Mechanism for transferring personal data between the EU and the US.
  • Self-certify compliance with DPF principles.
  • Non-compliance may violate Section 5 of the FTC Act.
Privacy Shield
Previously governed data transfer between the EU andthe US,
replaced by the Data Privacy Framework
  • Comply with ongoing obligations under Privacy Shield.
  • Follow robust privacy principles for international data transfers.
  • Accurate privacy policies.
U.S.-EU Safe Harbor
  • Legal mechanism for data transfer between the EU and the US.
  • Ongoing obligations for previously transferred data.
  • FTC enforcement of compliance.
Tech Guidance
Guidance for tech companies developing tools like mobile apps, smartphones
  • Consider privacy and security implications in product development.
  • Follow platform guidelines and best practices for secure development.

FTC Safeguards rule interpretation: 3Ps – People, Process & PETs

The Safeguards Rule majorly applies to financial institutions under the FTC’s jurisdiction, broadly defined to include activities that are financial in nature, such as mortgage lenders, tax preparation firms, and payday lenders.

Process

  • Risk Assessment (PTMs)
  • Safeguards Implementation
  • Monitoring & Testing
  • Incident Response Plan

People

  • Security program Manager
  • Staff training
  • Service provider oversight
  • Board Reporting

Privacy Enhancing Technologies (PETs)

  • Data Anonymization: Use techniques like k-anonymity, t-closeness, and differentialprivacy to transform personal data into an untraceable format.
  • Encryption: Encrypt data during storage and transmission to ensure it remainsunreadable to unauthorized parties.
  • Tokenization: Replace sensitive data with unique tokens to reduce the risk of exposureduring transactions and storage.
  • Differential Privacy: Add noise to datasets to protect individual records while allowingmeaningful analysis.
  • Synthetic Data Generation: Generate data that mimics real data but contains no actualpersonal information, making it safe for testing, development, and training machinelearning models.

Getting Started: Data privacy with Privasapien PET Solutions

Privasapien offers advanced solutions that align with Privacy Enhancing Technologies (PETs) to help businesses comply with FTC regulations and protect consumer data. Here’s how Privasapien products address key requirements:

Requirement

Explanation

Data Anonymization
  • Privacy X-ray: Performs privacy threat modelling on structured data and provides risk scores with mitigation recommendations.
  • Event Horizon: Provides full-fledged anonymization using k-anonymity, t-closeness, and differential privacy.
Encryption
  • Cryptosphere: Implements pseudonymization at the column and cell level with on-demand decryption.
  • RAGAM: Offers encryption and tokenization for unstructured data, with options for encrypted data usage in model training.
Tokenization
  • Cryptosphere: Enhances security by tokenizing sensitive data at granular levels.
  • RAGAM: Provides robust tokenization for unstructured data alongside encryption.
Differential Privacy
  • Differential Insight: Allows users to query databases using differential privacy principles.
Synthetic Data
  • Data Twin: Produces synthetic data that maintains the context of the original data.
  • PrivaGPT: Acts as an interface between the user and any large language model (LLM), creating synthetic prompts.

References:

Note: Please note that the information provided in this blog reflects the features and capabilities of Privasapien products as of the date of posting. These products are subject to continuous upgrades and improvements over time to ensure compliance with evolving privacy regulations and to enhance data protection measures.

May 2, 2024
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January 31, 2024
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