PERAI Technologies = Privacy Enhancing Technologies + Responsible AI Technologies

Responsible Al

PERAI^TMTechnologies = Privacy Enhancing Technologies + Responsible AI Technologies

About OWASP OWASP’s Recommended Mitigation Strategies along the ML Lifecycle Data Collection & Pre-Processing Model Training & Evaluation Model Deployment & Inference Model Maintenance / Common threats across the ML Lifecycle PERAI's Approach to Addressing OWASP Guidelines and Mitigating ML Attacks Getting Started with PERAI

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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:

https://owasp.org/www-project-machine-learning-security-top-10/

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.

PERAI^TMTechnologies =

Privacy Enhancing Technologies + Responsible AI Technologies

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.

References

Building Privacy Enhanced AI Systems that are privacy conscious, attack resistive, ethical, trustworthy and fair, enabling AI models to comply with global privacy and AI regulations.

‍Challenge: Privacy Regulations + Responsible AI Regulations emerging across the globe.

As per EU’s AI Act, penalties for a non-compliant AI model deployed by a provider can range from € 35million or 7% of global turnover to € 7.5 million or 1.5% of turnover. This was preceded by GDPR, which set the global standard for privacy regulations and spread like wild fire across the globe. It has a maximum penalty of up to €20 million, or 4% of the firm’s worldwide annual revenue from the preceding financial year, whichever amount is higher. Because data is the raw material to build AI systems, AI systems which are not compliant with Privacy and AI regulations can attract a penalty of upto 11% (7% +4%) of their global revenue just in one geography EU. We have not even spoken about penalties in US,India, Middle East, China, Australia and 120 other countries which already have or coming up similar privacy and AI regulations.

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Privacy & AI Attacks: Siloed traditional approach to Privacy & AI compliancewon’t work

There are different kinds of attacks possible at the data level beyond PII leak, like – singling out attack, linkage attack, inference attack, outlier attack, background knowledge attack, collective privacy attack and more at the data level. Failing to mitigate above privacy risks results in successful execution of emerging AI model attacks like direct attack, transfer attack, jail break attack, evasion attack and information extraction attacks like model stealing attack, attribute inference attack, membership inference attack, model inversion attack etc.

Just removing PII alone in the training data or implementing a protection at the prompt level may give only partial results, like the story of 6 blind men trying to define an elephant. Like an elephant, LLMs are already very complex and not-explainable to start with. A siloed approach of limited privacy protection and name-sake disconnected model security significantly increases the risk of regulatory violations and succumbing to model attacks.

If we don’t have a holistic approach to privacy preserved model building, the resultant models may become non-compliant with privacy and AI regulations across the globe. Hence a multi stage approach to privacy and responsible AI compliance is a foundational necessity.

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PERAI Technologies = Privacy Enhancing Technologies + Responsible AITechnologies

To build a regulatory complaint LLM, its critical that organizations focus on implementing PERAI Technologies across the LLM Ops or ML Ops pipeline starting from using Privacy Threat Modeling during data collection, Privacy Enhancing Technologies during data processing and Responsible AI based guardrails during inference and governance. Let us first understand PETs, RAI and then PERAI. This has to be an end-to-end integrated approach to privacy and responsible AI compliance.

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What are Privacy Enhancing Technologies or PETs?‍

A set of foundational technologies that provides privacy protection with mathematical guarantees for various use cases of data sharing or processing. There are different kinds of Privacy EnhancingTechnologies with different kinds of protection, which are suitable of different kinds of use cases.

While complying with data minimization requirements of privacy and AI regulations, an organization has to understand the primary privacy threat they want to mitigate in different data processing requirements and accordingly use suitable PET for data sharing.

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Below are some of the Privacy Enhancing Technologies:

Differential Privacy
Statistical Anonymization (K-anonymity, t-closeness & LDP)
Synthetic Data
Pseudonymization
Federated Learning
Others – SMPC, ZKP, FHE

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Global Regulations for PET + RAI:

Privacy Enhancing Technologies have been called out as one of the key enablers in AI regulations across the globe like:

US - Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (Sec 9 – Protecting Privacy)
US – NIST AI RMF (Sec 3.6)
India – MyGov – Responsible AI – Privacy Enhancing Strategies
EU – AI Act (Article 10 – Clause 5A)
Saudi Arabia – Ethical AI – Privacy Preservation

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What are Responsible AI Requirements & Technologies:

Responsible AI has 7 key characteristics as per NIST AI Risk Management Framework. They are:

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Organizations have to incorporate necessary technologies, safeguards and policies as part of their model building, deployment and inference practices to ensure above mentioned characters of their AI models.

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What is the need for integrating Privacy Enhancing Technologies &Responsible AI into PERAI? How can PrivaSapien help?

PrivaSapien’s pioneering PERAI technology enables organizations to take an integrated approach toLLMOps. All the 7 characteristics required for building Responsible AI have to be integrated into theMLOps/ LLMOps right from the data collection process. Below is the approach for building ResponsibleAI LLMOps, along with corresponding mitigatory technologies:

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1. Data Collection & DPIA

Requirements: Safe, Fair & Privacy Aware

Technologies: Privacy Threat Modeling, Bias Assessment, Augmented DPIA, Mitigatory Recommendation & Regulatory requirements

PrivaSapien’s PERAI Products: PrivacyX-ray(PTM), Prescriptron (Augmented DPIA)

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2. Data Preparation & Feature Engineering:

Requirements: Privacy Preserving & Fair

Technologies: Privacy Enhancing Technologies

PrivaSapien’s PERAI Products: EventHorizon (Statistical Anonymization), DataTwin (SyntheticData), Differential Insight (DifferentialPrivacy), CryptoSphere (Cryptographic Pseudonymization)

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3. Privacy Preserved Model Training & Verification

Requirements: Privacy Preserving, Accountable, Transparent & Valid

Technologies: Privacy Enhancing Technologies, DPIA, testing

PrivaSapien’s PERAI Products: Prescriptron (AugmentedDPIA), EventHorizon (Statistical Anonymization), DataTwin (SyntheticData), Differential Insight (DifferentialPrivacy), CryptoSphere (Cryptographic Pseudonymization)

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4. Model Deployment

Requirements: Secure, Transparent

Technologies: Adversarial Attack Detection & Mitigation

PrivaSapien’s PERAI Products: PrivaGPT–Model Security Module

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5. Model Inference

Requirements: Safe, Accountable, Transparent

Technologies: Risk Detection, Risk Summarization, Synthetic Prompt Engineering, Risk based query control

PrivaSapien’s PERAI Products: PrivaGPT– User Safety Module

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6. LLM Governance

Requirements: Fair, Accountable & Transparent

Technologies: Risk summarization, Human Feedback, AI Governance Reporting

PrivaSapien’s PERAI Products: PrivaGPT – LLMGovernance

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Conclusion:

Privacy Enhancing & Responsible AI (PERAI) Technologies are going to be foundational and non-negotiable requirement for organizations in the Data & AI era. Without these technologies - storing, processing, sharing, model building and inferencing are going to be too risky from a compliance and customer protection perspective. PrivaSapien offers a pioneering PERAI platform which can be your partner to leading the Data & AI era.

Responsible Al

PERAI^TMTechnologies = Privacy Enhancing Technologies + Responsible AI Technologies

Navigating US – FTC regulations with PTM, PETs

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:

https://www.ftc.gov/business-guidance/privacy-security

The Gen AI Training Data Transparency Act

As AI continues to revolutionize industries worldwide, the need for ethical and transparent AI development has never been more crucial. With California’s Generative AI Training Data Transparency Act, the state is leading the charge in regulating how AI developers disclose their training datasets. This act marks a pivotal shift, signaling that the age of opaque AI systems is coming to an end. Early adoption of these standards builds trust, Making Gen AI responsible!!

Who Needs to Comply?

If you are a Gen AI model developer, fine tuner, or service provider who has developed, fine-tuned, or made Gen AI-based services available to Californians on or after January 1st, 2022. 

By when to Comply?

You must publish Generative AI Training Data Transparency documentation on your website by January 1st, 2026, for models or services released on or after January 1st, 2022, and for all subsequent releases. 

What are the regulatory requirements?

The published document by developers or service providers must include a high-level summary of the datasets used to train their Gen AI systems or services, available on their website.

What are details to be published?

As per the Generative AI Training Data Transparency Act, the following below 12 attributes of datasets used for training are to be published by the model or service provider:

1. Source

Source or Owners of the datasets

2. Purpose

A Description on – “How does these datasets help in achieving the purpose of the Gen AI model or service?”

3. Volume

The no. of datapoints within the datasets, using general ranges or estimates for datasets that are continuously updated

4. Type

Describe the types of data points in the datasets, including the labels used or key characteristics

5. IP Status

Indicate whether the datasets contain data protected by copyright, trademark, or patent, or if they are entirely in the public domain

6. Ownership

Specify whether the datasets were purchased or licensed for use in the AI system

7. Personal Data

Indicate whether the datasets include personal information as defined under section 1798.140(v) of the California Consumer Privacy Act (August 2024), specifically information that is "identifiable directly or indirectly."

8. Privacy Preserved Data

Indicate whether the dataset includes aggregate consumer information, as defined under section 1798.40(b) of the California Consumer Privacy Act (August 2024).

This refers to data that is "not linked or reasonably linkable to any consumer or household" and clarifies that "aggregate consumer information does not include one or more individual consumer records that have been de-identified."

9. Data Processing

Describe any cleaning, processing, or modification of the datasets, and how these efforts relate to the AI system’s intended purpose.

10.Time/Duration

Specify the data collection period, usage duration, and whether collection is ongoing, along with any time-related obligations.

11. First Use

Provide the date when the datasets were first utilized in the development of the AI system or service.

12. Synthetic Data

State whether synthetic data was or is being used for model training, including details on its functional need, purpose, and how it aligns with the intended goals of the AI system or service.

While these documentation obligations address immediate compliance, they also position companies as ethical leaders in an increasingly regulated global AI market. Organizations that embrace transparency will build trust, positioning themselves favorably in the eyes of customers and regulators alike.

Who is exempted?

AI systems are exempt if their sole purpose is:

Ensuring security and integrity, as defined by Section 1798.140 (ac) of the California Consumer Privacy Act (posted August 2024).
Operating aircraft within national airspace.
Supporting national security, military, or defense purposes, where the system is made available to a federal entity.

How should you prepare for Compliance?

To comply, organizations must adopt a proactive and structured approach to managing their AI models—both published and future versions. This involves enlisting datasets, ensuring compliance with legal and intellectual property requirements, capturing essential dataset attributes, and preparing transparent reports.

For Gen AI models already published

1. Enlist Datasets

AI & Data governance teams to enlist all the datasets used for

a. Model training

b. Fine tuning

c. RAG based inference

2. Compliance Check

For each dataset used in the training process:

a. Consult with your data governance team, Data Protection Officer (DPO), and legal team to ensure compliance with the California Consumer Privacy Act (CCPA), intellectual property (IP), and ownership-related requirements.

b. Gather and document all 12 required attributes for each dataset that has been enlisted, ensuring full transparency and compliance with the regulations.

3. External Models (if used)

If you are using external foundational models/ fine-tuned models/ Gen AI API calls

a. Consult with the AI developer or service provider to acquire the Dataset Transparency list needed to create your compliance publication for the Generative AI Training Data Transparency Act.

b. Consult with your data governance team, Data Protection Officer (DPO), and legal team to ensure compliance with the California Consumer Privacy Act (CCPA) (Posted Aug ’24), as well as intellectual property and ownership-related requirements.

c. Gather and document all 12 required attributes for each dataset that has been enlisted, ensuring full transparency and compliance with the regulations.

4. Publish Report

Prepare and publish the Gen AI Training Dataset Transparency report on your website by January 1, 2026.

For Gen AI models or new versions to be published in the future

Enlist Datasets

AI & Data governance teams to enlist all the datasets used for

a. Model training

b. Fine tuning

c. RAG based inference

Compliance

For each dataset used in the training process:

a. Capture all 12 required attributes for the enlisted datasets.

b. Identify potential regulatory violations in a joint review with your DPO, Responsible AI Officer, Data Governance team, and legal team.

c. Build a strategy to meet regulatory requirements before starting the training.

Privacy Compliance

Ensure compliance with the California Consumer Privacy Act (CCPA) by:

a. Conduct Privacy Threat Modeling at the data collection level.

b. Apply Privacy Enhancing Technologies (PETs) at the data preprocessing level, including aggregated datasets and differentially private synthetic data with auditable mathematical proofs.

c. Post-privacy risk mitigation, review and approve datasets for model training, and record this in the Data Protection Impact Assessment (DPIA).

d. Implementing Privacy-Preserved Machine Learning to ensure compliance with privacy protection, time limitations, and purpose limitations.

e. Conducting an AI Impact Assessment before publishing the model, ensuring technical safeguards are in place.

Legal Compliance

Ensure compliance with other legal requirements, including:

a. Intellectual property rights of data providers.

b. Ownership-related issues when using data for AI model training.

How PrivaSapien Supports Your Responsible AI Journey?

PrivaSapien is a pioneer Privacy Enhancing and Responsible AI (PERAI) technologies. We offer a first-of-its-kind, end-to-end Responsible AI stack that can help you meet the requirements of the Generative AI Training Data Transparency Act and the California Consumer Privacy Act (Posted Aug ’24).

As of 3^rd October 2024, We have won multiple awards globally, including Accenture’s Global Tech Next Challenge in Digital Core for our Responsible AI tech stack, the Google for Start-ups Accelerator from over 1,000 companies, the Saudi Arabia Data & AI Authority’s PET Sandbox, and the Indian government’s award for privacy-preserved aggregate data sharing. We are also backed by U.S.-based venture capital firms.

With respect to the Generative AI Training Data Transparency Act, we help organizations build, fine-tune, and offer Gen AI services in a compliant way. While the Gen AI TDT Act itself does not levy penalties, the requirement to transparently publish data practices used for model training can result in severe privacy penalties from regulations such as CPRA, GDPR, PDPL, and DPDP for organizations that fail to follow privacy and Responsible AI principles.

PrivaSapien visionary and revolutionary technology stack is designed to meet Privacy, Responsible AI, and Transparency requirements with the following capabilities:

Data Collection Stage

Quantify risk with Privacy Threat Modeling, including automated risk scoring, technical mitigation recommendations, and meeting regulatory obligations.

Data Protection Impact Assessment (DPIA)

Conduct augmented and privacy aware DPIAs as per CCPA and GDPR for releasing data for downstream processing, such as model training, with approval by the DPO.

Data Privacy Preservation

Implement various advanced Privacy-Enhancing Technologies to meet business requirements for model training, both structured and unstructured, with auditable and repeatable mathematical proofs for verification and publication.

Privacy-Preserved Model Training

Enable organizations to maintain privacy during inference, including privacy preservation in RAGs (Retrieval-Augmented Generation).

AI Impact Assessment

Enable organizations to conduct an AI Impact Assessment in line with regulatory requirements like Responsible AI, including transparency requirements.

Privacy Compliant Inference

Have technical safeguards in place to ensure privacy-preserving inference in compliance with CCPA, GDPR, and other global privacy and Responsible AI requirements.

Transparency & Governance

Provide a governance report that lists the usage of datasets, models, and prompts on a periodic basis, ensuring it is auditable and publishable.

Conclusion:

Gen AI Training Data Transparency Act is a critical nudge towards Responsible AI development practices incorporating ethical usage of data bound by purpose limitation, time limitation, privacy preserved aggregate data usage, responsible synthetic data usage and transparent publication of the same. Organizations maintaining compliance and transparency are going to build a strategic advantage in attracting customers as downstream services also now have the obligation to make these transparent publication of their & upstream providers practices in developing Gen AI models and services. PrivaSapien’s visionary Privacy & Responsible AI stack can accelerate your compliance for GAI Training Data Transparency Act and provide you a competitive advantage in the data & AI era.

In this Article

Threats:

OWASP Recommendations:

Threats:

OWASP Recommendations:

Threats:

OWASP Recommendations:

Threats:

OWASP Recommendations:

In this Article

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

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

Decoding Privacy by Design: A Global Standard and Regulations Overview

Framework: From Design to Privacy Implementation

Privacy Risk Assessment with Privacy Threat modellingand PET based mitigatory recommendation

Process

Privacy Controls: Leveraging PETs for Data Protection

Privacy Controls: Leveraging PETs for Data Protection

References

In this Article

Navigating US – FTC regulations with PTM, PETs

FTC privacy & Security requirements

Act/Rule (Description)

Brief about Requirements

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

Process

People

Privacy Enhancing Technologies (PETs)

Getting Started: Data privacy with Privasapien PET Solutions

Requirement

Explanation

In this Article

The Gen AI Training Data Transparency Act

Who Needs to Comply?

By when to Comply?

What are the regulatory requirements?

What are details to be published?

Who is exempted?

How should you prepare for Compliance?

For Gen AI models already published

For Gen AI models or new versions to be published in the future

How PrivaSapien Supports Your Responsible AI Journey?

Conclusion:

Location

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

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