Comply with US Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence using Private AI

Nov 3, 2023
Share this post
Sharing to FacebookSharing to LinkedInSharing to XSharing to Email

The Biden-Harris Administration recently enacted a sweeping Executive Order to forge America’s path in responsible AI development, encouraging both innovation and risk mitigation. The Executive Order spells out a multi-faceted plan touching upon AI safety and security, privacy, equity, civil rights, and more, with profound implications for organizations that are already embedded in the AI landscape or are taking active steps into this burgeoning field. The order essentially provides a blueprint for AI’s future, requiring immediate action to conform to new standards and practices.Two risks highlighted in the Order stand out to us, as mitigating them falls squarely into Private AI’s area of expertise: the requirement to preserve privacy and to reduce bias and algorithmic discrimination. These risks take center-stage in the Order. In fact, threats to privacy and discrimination are pervasive to almost all areas where the US administration sees the use of AI posing risks to individuals, from access to housing and jobs to criminal prosecution. The reason for the pervasiveness is that all AI systems have this in common: they are trained on vast amounts of data, and if the data set contains personal and biased information, the output will reflect this. Concerning generative AI, the same holds true with regard to the input users provide to the LLM.This article addresses the new Executive Order and details how Private AI can lighten the task of these two important compliance aspects, freeing up resources to address the many other requirements the US administration has set out.

Safeguarding Privacy through Redaction of PII

The Executive Order highlights the need to protect Americans' privacy by accelerating the development and use of privacy-enhancing technologies (PETs). The importance of privacy protection is emphasized with reference to the chilling effect that invasion of privacy has on exercising First Amendment rights. The thought here is presumably that when individuals know their data could be improperly collected or misused, they may refrain from exercising their rights to free speech or assembly, for fear of surveillance or retribution. The Order further mentions fraud in connection with privacy risks. Without spelling this out further, it can be inferred that one risk the US administration is attuned to is that LLMs are known to reproduce personal information contained in training data in production. Malicious actors may tease out this information and use it for identity theft and other fraudulent purposes.Specializing in privacy-enhancing AI-driven redaction software, Private AI is an invaluable partner in ensuring compliance with this part of the Executive Order. Historical approaches to solving these privacy problems are no longer working in the age of Big Data. Private AI's leading edge technology moves beyond simple pattern matching, using machine learning to allow organizations to identify and redact Personally Identifiable Information (PII), Protected Health Information (PHI), and Payment Card Information (PCI) in structured and unstructured data forming the basis of AI systems.Context-aware machine learning models provide superior accuracy, allowing organizations to process data in a way that respects privacy: Removing personal information before it gets ingested by AI is the safest way to reduce risk as it ensures that no personal information gets revealed in production. It, of course, has the further benefit that compliance with applicable privacy laws is greatly facilitated since no personal data is disclosed in the process.

Advancing Equity by Mitigating Bias

The Executive Order also demands action to ensure that AI advances equity and civil rights, keeping algorithms from exacerbating discrimination. While an evaluation of a mortgage, job, or rental application can of course also be met with bias when a human is in charge, an AI system taking over can assess infinitely more applications in hardly any time, thereby significantly broadening the impact of existing bias incorporated in its training data.Private AI's capabilities offer a solution. By redacting identifiers that reveal gender, ethnic origin, sexual orientation, etc., the data set is rendered largely “neutral.” Redaction thus doesn't just safeguard privacy; it can be a tool for social justice by reducing bias in data-driven systems. Private AI’s technology does not only work to sanitize training data. It can also filter out personal identifiers from user input before it is transferred to the LLM, ensuring that any sensitive data, including those often linked to biases, are not disclosed, and can’t form the basis for a biased output generated by the model.

Towards a More Trustworthy AI Ecosystem

Beyond mere compliance, Private AI contributes to the larger goal of creating an AI ecosystem that is safe, secure, and trustworthy. By providing a privacy layer that can work in sync with AI systems like ChatGPT, Private AI enables the seamless yet secure interaction that is indispensable for building public trust. Its capability of replacing personal data with synthetic data also ensures data usability for many purposes because the semantic integrity of the data is retained.

Conclusion

The Executive Order by the Biden-Harris Administration is a clarion call for responsible AI innovation, one that blends safety, privacy, and equity. Private AI serves as an excellent partner for organizations aiming to navigate this complex new landscape. We agree with Biden that the future of AI isn’t just about who builds the most advanced algorithms; it's about doing so responsibly. With tools like Private AI, we don't have to choose between innovation and respect for individual rights and the values of our society. We can, in fact, have our (secure, private, and equitable) cake and eat it too.

Data Left Behind: AI Scribes’ Promises in Healthcare

Data Left Behind: Healthcare’s Untapped Goldmine

The Future of Health Data: How New Tech is Changing the Game

Why is linguistics essential when dealing with healthcare data?

Why Health Data Strategies Fail Before They Start

Private AI to Redefine Enterprise Data Privacy and Compliance with NVIDIA

EDPB’s Pseudonymization Guideline and the Challenge of Unstructured Data

HHS’ proposed HIPAA Amendment to Strengthen Cybersecurity in Healthcare and how Private AI can Support Compliance

Japan's Health Data Anonymization Act: Enabling Large-Scale Health Research

What the International AI Safety Report 2025 has to say about Privacy Risks from General Purpose AI

Private AI 4.0: Your Data’s Potential, Protected and Unlocked

How Private AI Facilitates GDPR Compliance for AI Models: Insights from the EDPB's Latest Opinion

Navigating the New Frontier of Data Privacy: Protecting Confidential Company Information in the Age of AI

Belgium’s Data Protection Authority on the Interplay of the EU AI Act and the GDPR

Enhancing Compliance with US Privacy Regulations for the Insurance Industry Using Private AI

Navigating Compliance with Quebec’s Act Respecting Health and Social Services Information Through Private AI’s De-identification Technology

Unlocking New Levels of Accuracy in Privacy-Preserving AI with Co-Reference Resolution

Strengthened Data Protection Enforcement on the Horizon in Japan

How Private AI Can Help to Comply with Thailand's PDPA

How Private AI Can Help Financial Institutions Comply with OSFI Guidelines

The American Privacy Rights Act – The Next Generation of Privacy Laws

How Private AI Can Help with Compliance under China’s Personal Information Protection Law (PIPL)

PII Redaction for Reviews Data: Ensuring Privacy Compliance when Using Review APIs

Independent Review Certifies Private AI’s PII Identification Model as Secure and Reliable

To Use or Not to Use AI: A Delicate Balance Between Productivity and Privacy

To Use or Not to Use AI: A Delicate Balance Between Productivity and Privacy

News from NIST: Dioptra, AI Risk Management Framework (AI RMF) Generative AI Profile, and How PII Identification and Redaction can Support Suggested Best Practices

Handling Personal Information by Financial Institutions in Japan – The Strict Requirements of the FSA Guidelines

日本における金融機関の個人情報の取り扱い - 金融庁ガイドラインの要件

Leveraging Private AI to Meet the EDPB’s AI Audit Checklist for GDPR-Compliant AI Systems

Who is Responsible for Protecting PII?

How Private AI can help the Public Sector to Comply with the Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024

A Comparison of the Approaches to Generative AI in Japan and China

Updated OECD AI Principles to keep up with novel and increased risks from general purpose and generative AI

Is Consent Required for Processing Personal Data via LLMs?

The evolving landscape of data privacy legislation in healthcare in Germany

The CIO’s and CISO’s Guide for Proactive Reporting and DLP with Private AI and Elastic

The Evolving Landscape of Health Data Protection Laws in the United States

Comparing Privacy and Safety Concerns Around Llama 2, GPT4, and Gemini

How to Safely Redact PII from Segment Events using Destination Insert Functions and Private AI API

WHO’s AI Ethics and Governance Guidance for Large Multi-Modal Models operating in the Health Sector – Data Protection Considerations

How to Protect Confidential Corporate Information in the ChatGPT Era

Unlocking the Power of Retrieval Augmented Generation with Added Privacy: A Comprehensive Guide

Leveraging ChatGPT and other AI Tools for Legal Services

Leveraging ChatGPT and other AI tools for HR

Leveraging ChatGPT in the Banking Industry

Law 25 and Data Transfers Outside of Quebec

The Colorado and Connecticut Data Privacy Acts

Unlocking Compliance with the Japanese Data Privacy Act (APPI) using Private AI

Tokenization and Its Benefits for Data Protection

Private AI Launches Cloud API to Streamline Data Privacy

Processing of Special Categories of Data in Germany

End-to-end Privacy Management

Privacy Breach Reporting Requirements under Law25

Migrating Your Privacy Workflows from Amazon Comprehend to Private AI

A Comparison of the Approaches to Generative AI in the US and EU

Benefits of AI in Healthcare and Data Sources (Part 1)

Privacy Attacks against Data and AI Models (Part 3)

Risks of Noncompliance and Challenges around Privacy-Preserving Techniques (Part 2)

Enhancing Data Lake Security: A Guide to PII Scanning in S3 buckets

The Costs of a Data Breach in the Healthcare Sector and its Privacy Compliance Implications

Navigating GDPR Compliance in the Life Cycle of LLM-Based Solutions

What’s New in Version 3.8

How to Protect Your Business from Data Leaks: Lessons from Toyota and the Department of Home Affairs

New York's Acceptable Use of AI Policy: A Focus on Privacy Obligations

Safeguarding Personal Data in Sentiment Analysis: A Guide to PII Anonymization

Changes to South Korea’s Personal Information Protection Act to Take Effect on March 15, 2024

Australia’s Plan to Regulate High-Risk AI

How Private AI can help comply with the EU AI Act

Comment la Loi 25 Impacte l'Utilisation de ChatGPT et de l'IA en Général

Endgültiger Entwurf des Gesetzes über Künstliche Intelligenz – Datenschutzpflichten der KI-Modelle mit Allgemeinem Verwendungszweck

How Law25 Impacts the Use of ChatGPT and AI in General

Is Salesforce Law25 Compliant?

Creating De-Identified Embeddings

Exciting Updates in 3.7

EU AI Act Final Draft – Obligations of General-Purpose AI Systems relating to Data Privacy

FTC Privacy Enforcement Actions Against AI Companies

The CCPA, CPRA, and California's Evolving Data Protection Landscape

HIPAA Compliance – Expert Determination Aided by Private AI

Private AI Software As a Service Agreement

EU's Review of Canada's Data Protection Adequacy: Implications for Ongoing Privacy Reform

Acceptable Use Policy

ISO/IEC 42001: A New Standard for Ethical and Responsible AI Management

Reviewing OpenAI's 31st Jan 2024 Privacy and Business Terms Updates

Comparing OpenAI vs. Azure OpenAI Services

Quebec’s Draft Regulation Respecting the Anonymization of Personal Information

Version 3.6 Release: Enhanced Streaming, Auto Model Selection, and More in Our Data Privacy Platform

Brazil's LGPD: Anonymization, Pseudonymization, and Access Requests

LGPD do Brasil: Anonimização, Pseudonimização e Solicitações de Acesso à Informação

Canada’s Principles for Responsible, Trustworthy and Privacy-Protective Generative AI Technologies and How to Comply Using Private AI

Private AI Named One of The Most Innovative RegTech Companies by RegTech100

Data Integrity, Data Security, and the New NIST Cybersecurity Framework

Safeguarding Privacy with Commercial LLMs

Cybersecurity in the Public Sector: Protecting Vital Services

Privacy Impact Assessment (PIA) Requirements under Law25

Elevate Your Experience with Version 3.5

Fine-Tuning LLMs with a Focus on Privacy

GDPR in Germany: Challenges of German Data Privacy (Part 2)

Comply with US Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence using Private AI

How to Comply with EU AI Act using PrivateGPT