Hi there readers,

As businesses across sectors embrace the power of AI, responsible AI has become more than just a best practice—it’s a critical foundation for sustainable growth and innovation. At Opaque, we’re seeing how leading organizations recognize that responsible AI, built on privacy, fairness, transparency, and accountability, is a necessity in today’s digital landscape.

In this issue, we explore the essential role of responsible AI in enabling organizations to maximize AI’s potential while ensuring ethical integrity and regulatory compliance. From safeguarding data privacy and sovereignty to reducing bias and enhancing transparency, responsible AI frameworks empower businesses to drive value while building trust.

Our featured article below examines how companies in high-stakes industries like financial services, healthcare, and tech are navigating these challenges and leveraging Opaque’s solution to support responsible AI at scale. By embedding responsible AI practices into their workflows, they’re not only protecting critical data but also accelerating innovation with confidence.

I hope you find these insights valuable, and I look forward to your thoughts on how responsible AI can shape the future of business.

— Rishabh Poddar, Co-Founder & CTO, Opaque Systems

The Essential Role of Responsible AI in Modern Business

As AI drives forward innovation, reshaping industries and creating unprecedented opportunities, responsible AI is now recognized as a foundational principle for modern businesses. More than just a checkbox, responsible AI offers a way to unlock AI’s full potential with confidence, ensuring it is ethical, transparent, and aligned with the values of both businesses and society.

Many organizations are already aware of the importance of responsible AI. Yet, they may miss key aspects like data bias, privacy, transparency, and accountability. Consider the implications: AI models trained on limited datasets can inadvertently reinforce biases, leading to skewed or unfair outcomes. Research by USC, for instance, shows bias in up to 38.6% of AI outputs, while an Applause survey found that nearly half of AI users have encountered responses they consider biased.

Likewise, systems that operate as “black boxes” can lack transparency, making it difficult for businesses to understand how decisions are made and what data drives those decisions. These factors illustrate why responsible AI, an approach that balances innovation with transparent and ethical AI workflows, is critical to business resilience and growth.

A comprehensive responsible AI framework—grounded in privacy, fairness, transparency, and accountability—empowers businesses to scale AI responsibly, supporting not just regulatory compliance but also sustainable growth. It is the foundation upon which companies can accelerate AI adoption with confidence, transforming responsible AI into a true competitive advantage.

Data Privacy and Sovereignty: A Foundation for Growth

At the heart of responsible AI is a commitment to data privacy and sovereignty. Today’s privacy-preserving technologies allow AI and machine learning (ML) workloads to flourish without compromising sensitive data. For example, institutions in heavily regulated industries like financial services can enhance fraud detection while keeping customer data encrypted at every stage.

Multi-party collaboration, enabled by confidential computing, plays a crucial role in this privacy-first landscape. With platforms like Opaque that utilize confidential computing, multiple organizations can securely collaborate, sharing datasets for AI model training or improvement without ever relinquishing control over their data. This ensures that data remains encrypted not only at rest and in transit but also during computation, empowering organizations to pool data resources and identify patterns—like fraud across institutions—without sacrificing data privacy. By leveraging collaborative AI, Opaque enables new, innovative use cases that were previously impossible.

By maintaining control over how and where data is processed, data sovereignty allows businesses to scale AI initiatives and collaborate with third parties with confidence. In this way, data sovereignty not only safeguards compliance with global regulations but also maximizes the value of data-driven AI efforts.

Fairness: Mitigating Bias in AI Systems

Fairness is central to building AI systems that deliver equitable outcomes. Responsible AI frameworks enable businesses to design systems that benefit all demographics, drawing on diverse and representative datasets. However, achieving true fairness often requires securely combining datasets that may be spread across regions or protected by stringent privacy laws.

Opaque’s confidential AI platform allows organizations to aggregate and analyze diverse datasets while maintaining strict privacy controls. This approach mitigates bias, helping businesses create AI models that are more representative of diverse populations.

Imagine a company operating in multiple regions, including underserved areas. Privacy laws may limit data-sharing across regions, while data sovereignty concerns may prevent them from collaborating with partners who possess valuable datasets. Without a secure, privacy-preserving approach, AI models may risk lacking representation and fairness.

Opaque’s platform enables these organizations to securely combine data from different regions, creating AI models that reduce bias without compromising data privacy. This capability opens doors to mutually beneficial outcomes, enabling businesses to develop fairer, more inclusive AI systems.

Transparency: Building Trust in AI Workflows

Transparency is vital for building trust and ensuring that AI systems are both compliant and accurate. It enables organizations to trace and audit AI workflows, providing clarity to users, regulators, and stakeholders while reducing regulatory risk.

Opaque’s solution enhances transparency with cryptographically verifiable audit trails, creating tamper-proof logs that track each step in the AI pipeline, from data access to model operations. For example, a pharmaceutical company can use Opaque’s platform to securely process patient data while producing cryptographically verified audit logs. This ensures compliance with privacy laws, builds trust among patients, and strengthens the company’s standing in a highly regulated field.

Accountability goes hand-in-hand with transparency. Beyond compliance, a transparent and accountable AI system underscores a company’s commitment to upholding high standards. Imagine a healthcare provider using AI to analyze patient data for better treatment outcomes. With a responsible AI platform like Opaque’s, they can establish strict access controls, enforce governance policies, and audit every access request, reinforcing patients’ confidence in the responsible use of their data.

The Business Case for Responsible AI

Responsible AI is more than compliance; it’s a business imperative that strengthens trust, reduces risk, and unlocks growth through innovation. By embedding principles of privacy, fairness, transparency, and accountability into AI deployment, businesses build a resilient foundation for AI at scale.

Opaque’s platform enables organizations to adopt responsible AI practices, safeguarding data privacy, and fostering secure multi-party collaboration. This empowers businesses to deploy AI confidently, with reduced bottlenecks, transparent workflows, and minimized data silos.

In today’s landscape, responsible AI is essential for any business aiming to scale AI successfully. Companies that adopt it are better positioned to drive growth, harness AI’s potential, and stay competitive in a rapidly evolving market.

In the Lab

The Latest Happenings at Opaque Systems

Keynote Presentation: Opaque at GenAI Summit 2024
GenAI Summit is a premier event dedicated to GenAI adoption in enterprise, where leaders in data, technology, and innovation converge. This Saturday, Co-founder and President of Opaque, Raluca Ada Popa, will deliver a Keynote presentation at GenAI Summit Silicon Valley 2024, titled: “Protecting Confidential Data in the Generative AI Pipeline”.

Opaque Selected for Microsoft for Startups Pegasus Program
Opaque has been selected for the exclusive Microsoft for Startups Pegasus Program. The invite-only program is designed to support high-potential startups, providing the industry-leading services, guidance, and technology needed to build a successful, resilient company. By joining this program, Opaque is poised to expand its market reach and strengthen its leadership in Confidential AI, empowering its growing customer base to securely adopt AI and drive efficiency, innovation, and competitive advantage.

Feature: Building Ecosystem-wide Success for FoodTech Founders
FoodTech Stories features the leaders pioneering new approaches to technology, collaboration, and storytelling in the FoodTech and AgTech space. In this episode, Scott May, Founder and Head of MISTA, talks with host Megan Thomas about the barriers and breakthroughs that innovators face in both startup and corporate companies. The essential role of data sovereignty in transforming and decentralizing the food industry is discussed. In this context, Scott May calls out Opaque’s Confidential AI system as a breakthrough technology that upholds data protection, while simultaneously unlocking actionable insights through collaborative data sharing.

Executive Roundtable Dinner Hosted by Opaque 
Opaque Systems hosted an executive dinner with AI and Security researchers from UC Berkeley Skylab (formerly RISELAB), along with data and AI leaders from Geico, Best Buy Health, AMD, NVIDIA, J&J, and many others. The number one discussion topic at the dinner was data control. The key takeaway: whoever controls the data, controls the industry.

Demonstration: Security Overview of the Opaque Confidential AI Platform
Jamie Aliperti, Director of Solution Engineering at Opaque Systems, provides an in-depth overview of the security aspects of the Opaque Confidential AI Platform, highlighting how the platform empowers organizations to collaborate on sensitive data without compromising confidentiality or data sovereignty. By leveraging advanced encryption and privacy-preserving technologies, the platform ensures that data remains protected throughout processing.

McKinsey's North America Real World AI Summit: Opaque Managing Risks in AI Panel 
At McKinsey's North America Real World AI Summit, Miran Chun—Vice President and Head of Marketing at Opaque—joined panelists for an open discussion on Trust and Safety: Managing Risks in AI. The invitation-only event—held this year at Utah’s Sundance Mountain Resort—is a gathering of senior leaders and tech industry experts, delivering real value through exploration of how AI is transforming the world.

Code for Thought

Worthwhile Reads

🧠 A new mindset in application development. The RAG paradigm has popularized the notion of supplementing prompts for GenAI models with trusted enterprise data. But there are alternative forms of prompt augmentation that can achieve these objectives by integrating complex workflows to inform machine-based decisions while initiating actions within next-generation AI applications. In certain implementations, these agents can scour any number of enterprise data sources to gather information that’s relevant to a task. In this article, CEO of Opaque Systems, Aaron Fulkerson, shares insights on how organizations can employ different agents for different sources.  

✋ Most companies still aren’t data-ready for GenAI. A new report by Accenture found that six out of ten “reinvention-ready” companies have moved to the highest level of operations maturity, yet nearly two thirds still struggle to change the way they operate. For example, they lag behind on building a robust data foundation: 61% report that their data assets are not yet ready for GenAI and 70% find it hard to scale projects that use proprietary data. By enabling companies to work securely with sensitive data while maintaining control over their proprietary information, Opaque’s platform explores new opportunities with GenAI and addresses these challenges.

📊 The demand for high-quality data has never been more critical. According to Appen’s State of AI report, while 2024 is expected to close out with a 17% increase in GenAI use, data quality is projected to decline. This contrast highlights a growing issue in the AI landscape: as businesses push to innovate, the quality of the data they rely on may be deteriorating. Achieving AI success requires more than just large datasets—it requires data that is diverse, accurate, and trustworthy. With the help of confidential AI, businesses can overcome barriers and drive projects into production—unlocking richer, higher-quality insights. 

💼 Privacy and security threats to AI models are giving rise to a new crop of startups. As more companies adopt using AI, it has brought to light issues regarding security and privacy. Now, a growing group of startups are popping up to tackle security threats related to AI. Opaque, for instance, enables companies to share private data through a confidential computing platform to accelerate AI adoption. But the issue doesn’t stop there. The greater problem sits with data sovereignty—for most industries, relevance will require retaining control, if not outright ownership, of data. At Opaque, we believe that confidential AI can play a pivotal role in restoring data sovereignty to all.

Stay Tuned: AI Confidential Podcast
We're thrilled to announce the upcoming launch of the AI Confidential Podcast, where industry leaders share their insights on the transformative power of AI. This week, we recorded our first episode with Teresa Tung, Senior Managing Director and Global Data Capability Lead at Accenture, the global leader in driving AI adoption. To tune into our podcast, subscribe to the AI Confidential Newsletter, and you'll be notified when episode one drops.