In this illuminating episode of AI Confidential, I had the pleasure of hosting Will Grannis, CTO and VP at Google Cloud, for a deep dive into what it really takes to make AI work in complex enterprise environments. Watch the full episode on YouTube →
Beyond the AI Hype
One of Will’s most powerful insights resonated throughout our conversation: “AI isn’t a product—it’s a variety of methods and capabilities to supercharge apps, services and experiences.” This mindset shift is crucial because, as Will emphasizes, “AI needs scaffolding to yield value, a definitive use case/customer scenario to design well, and a clear, meaningful objective to evaluate performance.”
Real-World Impact
Our discussion brought this philosophy to life through compelling examples like Wendy’s implementation of AI in their ordering systems. What made this case particularly fascinating wasn’t just the technology, but how it was grounded in enterprise truth and proprietary knowledge. Will explained how combining Google AI capabilities with enterprise-specific data creates AI systems that deliver real value.
The Platform Engineering Imperative
A crucial theme emerged around what Will calls “platform engineering for AI.” As he puts it, this “will ultimately make the difference between being able to deploy confidently or being stranded in proofs of concept.” The focus here is comprehensive: security, reliability, efficiency, and building trust in the technology, people, and processes that accelerate adoption and returns.
Building Trust Through Control
We explored how Google Cloud’s Vertex AI platform addresses one of the biggest challenges in enterprise AI adoption: trust. The platform offers customizable controls that allow organizations to:
Filter and customize AI outputs for specific needs
Maintain data security and sovereignty
Ensure regulatory compliance
Enable rapid experimentation in safe environments
The Path to Production
What struck me most was Will’s pragmatic approach to AI implementation. Success isn’t just about having cutting-edge technology—it’s about:
Creating secure runtime operations
Implementing proper data segregation
Enabling rapid experimentation
Maintaining constant optimization
Building trust through transparency and control
Looking Ahead
The future of AI in enterprise settings isn’t about replacing existing systems wholesale—it’s about strategic enhancement and thoughtful integration. As Will shared, the most successful implementations come from organizations that approach AI as a capability to be carefully woven into their existing operations, not as a magic solution to be dropped in.
Listen to this episode on Spotify or visit our podcast page for more platforms. For weekly insights on secure and responsible AI implementation, subscribe to our newsletter.
Join me for the next episode of AI Confidential where we’ll continue exploring the frontiers of secure and responsible AI implementation. Subscribe to stay updated on future episodes and insights.
As organizations build out their AI infrastructure, how are you ensuring the security and privacy of your sensitive data throughout the AI pipeline? Share your approach in the comments below.
In this eye-opening episode of AI Confidential, I had the privilege of hosting two pioneers in AI security and privacy: Daniel Rohrer, VP of Software Security at NVIDIA, and Raluca Ada Popa, Professor at UC Berkeley, Co-Director of UC Berkeley Skylab, and Co-Founder and President of Opaque Systems. Together, we explored the cutting edge of privacy-preserving AI technology and its implications for the future of innovation. Watch the full episode on YouTube →
The Hardware Revolution
One of the most exciting developments we discussed was NVIDIA’s recent introduction of GPU Hardware Enclaves with the H100. As Daniel explained, this breakthrough, which became available through cloud providers like Azure in September 2023, fundamentally transforms what’s possible with secure AI computing. For the first time, organizations can achieve true end-to-end security for computationally intensive AI workloads at scale.
The Power of Attestation
Raluca brought a unique academic and entrepreneurial perspective to our discussion of how confidential computing transforms trust in AI systems. The key insight? It’s not just about encryption—it’s about proving exactly what happens to data throughout the AI pipeline. Through confidential computing, organizations can now:
Cryptographically verify code execution
Track model access to data
Document complete data lineage
Ensure compliance through technical guarantees
Beyond Traditional Security
Our conversation revealed how these capabilities enable entirely new forms of collaboration and innovation. Organizations can now:
Process sensitive data while maintaining encryption
Enable secure multi-party computation with verifiable guardrails
Protect both data and model weights in AI workflows
Maintain documented compliance while driving innovation
Real-World Impact
The applications we explored were compelling: from healthcare institutions collaborating on better treatment protocols to financial institutions jointly fighting fraud. What makes these use cases possible isn’t just the encryption—it’s the ability to prove exactly how data is being used.
The Path Forward
As both Daniel and Raluca emphasized, attestable AI pipelines aren’t just a security feature—they’re becoming a business necessity. In today’s AI-driven world, losing control of your data isn’t just a temporary setback—it can have irreversible consequences for competitiveness and security.
The future belongs to organizations that can not only protect their data but prove how it’s being used. Confidential computing makes this possible, turning data privacy from a constraint into a catalyst for innovation.
Join me for the next episode of AI Confidential where we’ll continue exploring the frontiers of secure and responsible AI implementation. Subscribe to stay updated on future episodes and insights.
As we move into this new era of secure AI, how is your organization balancing innovation with data privacy? Share your approach in the comments below.
When I sat down with Teresa Tung from Accenture for Episode 2 of AI Confidential (you can find this episode on Youtube in addition to Spotify), I was struck by a stark reality that many enterprise leaders are facing: while 75% of CXOs recognize the critical need for high-quality data to power their generative AI initiatives, nearly half lack the trusted data required for operational deployment.
This gap isn’t just a statistic—it’s a story I’ve seen play out repeatedly across boardrooms and technical teams. As companies rush to embrace generative AI, they’re discovering that the real challenge isn’t implementing the technology—it’s protecting and leveraging their most valuable asset: data.
Teresa shared a fascinating perspective from her work at Accenture that resonated deeply with me. She pointed out that in the next five years, industry leadership will be determined not by who has the most advanced AI models, but by who can effectively control and utilize their data. It’s a shift that reminds me of the early days of digital transformation, where companies that failed to adapt quickly found themselves in a Kodak-like situation.
The Security Paradox
Here’s the challenge that keeps enterprise architects, CTOs, and CIOs up at night: the most valuable data for AI applications is often the most sensitive. Whether it’s financial records, customer interactions, or proprietary research, this “crown jewel” data holds transformative potential but comes with enormous risk.
During our conversation, Teresa shared an illuminating example from an automotive manufacturer grappling with this exact dilemma. The company saw tremendous potential in using AI to enhance customer interactions but faced the fundamental challenge of keeping sensitive data secure while making it actionable.
Beyond Pilot Purgatory
What’s become clear through my conversations with technology leaders is that many organizations are stuck in what I call “pilot purgatory”—they can experiment with AI on non-sensitive data, but can’t scale to production because they lack frameworks for securing sensitive data at scale.
This is where technologies like Confidential Computing enter the picture. As Teresa and I discussed, it’s not just about encrypting data at rest or in transit anymore—it’s about maintaining security while data is being processed. This capability is transforming how companies can approach AI implementation, enabling them to:
Process sensitive data while maintaining encryption
Share insights without exposing raw data
Create new business models through secure multi-party computation
The Path Forward
For technology leaders navigating this landscape, the message is clear: the winners in the AI race might be determined partly by who moves fastest, but whoever builds the most trustworthy and secure foundations will endure and stand the test of time. As Teresa pointed out, successful AI implementation requires treating data as a product—with all the quality controls, supply chain considerations, and security measures that implies.
Looking ahead, I believe we’re entering a new era of AI adoption where security and scalability must be considered from day one. The companies that thrive will be those that can balance innovation with protection, speed with security, and ambition with responsibility.
Listen to this episode on Spotify or visit our podcast page for more platforms. For weekly insights on secure and responsible AI implementation, subscribe to our newsletter.
Join me for the next episode of AI Confidential where we’ll continue exploring the frontiers of secure and responsible AI implementation. Subscribe to stay updated on future episodes and insights.
What challenges are you facing in scaling AI while maintaining data security? I’d love to hear your thoughts in the comments below.
In a recent discussion between technology leaders Mark Papermaster (CTO and Deputy CISO of Microsoft Azure) and Mark Russinovich (CTO of AMD), the focus was on the transformative potential of confidential Computing in reshaping data security practices within the technology industry. Against a backdrop of escalating concerns surrounding data privacy and cybersecurity threats, the conversation delved into key themes such as Security and Trust, Confidential Computing, Data Control, and Collaboration. These themes underscored the critical importance of safeguarding customer data in cloud environments through innovative solutions like secure enclaves and hardware root of trust mechanisms. Confidential Computing, defined as a technology that ensures data remains secure even during processing by unauthorized parties, emerged as a pivotal tool in enhancing data security measures amidst rapid advancements in AI technologies.
The dialogue also highlighted recent developments such as the collaboration between AMD and Microsoft to streamline confidential computing adoption and Microsoft’s ambitious goal to transition to a confidential cloud by 2025. The introduction of Azure Confidential Ledger further exemplified industry efforts towards bolstering supply chain security. Looking ahead, the future outlook points towards continued advancements in confidential Computing technologies with an emphasis on expanding their application to edge devices while establishing robust integrity measures across computing supply chains. As companies strive to navigate ethical considerations around data control and privacy in AI applications alongside potential regulatory challenges associated with widespread adoption of secure computing practices, it becomes increasingly clear that fostering trust through enhanced security measures will be paramount for shaping the future landscape of technology innovation.
This article was written for the Jack Kent Cooke Foundation and originally published on the Cooke Alumni Blog.
2002 Undergraduate Transfer Scholar, University of North Carolina at Chapel Hill
My career path has been a winding road filled with learning, growth, and unexpected turns. I’m proud to say that I am a first-generation college graduate and part of the inaugural cohort of the Cooke Transfer Scholars and in 2004 I received my BS in Applied Math Computer Science from UNC-Chapel Hill. My path started at Microsoft, where I worked briefly in Research, which led to co-founding a software company (MindTouch), eventually joining a 22,000-person company (ServiceNow), and currently leading another startup (Opaque Systems). Along the way, I’ve gathered valuable lessons that I hope will inspire and guide you in your own career.
The Beginning: Microsoft
My career began at Microsoft, which was a remarkable experience. I worked with brilliant people on Research that informed some of the foundational Cloud technologies used today. We had access to resources that fueled my passion for technology. This period taught me the importance of kindness in the workplace. Kindness isn’t just about being nice; it’s about genuinely caring for others and their success. By being kind, you build relationships that can last a lifetime, and these relationships are invaluable in your career. While I worked at Microsoft for less than a year, I still have many friendships from that time that are meaningful to me twenty years later.
Lesson One: Operate with Kindness
Kindness has always been a core value for me. It makes life more fulfilling and opens doors professionally. Twenty years of professional experience have taught me that relationships and helping others bring me the most fulfillment and joy. Also, as a side effect, being kind enables you to develop more relationships and creates a supportive network throughout your career.
Entrepreneurship: Founding MindTouch
I’ve always been comfortable taking risks and betting on myself. I developed this skill early because my circumstances afforded me few opportunities. I learned to “do it myself” and figure it out through trial and error, embodying the attitude common in punk culture that emerges when you’ve got nothing to lose. In 2005, I co-founded MindTouch with a friend from Microsoft. What started as an open-source project quickly gained traction, becoming one of the world’s most popular open-source software projects within two years; today, this software still serves hundreds of millions of users (an example can be found atLibreTexts). Leading MindTouch was incredibly fulfilling. We built a product that empowered people to share knowledge and collaborate more effectively. Not only was MindTouch helping people who used the software, it also launched and advanced the careers of hundreds of MindTouchers who worked for the company.
Lesson Two: Be Proactive and Drive
Most people talk about their dreams and goals, but very few start them and even fewer stay committed to putting in the hard work to achieve their goals. It’s essential to be proactive. Don’t make excuses—take action. Be punk, be bold, and learn by trial and error. I’ve taken inspiration from the concept of Zanshin, which I learned about in the book “Zen in the Art of Archery.” It’s about being fully aware and committed to your actions. Be present in all aspects of your work, always strive to improve and develop your craft.
Transformative Leadership: Growing ServiceNow
After leaving MindTouch, which we sold to a larger company, I joined ServiceNow. At MindTouch, our business was in the tens of millions, and then I joined ServiceNow, where I was helping to run businesses that contributed billions to the company. After helping to triple the revenue of one business, I then led the creation of a new business and product line. This product became the fastest-growing in the company’s history. However, the journey was challenging. Some people resisted the new business unit because it disrupted existing structures and change can be uncomfortable but it’s the only way to grow. Despite the resistance, we stayed focused on our vision and values, which ultimately led to our success.
Lesson Three: Be Enthusiastically Interested in Others
Understanding what others care about and what motivates them is essential. This attitude, born from a genuine interest in helping others, has helped me build authentic relationships with colleagues and customers. It’s not just about business; it’s about creating meaningful connections. Caring to understand the customer and internal stakeholders allowed me to navigate the political landscape authentically and develop trust.
Lesson Four: Be Determined
Achieving your objectives requires not letting setbacks or failures discourage you. My time at ServiceNow was filled with numerous challenges and resistance, but staying determined helped us push through. We remained focused on our customers, vision, and values, ultimately creating products that significantly improved the industry, accelerated the careers of hundreds, probably thousands, and added billions to ServiceNow market cap.
Leading the Future of AI: Joining Opaque Systems
Now, as CEO of Opaque Systems, I am at the forefront of AI innovation. Opaque Systems is dedicated to accelerating AI adoption by ensuring data privacy, security, and sovereignty. Several world-renowned researchers launched the company from a famous computer science lab at UC Berkeley (RISElab). My role as CEO combines my passion for technology with my commitment to ethical technology adoption. Here we are tackling the trust crisis in AI with confidential data, ensuring AI can advance without compromising privacy.
As CEO of an early-stage software company, I am mostly focused on our “product-market-fit.” This means I’m spending most of my time speaking with current customers, prospective customers, and partners to determine the optimal go-to-market and product strategies. Go-to-market is a fancy way of saying that we’re figuring out the best customers to focus on and how to communicate the benefits of the technology to as narrow an audience as possible. By remaining focused we will grow the business more quickly than be being diffused across many potential buyers and use cases. The go-to-market strategy informs the product strategy, which is another fancy way of saying features we should focus on and how to design the user experience. On any given day, I spend most of my time in Zoom meetings with Chief Information Officers, Chief Technology Officers, Chief Data Officers, and Chief Information Security Officers of enterprise-scale companies in the Financial Services, High Tech, Government, and Manufacturing industries. When I’m not doing this, I’m helping to recruit new talent to the Opaque team.
Lesson Five: Have a Bold Vision and Be Disciplined
Having a bold vision is essential, but you must also follow the order of operations to achieve it. Just like in arithmetic, where you solve problems in a specific sequence (parentheses first, then exponents, followed by multiplication and division, and finally addition and subtraction) achieving your goals requires a similar approach. Furthermore, being successful at anything requires practice and repetition. Focus on the first steps and consistently do the tedious work daily. Discipline and a willingness to focus on the work that matters, even if it’s not the most fun, are crucial.
Reflecting on my Journey
Looking back, each step in my career has taught me something valuable and have shaped who I am today. I encourage you to embrace kindness, be proactive, take an enthusiastic interest in others, stay determined, and have a bold vision while staying disciplined to do the tedious work.
I hope my story offers guidance and inspiration to other scholars, college students, and young professionals reading this. Remember that success isn’t just about reaching your goals; it’s about how you get there and the relationships you build along the way. Be kind, proactive, and curious about others; always maintain sight of your vision. Your journey is unique, and I’m excited to see where it takes you. Please contact me on LinkedIn directly with your stories, especially if this was helpful, and let me know if I can be helpful to you in your journey.
Aaron Fulkerson 