Building Enterprise AI That Earns Trust

Hardial Singh has spent over 15 years designing data and AI systems for organizations where failure carries real consequences. His work in healthcare, finance, insurance, and telecommunications has focused on building architectures that perform at petabyte scale while meeting strict regulatory requirements. At organizations including Kaiser Permanente and Cleveland Clinic, he led initiatives that balanced analytical ambition with the privacy and compliance demands unique to healthcare data. His technical expertise spans hybrid cloud modernization, zero-trust security, MLOps, and generative AI governance. Singh holds a Master of Science from California State University, Northridge, and a background in mechanical engineering that continues to shape how he approaches system design. In this conversation, he discusses what regulated industries teach you about architecture, why governance determines whether AI initiatives succeed or stall, and the capabilities enterprises will need as AI becomes embedded deeper in business operations. 

You’ve spent over 15 years architecting data and AI systems for organizations in healthcare, finance, and insurance. What drew you to working in these highly regulated industries, and how has that shaped your technical approach? 

I was drawn to regulated industries because they make you design with real accountability. When you work with healthcare outcomes, insurance claims, or financial transactions, you quickly realize you’re not just building software. You’re building systems that influence people’s lives, their financial stability, and their trust in an organization’s integrity. That responsibility stays with you, and it fundamentally changes how you approach architecture. 

Instead of asking, “How do we build this quickly?” you start asking, “How do we build this right, in a way that’s traceable and defensible?” Predictability, transparency, and integrity become non-negotiable. Encryption, MFA, governance, lineage, those stop being optional controls and become the structural supports of the system. You know someone will eventually scrutinize your decisions, even if they never see your name attached to them. 

That environment shaped how I think about designing systems even today. Whether I’m working with modern AI stacks or cloud-native platforms, I carry that regulated industry mindset. I design with the assumption that trust has to be earned, maintained, and continuously proven. 

Your work spans hybrid cloud modernization and AI automation frameworks. For organizations still running legacy systems, what does a realistic path to modernization look like, and where do most companies stumble? 

Modernization starts long before the first workload moves or the first line of code is rewritten. The initial step is understanding the landscape you’re entering. Many enterprises operate systems built over a decade or more, scripts that no one fully remembers, shadow processes that evolved out of necessity, and databases stitched together to keep things running. 

What I’ve seen repeatedly is that modernization succeeds when organizations stop thinking of it purely as “moving to the cloud” and start thinking of it as “reclaiming control over their data.” That means clarifying ownership, improving lineage, fixing data quality issues, tightening access boundaries, and establishing a clear governance model. Without these fundamentals, cloud adoption doesn’t solve problems; it magnifies them. 

Once the data foundation is solid, modern technologies like containerized workloads, streaming pipelines, MLOps engines fit naturally into place. The organizations that truly succeed are the ones willing to take a steady, structured approach: stabilize first, govern second, automate third, scale last. It may not be flashy, but it’s the approach that consistently delivers results. 

You’ve built security architectures grounded in zero-trust principles for enterprise clients. How do you balance the need for robust security with the operational demands of petabyte-scale analytics? 

People often assume zero trust gets in the way of performance, but it’s actually one of the few models that keeps pace with petabyte-scale systems. Manual approvals and static rules can’t handle environments where vast amounts of data move rapidly across distributed platforms and hundreds of users or services touch that data. 

The balance comes from embedding security into the workflow instead of treating it as an external checkpoint. Every query, API call, or machine learning inference should carry identity context by default. When identity travels with the request, policies can be applied at the data layer with almost no friction. Tokenization, column-level protections, and encryption in transit and at rest become seamless behaviors rather than barriers. 

Then you add intelligent monitoring. At this scale, anomaly detection isn’t optional. Humans simply can’t track the patterns. With automated guardrails running silently in the background, zero trust actually empowers teams to move faster because the system is constantly watching for anything outside the norm. 

Machine learning deployment at scale remains a challenge for many enterprises. What does your MLOps practice look like when you’re moving a model from development into production, and what determines whether that transition succeeds or fails? 

I often remind teams that production failures rarely come from the model itself. They come from the lifecycle around the model not being engineered with enough rigor. A model that performs beautifully in development means very little unless it behaves reliably, transparently, and safely in real-world conditions. 

A strong MLOps practice includes reproducible training pipelines, strict versioning, clear lineage for features and artifacts, secure isolation for sensitive data, and constant monitoring for drift or unexpected behavior. You need to know why a prediction changed and whether that shift is acceptable or problematic. 

The biggest determinant of success is visibility. If teams can’t explain what caused a drop in performance or a change in behavior, trust evaporates quickly. And without trust, even the most advanced model won’t last in production. 

Organizations that excel treat models like long-lifecycle products. They invest in governance, documentation, validation, and feedback loops. None of this is glamorous, but it’s exactly what turns AI into a dependable business capability rather than a lab experiment. 

You’ve led data initiatives at organizations like Kaiser Permanente and Cleveland Clinic. Healthcare data comes with unique constraints around privacy and compliance. How do you architect systems that serve both analytical needs and regulatory requirements like HIPAA? 

The secret is treating compliance as a design parameter, not a final hurdle. With healthcare data, the safest approach is minimizing exposure right from the start. That’s why I prioritize de-identifying or tokenizing PHI before it ever enters analytical systems. Analysts and machine learning teams rarely need raw PHI, and removing it early dramatically reduces risk. 

Once that principle is established, the rest falls naturally into place: strong authentication, encryption at every layer, transparent lineage across the data lifecycle, and automated auditing that continuously proves compliance. When you design systems as if regulators, auditors, or even patients could look inside at any moment, compliance stops being a separate effort and becomes part of the architecture. 

This approach allows analytics and compliance to reinforce each other instead of competing for priority. 

Generative AI and agentic AI are reshaping enterprise technology strategies. How are you seeing organizations approach these technologies, and what separates the projects that deliver value from those that stall? 

Right now, I see two very different patterns. Some organizations are excited about generative and agentic AI but haven’t fixed their underlying data governance issues. Others are more disciplined and focus on establishing guardrails before deploying anything. 

Successful enterprises recognize that generative and agentic AI aren’t just tools. They’re systems that make decisions, produce content, and in the case of agentic AI, take actions autonomously. That power requires structure. The organizations that get results define clear rules around which data the AI can access, how prompts and actions should be logged, how outputs should be validated, and how privacy and safety are maintained. 

Agentic AI introduces an additional layer of responsibility. Since agents can perform multi-step tasks or trigger downstream workflows, weak governance can lead to amplified chaos. Enterprises that succeed build oversight mechanisms, clear escalation paths, and monitoring that tracks both model behavior and agent behavior. 

The projects that stall usually rush into experimentation without laying this groundwork. Without clean data, ownership clarity, and strong monitoring, generative and agentic AI quickly becomes unpredictable, and unpredictability erodes trust faster than anything else. 

You’ve received recognition for delivery on critical projects throughout your career. When you think about what makes a complex data initiative succeed, what factors matter most beyond the technical architecture itself? 

Across every major transformation I’ve been part of, the deciding factor has always been clarity. Technology is important, but clarity is what actually drives progress. Everyone from engineers to compliance teams to business stakeholders must understand why the initiative matters, what success looks like, and what their role is in achieving it. 

Most failures don’t come from flawed technical decisions. They happen in the seams between teams-ambiguous ownership, mismatched expectations, assumptions that were never spoken aloud. Those gaps can derail even the strongest architectural plan. 

But when alignment is strong, communication is open, and responsibilities are clearly understood, the most complex initiatives gain momentum quickly. When the human side is aligned, the technology finally has space to deliver its full value. 

Your background includes a mechanical engineering degree before you moved into data architecture. How has that engineering foundation influenced how you approach system design and problem-solving? 

Mechanical engineering taught me to think in systems. You learn early on that every structure has constraints, every componenthas tolerances, and every design choice has side effects. That mindset translates almost perfectly into cloud and AI system design. 

Even today, I approach distributed systems like engineered structures. I ask how components interact, where stress accumulates, what happens under extreme load, and how much safety margin exists. This perspective forces you to consider failure modes, not just performance peaks, and that discipline is invaluable when designing systems meant to operate reliably at scale. 

It’s a foundation I rely on constantly, and it shapes nearly every architectural decision I make. 

Cloud platforms continue to evolve rapidly, with new services and capabilities emerging constantly. How do you evaluate when to adopt new technologies versus when to rely on proven approaches, particularly for clients in risk-averse industries? 

I use a simple but strict filter: does this technology make the system safer, more reliable, or easier to operate? If the answer is Yes, it’s worth exploring. If the answer is No or if the value is unclear, then it probably doesn’t belong in a regulated or mission-critical environment. 

I love innovation, but I prioritize trust. In industries where risk tolerance is low, adopting a tool simply because it’s new or exciting can create long-lasting problems. A technology must earn its place by strengthening governance, reducing operational friction, or improving architectural resilience. 

When a solution proves its value without compromising stability, I’m very comfortable adopting it. But it needs to justify its presence 

Looking at the trajectory of enterprise AI adoption, what capabilities do you expect organizations will need to build over the next few years that many aren’t yet prioritizing? 

The organizations that will thrive in the next phase of enterprise AI are the ones that treat governance as a core capability. They’llneed strong processes for monitoring model behavior, identifying drift, evaluating bias, ensuring reproducibility, and maintainingethical oversight. Those capabilities will be as important as the models themselves. 

They’ll also need mature privacy practices, such as synthetic data generation, secure training methods, and more robust controls around sensitive information because AI will increasingly operate on regulated and confidential datasets. 

Finally, AI-driven security will become essential. As AI becomes embedded deeper in business operations, attackers will evolve as well. Organizations will need intelligent, adaptive defenses that can evolve just as quickly. 

Those who build these capabilities now won’t just adopt AI. They’ll scale it responsibly, sustainably, and with confidence.