By: Zach Miller
What does it really take to build AI systems that scale across massive enterprises, while staying fast, smart, and secure? In this exclusive interview, we sit down with Phanish Lakkarasu, a leading voice in AI infrastructure and enterprise security. Phanish’s career spans over a decade of innovation at companies like Visa and Qualys, where he’s contributed to platforms that power real-time fraud detection, automated compliance, and next-gen cybersecurity.
Phanish speaks from deep experience in MLOps, LLMOps, and big data, balancing technical mastery with a clear vision for what’s next. This interview covers the challenges of maintaining model performance at scale, automating complex workflows, and building AI systems that have the potential to evolve. How do global firms manage their AI behind the scenes? This interview touches on all of this and more.
Q1: Phanish, thank you for your time. You have such a remarkable journey from building early Hadoop ecosystems to leading enterprise-grade AI infrastructures at Visa. Can you walk us through the experiences that shaped your transition into architecting intelligent, scalable AI systems?
Phanish Lakkarasu: Thank you. My journey into architecting intelligent and scalable AI systems began during the early days of big data, where I worked on building foundational ecosystems like Hadoop and Cloudera. These platforms gave me a solid grounding in distributed computing, data pipelines, and scalable infrastructure. Over time, as data grew not just in volume but in velocity and complexity, I recognized the need for more intelligent, adaptive systems that could extract real-time insights while maintaining enterprise-grade reliability.
This realization led me to shift focus toward AI-driven automation and MLOps—work that later evolved into LLMOps and scalable cloud-native AI infrastructures. Working with organizations like Visa, Bank of America, and Walmart gave me a front-row seat to mission-critical challenges like fraud detection, compliance automation, and real-time decision-making. These use cases demanded systems that were not only intelligent but also secure, resilient, and scalable.
At Qualys, I currently focus on integrating AI with cybersecurity, building robust AI architectures that are cloud-agnostic and resilient under high-load conditions. My U.S. patent (US 10733555) also reflects this journey—innovating in distributed AI execution and automation. Altogether, these experiences shaped my philosophy: intelligent systems must be adaptable, secure, and deeply integrated into enterprise operations to drive meaningful value.
Q2: In your research article “Hyper-Personalized AI: Advancing AI-Powered Fraud Detection with Adaptive Learning Algorithms,” you explore how real-time adaptability is revolutionizing transaction monitoring. How do you envision adaptive learning shaping the next generation of fraud detection systems in environments like FinTech?
Phanish Lakkarasu: Adaptive learning is redefining fraud detection by making systems more context-aware and continuously self-improving. In traditional fraud detection models, rules and thresholds are often static, requiring manual updates to remain effective. In contrast, adaptive learning models evolve in real-time, analyzing transaction behavior dynamically to detect subtle shifts and emerging fraud patterns.
In FinTech environments, where transaction volume and complexity are immense, adaptive learning enables hyper-personalization. Each user’s behavioral profile becomes a unique signal, allowing the system to detect anomalies not just based on static rules, but on deviations from that user’s norm. This helps reduce false positives while improving detection accuracy for novel fraud types.
At Visa and in my broader AI security work, I’ve applied these principles to build AI systems that analyze streaming data with millisecond latency, adjusting risk scores and model responses on the fly. This real-time adaptability enhances security while also helping with regulatory compliance and customer trust. I believe the future of fraud detection lies in systems that are not just intelligent but also self-aware, learning continuously from each interaction to stay ahead of threats.
Q3: Your role at Visa involves deep integration with open-source tools like Apache Spark, Kafka, and Iceberg. Considering the fast-evolving landscape of data engineering, how do you strike a balance between utilizing open-source innovation and maintaining enterprise-grade reliability and compliance?
Phanish Lakkarasu: Open-source tools like Apache Spark, Kafka, and Iceberg are powerful enablers of innovation. They allow rapid experimentation, scalability, and integration across complex data ecosystems. At Visa and throughout my career, I’ve embraced these tools to build real-time analytics platforms, scalable ML pipelines, and data lakehouses that support everything from fraud detection to financial forecasting.
However, integrating these tools into enterprise environments requires a careful approach. First, we enforce strong DevSecOps and MLOps principles—embedding security, observability, and governance into every stage of the pipeline. This helps ensure that while we take advantage of the agility of open-source, we also maintain reliability and compliance.
Second, I often lead efforts to harden and extend open-source tools to meet enterprise requirements—whether it’s enhancing Spark for low-latency inference or using Iceberg with secure, version-controlled data snapshots. We also use cloud-native frameworks to enforce access controls, encryption, and auditing at scale.
Ultimately, the balance comes from a layered strategy: harness open innovation at the core, but surround it with enterprise-grade tooling and automation to meet compliance, scalability, and performance needs.
Q4: In your paper “Optimizing Enterprise AI Infrastructures: The Convergence of Agentic AI and Cloud-Native Security,” you highlight the potential of Agentic AI in cloud ecosystems. Could you expand on how you’re applying these principles in practice, particularly at Visa, to enhance cybersecurity and operational autonomy?
Phanish Lakkarasu: Absolutely. Agentic AI—AI systems that operate with a degree of autonomy, making proactive decisions based on predefined goals and evolving contexts, could play a significant role in cloud-native security and enterprise resilience.
At Visa and in my work with security-driven platforms like Qualys, we’re applying Agentic AI to enable real-time threat detection, dynamic risk assessment, and automated incident response. These intelligent agents monitor behavior across cloud environments, detect anomalies, and even initiate remediation workflows autonomously, reducing response time and human intervention.
For example, in our cloud infrastructure, these agents can detect a suspicious access pattern, cross-reference it with fraud signals from transaction data, and automatically initiate a lockdown or route the case for deeper ML-based investigation. Because they’re context-aware and continuously learning, these agents evolve as threats evolve, making them more effective against zero-day attacks and insider threats.
From a security standpoint, this approach aligns with zero-trust architectures, where each action is validated, logged, and monitored. Operationally, it enhances system resilience, reduces alert fatigue for security teams, and offers audit-ready transparency. Agentic AI is not just about automation—it’s about empowering systems with intelligence and autonomy to act safely and efficiently within enterprise boundaries.
Q5: With your dual expertise in machine learning operations and security, how do you approach the challenge of explainability in AI, especially when dealing with high-stakes financial transactions where regulatory transparency is crucial?
Phanish Lakkarasu: Explainability in AI, especially in high-stakes domains like financial transactions, isn’t just a technical requirement—it’s often a regulatory and ethical consideration. My approach starts with embedding interpretability into the MLOps lifecycle from day one, rather than treating it as an afterthought.
At Visa and in my broader work with financial institutions, we use a combination of model-agnostic explainability tools (like SHAP and LIME) and domain-specific rule layering to make complex models more transparent. For example, when a transaction is flagged as fraudulent, we ensure the system can trace back the decision to understandable features, such as location deviation, behavioral anomalies, or velocity of transaction attempts.
We also build explainability pipelines into the model deployment phase, where model outputs are accompanied by reason codes and visual summaries tailored for regulators, compliance teams, and business stakeholders. These insights are audit-ready and support real-time decision-making without sacrificing transparency.
From a security perspective, this clarity also helps in distinguishing true threats from noise, reducing false positives while maintaining compliance with frameworks like GDPR, PCI DSS, and emerging AI governance standards. Ultimately, my goal is to make AI not just accurate, but accountable, and explainability is the bridge between intelligence and trust.
Q6: You’ve worked with impressive technologies, from Hadoop and Cloudera to generative and neuromorphic AI models. Which emerging technology do you believe holds the most transformative potential for AI infrastructure over the next five years, and why?
Phanish Lakkarasu: Among the emerging technologies I’ve worked with, I believe Agentic AI, combined with neuromorphic computing, holds significant transformative potential for the next wave of AI infrastructure.
Agentic AI, which I’ve been advancing in cloud-native environments, represents a shift from reactive automation to proactive, goal-oriented intelligence. When this is paired with neuromorphic computing—hardware and architectures modeled after the human brain—it could enable ultra-efficient, low-latency AI systems that learn and adapt on the edge, with minimal energy and infrastructure overhead.
Why is this important? Enterprise AI is moving toward real-time, decentralized decision-making. From fraud detection at the transaction edge to autonomous threat prevention in cybersecurity, latency and adaptability are crucial. Neuromorphic architectures can run intelligent agents locally, allowing them to operate securely, even in environments with constrained connectivity or power.
Additionally, these technologies are inherently privacy-preserving and energy-efficient, two growing priorities in both FinTech and AI ethics. Over the next five years, I envision AI infrastructure evolving from centralized cloud deployments to hybrid edge-intelligence models that blend Agentic behavior with neuromorphic efficiency, reshaping how we think about speed, scale, and trust in enterprise AI.
Summary
Talking with Phanish Lakkarasu is incredibly eye-opening and insightful, as he doesn’t just understand infrastructure, but he builds it to handle real-world chaos, scale, and threats. From fraud detection in finance to self-improving AI systems, his work focuses on creating foundations that stand up to pressure and adapt quickly. His emphasis is on clarity, making AI systems not just powerful, but understandable, secure, and ready for whatever comes next.
All this makes this interview a snapshot of what’s already unfolding across the tech world. For anyone working in AI, security, or infrastructure, his insights are highly valuable. As businesses look to scale smarter and more securely, Phanish offers a practical view of what that path might look like.