As enterprises race to harness the transformative power of generative AI, most initiatives are quietly dying at the execution layer — not because the models aren’t capable, but because the foundations beneath them are crumbling. Data remains unstructured, governance is bolted on as an afterthought, and security teams are scrambling to defend systems that no longer behave predictably. The promise of GenAI is real, but so is the graveyard of abandoned pilots. Rahul Jha, VP of Cloud, GenAI & Cybersecurity at Visionet Systems, has seen this pattern up close. In this conversation, he breaks down the architectural blind spots, emerging threat surfaces, and the fundamental mindset shift enterprises must embrace to move from experimentation to genuine, scalable production.
VP of Cloud, GenAI & Cybersecurity
Visionet Systems
CISO Forum: What are the biggest architectural gaps preventing enterprises from moving GenAI from pilot to production at scale?
Rahul Jha: The biggest gap is the lack of a strong “AI or intelligence foundation” that can scale across multiple use cases. This includes both data readiness (and “knowledge readiness”) and the ability to implement solutions in a standard and secure way. Many organizations spend about 30–40% of their time just preparing data, and there is no unified knowledge fabric that different teams can reuse.
Additionally, while building agents is becoming easy, deploying them consistently, securely, and at scale remains a major challenge.
CISO Forum: Why do most GenAI initiatives fail at the execution layer despite strong model capabilities?
Rahul Jha: Most GenAI initiatives don’t fail because the models aren’t good enough. Instead, they fail in execution. Building a prototype or even an agent is now relatively easy, but taking them into production is hard. That’s where challenges around standardization, security, and operationalizing them within existing enterprise environments arise.
Many organizations lack the necessary foundation to deploy, manage, and scale these systems properly. Things like data governance, monitoring, and integrating GenAI into day-to-day workflows are often overlooked. So, even if the underlying models are strong, these systems struggle to translate into reliable, usable systems at scale.
CISO Forum: How should enterprises rethink cloud architecture to support production-grade GenAI workloads?
Rahul Jha: Enterprises should think beyond traditional cloud modernization and move toward “intelligence modernization.” This involves using cloud platforms as the foundation for building AI systems, leveraging hyperscaler ecosystems, and adopting API-driven architectures. The environment must be flexible enough to work across multiple models, frameworks, and providers. Security and governance need to be embedded from the start, and the core principles of cloud modernization still apply, but with added nuances for AI workloads.
CISO Forum: What new cybersecurity risks are emerging specifically from GenAI deployments in enterprise environments?
Rahul Jha: GenAI introduces entirely new attack surfaces and challenges. Systems are no longer deterministic, and behavior can vary even with the same input. New risks include conversational attack vectors, prompt injection, and challenges in securing agent reasoning and decision-making. There are also concerns around agent identity, authentication, and authorization. Additionally, organizations must secure multiple layers, such as intelligent supply chain architecture, agent consumption, agent ecosystems, and data, while adapting to a much faster development lifecycle.
CISO Forum: How can CISOs embed governance, auditability, and control into AI systems without slowing innovation?
Rahul Jha: CISOs need to rethink governance for a non-deterministic, fast-moving environment. Instead of adding heavy controls at the end, governance has to be built in from the start, across the full stack. That means looking at everything from model and data pipelines to agent behavior and how these systems interact with other tools and users. Practices like agent identity management, prompt-level security, output guardrails, and clear access controls help bring structure without getting in the way.
At the same time, it’s about making governance continuous rather than static. You need visibility into how models are behaving in real time, along with lifecycle tracking from creation to deployment to decommissioning. The goal isn’t to slow things down, but to put the right guardrails in place so teams can move fast without losing control. It’s as much a mindset shift as it is a tooling one.
CISO Forum: What does a “production-ready” GenAI stack look like in terms of data, infrastructure, and security integration?
Rahul Jha: A production-ready stack is built on a strong intelligence foundation. This includes:
● A well-prepared knowledge layer (data converted into a reusable knowledge fabric)
● A flexible infrastructure that can integrate multiple models, frameworks, and cloud providers
● API-driven architecture for interoperability
● Embedded security, governance, and compliance at every layer
● Standardized mechanisms for deploying and managing agents securely at scale
The focus is less on building agents and more on enabling scalable, secure, and standardized production environments.
CISO Forum: What are the key indicators that an enterprise is truly achieving ROI from GenAI—not just running pilots?
Rahul Jha: Real ROI from GenAI starts to show when it moves beyond isolated pilots and becomes part of how the business actually operates. It’s not driven by a single use case but by scaling multiple use cases across the enterprise, whether that’s customer support, internal operations, or decision-making. Organizations that achieve ROI are those that can rapidly build and deploy multiple agents on a shared foundation, rather than starting from scratch each time.
Another key indicator is how well the organization can reuse data (knowledge readiness) and scale it across diverse use cases. If data is structured, accessible, and consistently fed into different applications, that’s a sign of maturity. You also see ROI when deployments are not one-off efforts but are repeatable, measurable, and tied to clear business outcomes like cost reduction, faster turnaround times, or improved user experience.
