Agentic AI is moving from enterprise pilots to production. And as it does, the security gap is becoming harder to ignore.
AI agents act autonomously, touch live data, and make decisions at machine speed. They access enterprise systems, trigger workflows, and take real actions across infrastructure. Traditional security tools were built for a different threat surface entirely.
DAXA has joined the NVIDIA Inception program, bringing the company into the ecosystem where the enterprise agentic AI stack is being built and marking a significant step in its mission to secure agentic AI at enterprise scale.
What Is the NVIDIA Inception Program
The NVIDIA Inception program is designed to support startups at the forefront of industry transformation. It provides access to NVIDIA's technology stack, go-to-market resources, and a global network of partners and customers building the next generation of AI infrastructure.
For AI security and governance companies, joining Inception signals alignment with the infrastructure layer where enterprise AI is actually being deployed, from accelerated compute and NeMo-based agent frameworks to the enterprise partnerships that determine how agentic AI reaches production.
Why Agentic AI Demands a New Security and Governance Model
The shift from AI assistants to AI agents changes the risk model fundamentally.
Earlier AI risks were primarily about data exposure. What data could the model see? What could it leak?
With agentic AI, the bigger risk is system impact. What actions can the agent take? What can it modify, trigger, or delete, autonomously, at machine speed, without a human in the loop?
This is not a theoretical concern. Enterprises are already discovering what happens when agents operate without the right governance layer. The same qualities that make agentic AI compelling (autonomy, speed, continuous operation) are what make ungoverned agents dangerous when the governance layer is missing.
The security industry is catching up. But the infrastructure layer and the governance layer are being built simultaneously, often by different teams, without coordination. Governance cannot be an afterthought in this environment.
DAXA’s Approach: Runtime Governance That Travels With the Agent
DAXA's platform provides runtime data governance that travels with the agent across the enterprise AI stack.
Rather than relying on perimeter controls or model-level guardrails, DAXA enforces governance at the point where data is accessed and actions are taken: before sensitive data reaches the model, and before agent decisions execute against enterprise systems.
This covers three layers that matter most for enterprise agentic AI deployments:
- Data Access Governance Controlling what data AI agents can discover, retrieve, and act on, with fine-grained permissions enforced at the retrieval layer, not assumed at the infrastructure level.
- Inference-Layer Policy Governing how agents reason and what actions they can take, including requiring human confirmation for destructive or irreversible operations before they execute.
- Runtime Visibility A complete audit trail of what agents accessed, which models were invoked, and what actions were taken, making agentic AI deployable in regulated industries where accountability is non-negotiable.
Conclusion: Building AI Governance Where Enterprise AI Is Being Deployed
The NVIDIA Inception program brings DAXA into the ecosystem where the enterprise agentic AI stack is being built. There is particular strategic interest in how NVIDIA's NeMo framework and agentic safety infrastructure can complement runtime data governance for enterprise deployments at scale.
As enterprises move from evaluating agentic AI to deploying it in production, the governance layer becomes infrastructure. Not an add-on. A foundational requirement for any deployment that touches sensitive data or takes consequential actions.
“Joining NVIDIA Inception validates the direction we have believed in from day one,” said Huseni Saboowala, Co-founder and CEO of DAXA. “Enterprises are rapidly deploying AI agents that operate autonomously across sensitive data and critical systems. In this new era, governance cannot be fragmented across disconnected security, inference, and data layers. It has to be holistic, embedded at runtime, and travel with the agent itself. We believe the governance layer will become foundational infrastructure for enterprise AI, and we’re excited to help build that future alongside the NVIDIA ecosystem.”
