Persistent Systems, Kong join forces to secure enterprise AI scaling

2026-05-26 · Source: Tech Monitor · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, short

Summary

Persistent Systems and Kong have partnered to address the challenges enterprises face in scaling and governing AI systems from experimentation to production. The collaboration aims to unify connectivity, security, and policy management across APIs, data pipelines, AI models, and agent-based workflows. Persistent Systems highlights that the primary obstacle for enterprise AI is no longer model access but coordinating complex operational and security requirements, especially in hybrid or multi-cloud environments. As Kong's global systems integration partner, Persistent Systems will combine its engineering services with Kong's unified API and AI connectivity platform. This joint offering will modernize legacy API infrastructures, apply centralized policy controls, enhance visibility into AI and data operations, and reduce operational costs. It also supports generative AI and agent-based architectures, including Model Context Protocol workflows, with built-in security for PII protection and compliance-driven observability.

Key takeaway

For AI Architects or Directors of AI/ML scaling enterprise AI initiatives, you should prioritize establishing a unified control layer for API and AI connectivity. This partnership offers a solution to centralize policy enforcement, secure access, and gain observability across your diverse AI workloads and data pipelines, especially in hybrid or multi-cloud environments. Implementing such a framework will reduce fragmentation, mitigate risks, and accelerate the delivery of production-grade AI systems.

Key insights

Effective governance of intelligence flow across systems, not model access, defines enterprise AI success.

Principles

• APIs function as the control layer for enterprise AI.
• A unified control layer is essential for scalable, secure AI.
• Operational and security coordination is the main AI deployment challenge.

In practice

• Modernize legacy API infrastructures for AI.
• Centralize policy controls for AI and data operations.
• Implement PII protection and access management for AI traffic.

Topics

• Enterprise AI
• AI Governance
• API Management
• Multi-Cloud AI
• Persistent Systems
• Kong
• Generative AI

Best for: CTO, VP of Engineering/Data, Executive, AI Architect, Director of AI/ML, MLOps Engineer

Related on AIssential

• IBM’s enterprise AI strategy makes trust and control the production test — Governed enterprise AI, integrated with hybrid cloud and robust security, is essential for production-scale trust and value.
• We Didn’t Know What We Didn’t Know: Standing Up Enterprise AI Services at Scale — Scaling enterprise AI requires robust infrastructure, clear policy, and strong cross-functional partnerships, not just advanced models.
• Governing Coding Agent Sprawl with Unity AI Gateway — Unified governance platforms are crucial for managing security, cost, and visibility of diverse AI coding agents.
• KubeCon Europe 2026: The Not-So-Unseen Engine Behind AI Innovation? — Kubernetes is evolving into the enterprise AI control plane, driven by abstraction, standardization, and integrated governance.
• AI Is Turning Unified Storage Into A Strategic Decision — AI's reliance on live enterprise data elevates storage to a strategic component for capability, performance, and GRC.
• The End of Software’s Monopoly on Work — AI agents shift enterprise work from applications to a trusted data context layer, demanding unified, governed data for reliable action.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Monitor.