Enterprise Security Platform for On-Premises

Zero Trust-aligned foundation to secure AI workloads and modern infrastructure within enterprise-controlled environments
Design Intent

This design assumes a centrally governed security operating model where all AI and modernised workloads are executed within enterprise data centre boundaries under strict identity-first control on on-premises infrastructure. Governance is enforced through directory-led access discipline and continuous monitoring, with Microsoft Entra ID anchoring identity assurance and Microsoft Purview reinforcing compliance visibility. Consumption follows a least-privilege model, ensuring all interactions are authenticated, authorised, and auditable before execution. The approach prioritises containment, traceability, and resilience over open or decentralised workload execution.

Design
Design Walkthrough
  • Establishing identity and policy controls before any workload interaction ensures every access request is verified upfront, preventing unauthorised execution paths and enforcing Zero Trust discipline (Microsoft Entra ID, ServiceNow CMDB)
  • Isolating runtime environments on dedicated compute and container platforms limits lateral movement and reduces blast radius in case of compromise, protecting both AI models and infrastructure (VMware, NVIDIA GPU, Kubernetes, OpenShift)
  • Mediating all enterprise data access through governed services ensures consistent policy enforcement and prevents direct, uncontrolled interaction with backend systems (Data Lake, Object Storage, API Gateway)
  • Separating threat detection from compliance governance allows real-time monitoring and longer-term audit validation to operate independently, preventing blind spots between security response and regulatory assurance (IBM QRadar, Microsoft Purview)
  • Centralising observability and telemetry across all layers ensures operational visibility and rapid anomaly detection, preventing delayed response to security or performance issues (New Relic, Security Logs)
Operational Outcomes
Enables
  • Consistent enforcement of Zero Trust across AI and enterprise workloads
  • Controlled and auditable access to sensitive data and runtime environments
  • Reduced risk through workload isolation and governed integrations
  • Continuous visibility into security posture and operational health
Good fit when
  • AI workloads must remain within on-premises environments for compliance
  • Enterprise security governance cannot be decentralised across teams
  • Sensitive data requires strict access control and auditability
  • Infrastructure modernisation must align with existing security controls
This reference architecture reflects patterns we see when enterprises attempt to standardise platforms while still allowing teams to move at different speeds.

A practical way to understand whether our approach fits your operating reality.

© 2026 Chavan. All rights reserved
© 2026 Chavan. All rights reserved