Navigating secure AI deployment: Architecture for enhancing AI system security and safety

Navigating secure AI deployment: Architecture for enhancing AI system security and safety 1. Edge and ingress Key component 2. Identity and access Key components 3. Model, compute, and tools (runtime) Key components 4. Model and data protection Key components 5. Safety and guardrails Key components 6. Observability Key component 7. Governance and lifecycle controls Key components Conclusion The adaptable enterprise: Why AI readiness is disruption readiness About the authors Ishu Verma Florencio Cano Gabarda More like this Smarter troubleshooting with the new MCP server for Red Hat Enterprise Linux (now in developer preview) Demystifying llm-d and vLLM: The race to production Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share In the previous articles, we discussed how integrating AI into business-critical systems opens up enterprises to a new set of risks with AI security and AI safety [ link ], and explored the evolving AI security and safety threat landscape, drawing from leading frameworks such as MITRE ATLAS, NIST, OWASP, and others [ link ]. In this article, we'll examine the architectural considerations for deploying AI systems that are both secure and safe. A resilient AI architecture must be designed with a defense-in-depth philosophy, integrating controls that address both traditional cybersecurity threats and unique AI safety risks. The following components are essential pillars of an enterprise-grade AI system implementation that puts security front-and-center. These components are layered on top of each other to create a comprehensive security posture.