Cloud & AI Workload Security Architecture
Architecture for the Cloud You're Running Today and the AI Workloads You're Deploying Tomorrow
Cloud is no longer a destination. Most enterprises are operating multi-cloud, hybrid, and cloud-native, running workloads across AWS, Azure, GCP, SaaS platforms, and increasingly across emerging AI infrastructure that doesn't fit cleanly into any of those categories. The security architecture for this environment looks fundamentally different from the cloud security playbook of five years ago.
AI workloads have become the fastest-growing layer of cloud infrastructure investment, introducing new architectural problems that most existing cloud security programs were not designed to address:
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Where do you deploy LLMs?
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How do you secure self-hosted models versus managed APIs versus hybrid patterns?
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How do you architect for AI agents acting in your environment with delegated authority?
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How do you protect AI training and inference data pipelines?
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How do you control egress when models can exfiltrate data through their outputs?
Our Cloud & AI Workload Security Architecture service helps you design the security architecture across both layers: the cloud-native infrastructure your workloads run on, and the AI infrastructure that increasingly sits on top. We assess your current state across CSPM, CWPP, CIEM, and emerging AI security tooling; design a target architecture aligned to Zero Trust principles; evaluate the fragmented cloud and AI security vendor landscape vendor-neutrally; and deliver a phased roadmap your team can actually execute. TBDCyber doesn't sell or resell cloud security tooling.
Our Tailored Approach Can Include
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Cloud Security Posture
& Architecture Assessment
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Multi-cloud and hybrid cloud security posture assessment across AWS, Azure, GCP, and SaaS platforms.
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Evaluation of existing CSPM (Cloud Security Posture Management), CWPP (Cloud Workload Protection), CIEM (Cloud Infrastructure Entitlement Management), and CNAPP (Cloud-Native Application Protection Platform) coverage and gaps.
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Cloud landing zone and account-structure review against best practices.
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Identity and access posture in cloud, including overlap with the broader Identity Strategy and PAM/CIEM work
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Network architecture review: VPC design, segmentation, egress controls, private connectivity, and east-west traffic visibility.
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Container and Kubernetes security posture, including cluster configuration, runtime protection, and supply chain controls.
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Serverless and event-driven workload security assessment.
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AI workload inventory: where models are running, what data they're touching, what identities they're operating under.
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Cloud Security
Architecture Design
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Reference architecture aligned to Zero Trust principles, mapped to AWS, Azure, GCP, and multi-cloud environments.
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Cloud landing zone architecture: account structure, network design, identity foundation, logging and telemetry, baseline guardrails.
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Cloud-native data protection architecture: encryption, key management, classification-driven controls, and data flow restrictions.
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Container and Kubernetes security architecture: image supply chain, admission control, runtime protection, network policy, and workload identity.
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Serverless security architecture: function permissions, event source controls, and execution isolation.
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Multi-cloud and hybrid cloud strategy, including consistent control plane patterns across environments.
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Cryptographic architecture, including post-quantum cryptography (PQC) readiness for cloud-resident data and connections
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AI Workload
Security Architecture
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LLM deployment patterns: managed APIs, self-hosted open-weight models, hybrid retrieval-augmented patterns, and the security trade-offs across each.
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AI agent infrastructure: Model Context Protocol (MCP) server security, AI gateway architecture, agent orchestration platforms, and agent-to-agent trust patterns.
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Model supply chain security: provenance, integrity, and tampering controls for models pulled from public hubs (Hugging Face, model registries) or fine-tuned internally.
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AI data pipeline security: training data protection, retrieval-augmented generation (RAG) source controls, and inference-time data leakage prevention.
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AI-specific attack surfaces: prompt injection at the infrastructure layer, model exfiltration, jailbreak resistance at the deployment layer, and adversarial input controls.
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Egress and data-flow controls for AI workloads.
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AI workload identity, integrating with the Non-Human & AI Agent Identity Strategy service for the identity-pillar deep dive.
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Observability for AI workloads: logging, tracing, and the unique audit requirements that AI introduces.
Technology Selection,
Roadmap & Governance
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Vendor-neutral evaluation across the cloud security landscape: CNAPP platforms, CSPM, CWPP, CIEM, and Kubernetes-specific platforms.
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Vendor-neutral evaluation of the emerging AI security tooling landscape: AI security posture management, prompt firewall and AI gateway platforms, model supply chain tools, and adjacent categories.
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Tool consolidation strategy: most cloud security stacks have grown to 8–15 tools with significant overlap, and rationalization is often higher leverage than buying new tooling.
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Phased roadmap with quick wins, strategic investments, and dependencies clearly sequenced.
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Governance and operating model: cloud security ownership across security, platform engineering, application teams, and AI initiative owners.
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Cross-cloud governance for hybrid and multi-cloud environments, including consistent control plane, consistent reporting, and consistent accountability.
Benefits
Architecture for the Cloud You're Actually Running
Multi-cloud, hybrid, cloud-native, with AI workloads layered on top. The architecture we design fits the environment you have, not the simplified one in the reference diagrams.
AI Workload Security as a First-Class Pillar
Most cloud security programs haven't yet kept pace with the AI workloads now running on their infrastructure. We explicitly address LLM deployment, AI agent infrastructure, model supply chain, and AI-specific attack surfaces.
Tool Consolidation, Not Just Tool Addition
Most cloud security stacks are bloated. We help you rationalize what you have before adding what you don't, often saving more than the cost of the engagement in the process.
Connected to Your Broader Zero Trust Architecture
Cloud and AI workload security architecture is one pillar of your Zero Trust program, not a parallel initiative. We design it to integrate with identity, application, data, and visibility pillars from the start.
Related Services
Cloud & AI Workload Security Architecture is one pillar of a broader Zero Trust program. See Zero Trust Architecture Strategy for the full architectural anchor.
Building secure applications and AI features on top of this infrastructure? See Secure DevOps, Threat Modeling, and Secure AI Development.
Need governance and policy for the AI itself, not just the infrastructure? See AI Governance.
Identity for AI agents and other non-human actors? See Non-Human & AI Agent Identity.
Privileged access in cloud (CIEM)? See Privileged Access Management.
Data classification and protection in cloud? See Data Security Roadmap & Implementation.
Resources and Insights
Your AI Workloads Are Running on a Cloud Architecture That Wasn't Designed for Them.
Most enterprise cloud security programs were architected before LLM deployment patterns, AI agents, MCP servers, and agentic AI infrastructure entered the conversation. The result is an architecture that secures the cloud reasonably well and the AI workloads running on it almost not at all. Adversaries already know this.
TBDCyber designs cloud and AI workload security architectures that address both layers, anchored to Zero Trust principles, vendor-neutral, and built to integrate with the rest of your security program.
Talk to a Cloud Security Architect →