AI Security for Cloud-Native Enterprises
Govern every AI agent, workload, and model across your multi-cloud environment. Policy enforcement, behavioral monitoring, and compliance — at cloud scale. From ephemeral Kubernetes workloads to serverless LLM APIs.
Cloud Coverage
RuntimeAI integrates natively with every major cloud AI platform through the AI Integration Fabric. One control plane. Every cloud. Consistent policy.
Why Traditional Cloud Security Fails for AI Workloads
Cloud AI workloads don't fit asset-based security models. They're ephemeral, they call external APIs, and they move sensitive data across trust boundaries — invisibly.
AI Workloads Bypass Traditional CSPM
Agents don't fit asset-based security models. CSPM tools were built for static infrastructure — not for LLM API calls, agent sessions, and ephemeral compute that appears and disappears within minutes.
Multi-Cloud AI Creates Ungoverned Identity Sprawl
Agents running across three clouds have three separate identity fabrics. Cross-cloud governance requires a layer above each provider — one that enforces consistent policy regardless of where the workload runs.
LLM API Calls Move Sensitive Data With No Visibility
Every LLM API call is a potential data exfiltration vector. PII, IP, and regulated data flows from your cloud workloads to external model endpoints with no inspection, no enforcement, and no audit trail.
Ephemeral Workloads Outrun Traditional Logging
Cloud-native AI means workloads that exist for seconds or minutes. By the time a traditional SIEM processes the event, the workload is gone — and so is the evidence of what it did.
Six Capabilities That Govern Cloud AI at Scale
From workload discovery to compliance automation — a unified cloud AI governance layer that works at the speed and scale of modern cloud infrastructure.
Cloud AI Workload Governance
Discover and govern every AI workload across AWS, Azure, and GCP. Enforce policy at the workload level — not just the perimeter. Every model call, every agent session, every API invocation governed.
Multi-Cloud Agent Visibility
Real-time inventory of every AI agent running in your cloud environment. Know what's running, what it's accessing, what it's spending, and what it's sending to external model endpoints — across all three clouds simultaneously.
LLM API Traffic Control
Inspect and enforce policy on every LLM API call. Block prompt injection. Redact PII before it reaches the model. Rate-limit by tenant, agent, or cost center. Full audit trail of what was sent and returned.
Cloud Identity Fabric
Extend zero-trust identity to cloud-native AI workloads. KYA credentials for agents running in EKS, AKS, GKE, and serverless. No anonymous workloads. No cross-cloud identity gaps. Revocation in under 5 seconds.
Behavioral Anomaly Detection
Continuous monitoring of agent behavior across cloud environments. Detect behavioral drift, flag anomalies, and auto-remediate policy violations before they become incidents — at cloud speed.
Cloud Compliance Automation
Continuous compliance monitoring across NIST CSF, SOC 2, FedRAMP, CIS Benchmarks, and ISO 27001 — with AI-specific controls layered on top. One dashboard. One evidence board. All three clouds.
Cloud AI Use Cases
What governance at cloud scale actually looks like in production enterprise deployments.
Agents deployed across AWS Bedrock, Azure OpenAI, and GCP Vertex — each with different IAM models, different audit capabilities, and different compliance requirements.
Unified policy enforcement across all three providers through a single control plane. Consistent governance, consistent audit trail, consistent compliance posture — regardless of which cloud the agent runs on.
Cloud workloads calling external LLM APIs with no inspection layer between the application and the model endpoint. PII and regulated data flows without visibility or enforcement.
PII Shield intercepts every LLM API call and tokenizes sensitive data before it reaches the model. Full audit trail of what was sent, what was redacted, and what was returned — satisfying regulators and privacy officers.
Agents running in Kubernetes across three cloud providers — each pod ephemeral, each session brief, traditional IAM unable to track them at the granularity needed for audit.
Every agent running in EKS, AKS, or GKE gets a KYA credential — quantum-safe, revocable in under 5 seconds, with a full authorization chain that survives the pod lifecycle for audit and forensics.
Compliance & Standards
Cloud AI security posture that maps directly to the frameworks your security organization reports against.
One Control Plane for All Your Cloud AI
Multi-cloud AI governance shouldn't require three separate security programs. See how RuntimeAI unifies policy enforcement, identity, and compliance across every cloud in 20 minutes.