AI Workload Security on AWS: a lens over your audit
CloudArq's AI Workload lens is a view over your read-only AWS audit that surfaces the security and cost risk specific to AI workloads — Bedrock agents, RAG, guardrails, cross-region residency, and runaway spend. It is not a separate scanner and not a new category: each finding still counts toward its real Security or Cost pillar.
Updated 2026-06-29 · ~6 minute read
What the AI Workload lens is
The AI Workload lens is a view — a computed is_ai_workload flag — over findings you already get from your CloudArq audit. When a finding touches an AI workload (a Bedrock agent, a Knowledge Base, a guardrail, an inference profile), the lens surfaces it together with the others, so you can read your AI risk in one place.
It is not a separate scanner and not a seventh category. Every finding the lens shows still counts toward its real Security or Cost pillar in your score — the lens just gives that subset a name.
The 6 AI Workload checks
Each check describes a capability the read-only audit can surface — what a misconfiguration would expose or cost — never an observed attack or an asserted violation.
Agentic blast radius
Maps the IAM blast radius a compromised Bedrock agent could reach through its action-group Lambda execution role: the capability it would have, not whether it has been abused.
AI cross-region data residency
Flags an EU Bedrock workload that invoked a US or global inference profile — that profile can route the prompt to a US region. CloudArq never inspects the payload and never asserts a violation.
RAG vector-store cost
Spots a Bedrock Knowledge Base on OpenSearch Serverless paying the ~$350/mo 2-OCU minimum when S3 Vectors or pgvector would usually be far cheaper for a small knowledge base.
Bedrock guardrail parity
Finds Bedrock guardrails missing baseline content / PII protection — configuration gaps, not a claim that CloudArq catches unshielded model calls.
Bedrock cost optimization
Surfaces the standard Bedrock cost levers (prompt caching, Batch inference, model routing) when spend is meaningful; model-specific guidance needs invocation logging, which the finding states plainly.
Runaway-agent anomaly
Catches a Lambda recursive loop via AWS's RecursiveInvocationsDropped metric — a runaway-agent cost and anomaly signal. CloudArq reads the metric; it does not inspect agent reasoning.
How it works
What the lens looks like in your audit
Illustrative only — sample rows, not a real account. Each finding keeps its real Security or Cost pillar and ships with a guided fix.
Frequently asked
- 01Is this a separate product or scanner?
- No. The AI Workload lens is a view over your existing CloudArq audit — a computed is_ai_workload flag on findings you already get. There is no separate scanner to install and no seventh category; every AI finding still counts toward its real Security or Cost pillar.
- 02Does CloudArq need write access to my AI workloads?
- No. CloudArq connects through a read-only IAM role secured with an ExternalId. It never stores your credentials, application data, database contents, or S3 objects, and the audit itself is read-only — detection plus a guided fix, never auto-fix.
- 03What is agentic blast radius?
- The set of AWS permissions a compromised Bedrock agent could reach through its action-group Lambda execution role — the reachable capability, not an observed attack. CloudArq maps what that role could do; it never claims the agent has been abused.
- 04Which tiers include the AI Workload checks?
- The AI Workload lens ships on the Max tier. See the pricing page for the full tier breakdown of which checks each plan includes.