DeepInspect vs Wiz AI-SPM: Runtime Enforcement vs Cloud AI Posture Management

A platform engineering lead I spoke with in June had just finished a Wiz AI-SPM rollout across four AWS accounts. The security graph surfaced a SageMaker endpoint with a training bucket sitting in a publicly reachable subnet, and the team closed it inside a week. Good catch. Two months later, an internal audit asked a different question: when the customer-support agent called the underwriting model last Tuesday with a claimant's SSN in the prompt, who authorized that specific call, and where's the record. The security graph had nothing to show, because it was never built to watch a single request happen.

That gap is the subject of this piece. Wiz AI-SPM and DeepInspect both get named in AI security procurement conversations, and both eventually touch the same AI estate. The overlap stops there. Wiz maps what AI infrastructure exists in your cloud accounts and how it's configured. DeepInspect decides, per request, whether a specific caller is allowed to talk to a specific model right now, and writes down what it decided.

TL;DR

• Wiz AI-SPM discovers and scores AI assets (models, pipelines, training data, vector databases) across cloud accounts on a scanning cadence, using an agentless graph.
• DeepInspect sits inline in front of any LLM, evaluates every request against identity-bound policy before the model sees it, and signs an audit record of the decision.
• Wiz answers whether your AI infrastructure is configured safely. DeepInspect answers whether this specific request, from this caller, was authorized right now.
• Wiz's visibility comes from cloud metadata; live prompt content moving through a request sits outside a configuration graph's reach.
• Regulated teams generally need both: posture scanning for infrastructure gaps, request-level enforcement for control and evidence.

Wiz AI-SPM

Wiz built its name on the Wiz Security Graph, an agentless model that snapshots cloud accounts (AWS, Azure, GCP) and stitches infrastructure, identity, data, and application layers into one queryable map. AI-SPM is the extension of that graph into AI-specific assets: it discovers deployed models, managed AI services, and the pipelines feeding them, without installing an agent on any workload.

The discovery layer is broad by design. Wiz catalogs AI services and SDKs across managed platforms like AWS Bedrock, SageMaker, Azure AI, and Vertex AI, and it builds what the company calls an AI-BOM, an inventory of the models, frameworks, dependencies, and training data tied to each pipeline. Once an asset is on the graph, Wiz runs it through built-in rules that flag misconfiguration: a model endpoint left publicly reachable, a training bucket with looser permissions than the account's baseline, a service account attached to a pipeline that can reach more data than the pipeline needs. Wiz's own research puts real numbers on why this matters, its State of AI in the Cloud report found at least 81% of organizations use managed AI services while 25% still lack visibility into which AI services are running in their environment at all.

From there the graph traces attack paths, connecting an exposed endpoint to the identity that could reach it to the data that identity could touch, and ranks the resulting chains by exploitability. The operating rhythm is scan, catalog, score, and re-scan as the environment changes. That cadence is genuinely useful for the question it answers: is our AI footprint configured the way we think it is, and where's the path an attacker would actually take through it. It is a different question from whether the request a specific caller just sent to that endpoint should have been allowed to happen.

The inline enforcement proxy

DeepInspect is a stateless proxy that sits between authenticated users or agents and any LLM. Every HTTP request to a model API passes through it before the model sees it, and every response passes through it before the caller sees it.

The evaluation happens per request, not on a scanning interval. DeepInspect reads the identity context the calling application supplies, the role or authorization tied to that identity, and the policy decision point that governs the combination, then issues a pass, redact, or block decision before the traffic reaches the model. Because the proxy operates at the HTTP layer rather than inside a cloud provider's control plane, it works the same way in front of Bedrock, Azure OpenAI, Vertex, OpenAI directly, or a self-hosted endpoint. I laid out why that speed matters in 22-Second Breach Windows Mean Your AI Enforcement Must Be Inline: Google Mandiant's M-Trends 2026 report found the median handoff time from initial access to a secondary threat group had collapsed to 22 seconds. A posture scan that runs every few hours, however well it maps the environment, is not built to catch what happens in that window.

Every decision produces a signed, tamper-evident audit record: identity, policy version, data classification, outcome, timestamp. That record commits before the response reaches the calling application, keeping the evidence outside the calling application's own custody, which is the property an auditor actually cares about when they ask you to prove a decision rather than describe it.

Feature comparison

The two products answer different questions about the same AI estate, which is why they rarely compete for the same procurement line.

• What it measures: Wiz measures cloud configuration and exposure risk of AI assets (models, pipelines, data stores). DeepInspect measures request legitimacy (identity, authorization, policy compliance) on live traffic.
• When it acts: Wiz operates on a continuous scanning cadence against cloud account state. DeepInspect evaluates every request in real time, before it reaches the model.
• Discovery mechanism: Wiz uses an agentless security graph to catalog AI assets across cloud accounts automatically. DeepInspect does not discover infrastructure; it enforces policy on traffic that is already routed through it.
• Enforcement capability: Wiz surfaces misconfigurations and attack paths for a team to remediate. DeepInspect makes a pass, redact, or block decision inline, before the request reaches the LLM.
• Visibility into payload: Wiz sees cloud configuration, permissions, and metadata about AI assets. DeepInspect inspects the actual prompt and response content moving through the request.
• Identity awareness: Wiz maps cloud IAM roles and service accounts tied to infrastructure. DeepInspect binds each decision to the authenticated end user or agent identity making the specific call.
• Audit output: Wiz produces risk findings and attack-path reports for a security team's backlog. DeepInspect produces signed, per-decision audit records built for regulatory disclosure.
• Primary buyer: Wiz is typically bought by cloud security or CNAPP teams responsible for infrastructure posture. DeepInspect is typically bought by security, compliance, or platform teams that own access control and audit obligations for live AI traffic.
• Deployment pattern: Wiz connects to cloud accounts via API and requires no agents on workloads. DeepInspect sits in the request path as a proxy, independent of which cloud account or provider is hosting the model.

Pick Wiz AI-SPM if...

Wiz fits teams whose open question is still what AI infrastructure exists and how exposed it is, not who is calling it right now.

• Your priority is discovering every model, pipeline, and vector database running across your cloud accounts, including shadow deployments nobody filed a ticket for.
• You need to catch misconfigured permissions, public endpoints, or over-permissioned service accounts before an attacker finds them first.
• You want attack-path analysis that ties an exposed AI asset back to the identity and data it can reach, using infrastructure you already have Wiz coverage on.
• Your team's open question is "what's out there and is it configured safely," not "who was allowed to send this specific prompt."

Pick DeepInspect if...

DeepInspect fits teams whose question has moved from infrastructure exposure to request-level control and evidence.

• You need per-request, identity-aware enforcement, pass, redact, or block, on live traffic between callers and an LLM, not a periodic scan of cloud state.
• A regulator or auditor can ask "who accessed this data through the model, under what policy, and can you prove it" and you need a real answer with a timestamp attached.
• You are running toward the EU AI Act's Article 12 logging deadline, which took effect August 2, 2026, and your evidence needs to exist outside the calling application's own logs.
• You need enforcement that behaves the same way regardless of which cloud account or provider is hosting the model behind it that week.

Nine times out of ten, nobody planned the combination up front. Wiz gets bought first because a cloud security team needs to know what's running before anything else makes sense. The access-control question shows up later, usually from legal or a customer's security questionnaire, and a posture graph has no field for "prove this one decision." I find that gap mildly annoying given how much attention the AI-SPM category gets in vendor decks, since the two problems were never the same problem to begin with.

DeepInspect

This is the request-level gap Wiz AI-SPM was never built to close. The security graph can tell you a Bedrock endpoint is reachable from a role with broader permissions than it needs. It has no visibility into the prompt that role's application sends five minutes later, and no mechanism to stop that prompt if it happens to carry a customer's SSN toward a model nobody scoped for that data.

DeepInspect sits at the AI request boundary as a model-agnostic proxy, evaluating identity, role, and data classification on every request and producing a signed audit record for every decision it makes. It runs alongside cloud posture tooling rather than replacing it: Wiz answers whether the infrastructure is configured safely, while DeepInspect answers who was allowed to use it and proves the answer months later. The same distinction shows up in Model Guardrails Are Not a Security Control: a well-configured cloud environment lowers the odds of an incident, but proving a specific access decision happened correctly requires a deterministic, externally auditable record at the moment the request occurs.

If you are facing the August deadline, let's talk.