DeepInspect vs Cato Networks (Aim Security): Where the Enforcement Boundary Sits
Cato Networks acquired Aim Security in September 2025 and is folding its AI security capabilities into the Cato SASE Cloud platform through early 2026. This comparison lays out where a SASE-delivered AI security module fits, where a dedicated identity-aware policy gateway with per-decision audit fits, and which buyer each one serves, with honest pick-if framing for both.
In September 2025 Cato Networks announced its first acquisition, buying the enterprise AI security company Aim Security, and said it would integrate Aim's capabilities into the Cato SASE Cloud platform through early 2026. For a team that already runs Cato or is evaluating a SASE consolidation, that puts AI security on the shopping list as a module of the network platform. For a team weighing a dedicated enforcement and audit layer, the question becomes where each approach sits and what each can prove. I want to lay out both honestly, because they solve overlapping problems from different layers, and the right pick depends on what you are optimizing for.
TL;DR
Cato with Aim Security delivers AI security as part of a converged SASE platform, best fit when AI controls should ride your network and access transformation under one vendor. DeepInspect is a dedicated, model-agnostic policy gateway at the AI request boundary that produces a signed per-decision audit record, best fit when you need identity-bound enforcement and audit independent of your network vendor.
Cato Networks with Aim Security: where it sits
Cato is a SASE platform: it converges SD-WAN, a secure web gateway, CASB, DLP, and zero-trust network access into a single cloud-delivered edge. Aim Security, founded in 2022 and backed by YL Ventures and Canaan Partners, built its product across three AI use cases: securing employee use of public AI applications, securing private AI applications and agents, and securing the agentic development lifecycle. Folded into Cato, those capabilities become AI-aware controls delivered from the same edge that already inspects an organization's traffic.
The strength of this model is convergence. If your traffic already flows through Cato's cloud, adding AI controls there means one policy console, one enforcement fabric, and one vendor relationship covering network, access, and AI. For an organization mid-way through a SASE rollout, that is a coherent place to put AI governance.
DeepInspect: where it sits
DeepInspect is not a network platform. It is a stateless proxy that sits specifically at the AI request boundary, between authenticated users or agents and the LLM APIs they call. Its scope is narrow on purpose: evaluate each AI request against identity, role, data classification, model authorization, and policy; return pass, block, or redact before the model sees the traffic; and commit a signed per-decision audit record to a store the calling application does not control.
Because it governs the HTTP call rather than the network path, it is model-agnostic across OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, and self-hosted endpoints, and it deploys without requiring you to adopt a particular SASE or network vendor. Its center of gravity is the per-decision audit record and identity-bound authorization, the action lineage an auditor can inspect.
Feature comparison
On enforcement location: Cato enforces at the SASE edge across all traffic types; DeepInspect enforces at the AI request boundary specifically, after TLS termination, where prompt content and identity are both visible.
On audit: DeepInspect's core output is a signed, identity-bound per-decision record committed independent of the application. A SASE platform's logging is oriented around network and access events; verify what per-AI-request evidence the AI module retains and how independently it is stored.
On model coverage: DeepInspect governs any HTTP-based LLM endpoint regardless of host. A SASE-delivered control governs the AI traffic that traverses its edge.
On deployment: Cato assumes traffic transits its cloud. DeepInspect drops in front of LLM endpoints as a proxy without a network platform commitment.
Pick Cato with Aim Security if
You are consolidating onto SASE and want AI controls delivered from the same edge as your CASB, DLP, and ZTNA. You value single-vendor convergence across network, access, and AI. Your priority is broad coverage of employee AI use routed through your secure edge, and you want the AI module to inherit the policy and reporting model your network team already runs.
Pick DeepInspect if
You need a signed, identity-bound per-decision audit record for every AI call, independent of the application and the network vendor, to satisfy EU AI Act Article 12 or a sector regulator. You run models across multiple providers and want one enforcement policy regardless of host. You want the enforcement and audit layer to be independent of your network platform choice so it survives a future SASE vendor change. For the standalone-product view of the acquired technology, see DeepInspect vs Aim Security.
Pricing approach
Both companies communicate pricing through sales conversations rather than public tiers. Cato is licensed as a platform, typically by bandwidth, users, and the modules enabled, so AI security is priced inside a broader SASE subscription. DeepInspect is scoped to the AI enforcement and audit layer and priced against that footprint. The apples-to-apples question is whether you are buying a converged platform in which AI is one line, or a dedicated enforcement layer you can adopt on its own.
Frequently asked questions
- How is Cato with Aim different from DeepInspect?
Cato delivers AI security as a module of a converged SASE platform enforced at the network edge. DeepInspect is a dedicated proxy at the AI request boundary focused on identity-bound authorization and a signed per-decision audit record, deployable without a SASE platform. One optimizes for network convergence; the other optimizes for independent, model-agnostic enforcement and audit evidence.
- Can I run both?
Yes, and some organizations will. A SASE edge can route and broadly control AI traffic while a dedicated boundary provides the identity-bound per-decision record and deterministic block or redact on the request. They operate at different layers, so running the enforcement-and-audit boundary behind a SASE edge is a reasonable defense-in-depth arrangement.
- Does the Cato acquisition change Aim's roadmap?
Cato stated it would integrate Aim's capabilities into the Cato SASE Cloud through early 2026, with a migration path for existing standalone Aim customers. Buyers evaluating the standalone product should confirm the current integration timeline and which capabilities are generally available inside the platform versus still delivered as the standalone offering.
- Which one satisfies EU AI Act Article 12?
Article 12 requires automatic, per-request recording detailed enough to reconstruct what a high-risk system did. The determining factor is whether the tool produces a per-AI-request record, bound to identity, stored independent of the application that made the call. DeepInspect is built around that record. For a SASE module, confirm the granularity and independence of the AI-request logging before relying on it for Article 12.