DeepInspect vs Aim Security: How the Two Architectures Differ at the AI Request Boundary

DeepInspect and Aim Security both address enterprise AI security and frequently appear in the same procurement evaluation. The two operate on different architectural patterns and produce different operational artifacts. DeepInspect is a stateless policy-enforcement proxy that sits inline on the HTTP path between calling identities and LLM endpoints, committing a per-decision audit record at the request boundary. Aim Security operates as a security platform that combines discovery, posture management, and runtime controls across the enterprise AI footprint. Both are credible options. The choice depends on whether the binding requirement in the deployment is the contemporaneous regulatory record at the request boundary or a broader AI security posture across the discovery, governance, and response surfaces.

I want to walk through what each product is, where each one sits in the architecture, what audit record each one commits, and the buyer-fit framing for picking one over the other.

TL;DR

DeepInspect is an inline policy-enforcement proxy for AI traffic that produces the per-decision audit record EU AI Act Article 12 reviewers accept. Aim Security is an AI security platform with broader discovery and posture-management features across the enterprise AI footprint. Buyers facing the August 2, 2026 EU AI Act high-risk deadline as the binding regulatory event typically need DeepInspect's per-decision record. Buyers looking for an end-to-end AI security platform with discovery, posture, and runtime controls may evaluate Aim Security.

Aim Security: what it is and where it sits

Aim Security has positioned itself as an AI security platform with three primary capability areas: discovery of AI assets and usage across the enterprise, security posture management for those assets, and runtime controls applied through agent or proxy integrations. The platform addresses the breadth of the AI security surface, from sanctioned tool inventory to model risk assessment to runtime policy.

The discovery surface uses agent-based and API-based methods to identify AI usage across managed devices, network egress, and SaaS integrations. The posture-management surface scores the security state of each identified asset and surfaces gaps. The runtime control surface applies policy at the points the platform integrates: typically at the application SDK, the browser extension, or the network edge.

Aim Security raised a $50M Series B in late 2025 and counts large-enterprise customers in its public reference set. The product is well-funded and frequently appears in CISO procurement shortlists for AI security platform RFPs.

DeepInspect: what it is and where it sits

DeepInspect is a stateless policy-enforcement proxy that sits inline on the HTTP path between authenticated users or agents and any LLM endpoint. The proxy terminates the AI provider TLS, reads the request body, attaches the enterprise IdP identity through propagation, evaluates per-route, per-role, identity-bound policy against the prompt classification, applies a pass, redact, or block decision, commits the per-decision audit record, and forwards the request to the model.

The product is focused on the AI request boundary. DeepInspect does not discover AI usage across the enterprise as a primary feature; the inspection telemetry produces a continuously updated inventory of what flows through the proxy, but the discovery surface is narrower than a discovery-first platform. DeepInspect's defining property is the per-decision record: identity, role, classification, policy state, model, decision outcome, and cryptographic signature, committed independently of the application and the LLM provider.

Feature comparison

| Capability | DeepInspect | Aim Security | |---|---|---| | Inline policy enforcement at the AI request boundary | Core | Available through integrations | | Per-decision audit record with cryptographic signature | Core | Varies by integration mode | | Identity propagation from enterprise IdP | Core | Available | | Prompt-level classification at request time | Core | Available | | Fail-closed under uncertainty | Default posture | Configurable | | Discovery of AI usage across the enterprise | Derived from proxy telemetry | Core, agent and API-based | | AI asset posture management | Limited | Core | | Vendor and tool risk scoring | Not in scope | Core | | Model risk assessment | Not in scope | Available | | Multi-vendor model routing | Available | Available through integrations | | Deployment topology | SDK, network-edge, sidecar | Agent, browser, integration |

Pick Aim Security if

The buying decision favors Aim Security when the procurement scope spans the full AI security platform need, not only the request-boundary enforcement requirement.

The buyer wants a single platform that covers AI asset discovery, posture management, vendor risk, and runtime controls. The buyer accepts that the per-decision record at the request boundary may sit at a different level of granularity than what a regulatory auditor under EU AI Act Article 12 or DORA Article 19 would expect, or the buyer has other architecture providing that record.

The buyer prioritizes inventory and posture management for the entire AI footprint, including AI used inside vendor SaaS embeds, and the inspection-layer record is a secondary concern.

Pick DeepInspect if

The buying decision favors DeepInspect when the binding requirement is the per-decision audit record at the AI request boundary.

The buyer is preparing for the August 2, 2026 EU AI Act high-risk effective date or the August 6, 2026 Fannie Mae LL-2026-04 effective date, and the regulatory record at the request boundary is the failure mode that worries internal audit. The buyer has identified the inline policy-enforcement gateway as the architectural component the conformity file presumes exists.

The buyer wants identity-bound enforcement that fails closed under uncertainty and produces a cryptographically signed per-decision record. The buyer values architectural depth at the AI request boundary over breadth across the full AI security surface and is comfortable composing complementary tools for discovery or posture management.

The buyer operates in a regulated environment (healthcare, finance, government, insurance) where the EU AI Act, DORA, HIPAA, Fannie Mae, or NIST AI agent framework requirements are the primary procurement drivers.

Pricing approach

DeepInspect prices through sales conversations based on AI request volume and deployment topology, with multi-year terms typical for regulated buyers. Aim Security prices through annual platform contracts based on AI asset count, user count, and module selection. Both are enterprise-priced; neither publishes price-list tiers on their website. A direct comparison should include both the platform fee and the integration cost for the specific deployment topology.