Is Prompt Security a competitor to DeepInspect?

Prompt Security and DeepInspect overlap on prompt-level inspection and inline enforcement and diverge on identity model, audit posture, and target regime alignment. Some buyers choose one. Some buyers run both for different surfaces of their AI use. The framing of "competitor" depends on which buying motion drives the decision. A team buying for shadow AI discovery across SaaS will lean toward Prompt Security. A team buying for EU AI Act Article 12 logging on developer-built AI features will lean toward DeepInspect.

How does Prompt Security handle Article 12 logging?

Prompt Security's logging captures the prompts, the responses, and the policy decisions the product makes. The logs are useful for security investigations and policy tuning. They are not signed or built to function as tamper-evident per-decision audit records under EU AI Act Article 12. Organizations subject to Article 12 obligations should expect to add an audit layer that produces the record format the regulator expects. DeepInspect's per-decision signed record is designed for that obligation directly.

Can Prompt Security enforce per-role policies?

Prompt Security supports policies that vary by user and group. The implementation runs against the prompt content and the user attribution rather than against per-role permissions at the decision layer. Buyers who need fine-grained per-route and per-role enforcement tied to an identity model managed in their IdP should evaluate the depth of the identity binding before assuming Prompt Security covers the full requirement.

What architecture should an organization run on top of Prompt Security to satisfy Article 12?

The architecture that satisfies Article 12 is an enforcement and audit layer that records per-decision evidence with identity context, policy version, data classification, and tamper-evident signatures, under retention rules the deploying organization controls. That layer can sit in front of, behind, or alongside Prompt Security depending on the traffic shape. The two products operate on the same HTTP AI traffic and produce records at different levels of granularity. A deploying organization is responsible for the architectural choice that produces a record the regulator accepts.

Do Prompt Security and DeepInspect both support self-hosted LLMs?

DeepInspect is model-agnostic and supports any HTTP-based LLM endpoint, including self-hosted Llama, Mistral, and on-prem inference deployments. Prompt Security supports a broad catalog of LLM providers and SaaS surfaces, with self-hosted support that is narrower than the SaaS coverage. Buyers running self-hosted inference should confirm the specific endpoints with each vendor.

DeepInspect vs Prompt Security: Architecture, Audit, and Buyer Fit

Prompt Security operates as a security layer for generative AI use across the enterprise. The product family covers a browser extension and proxy for shadow AI visibility on SaaS surfaces, prompt-level inspection for sanctioned AI applications, and an API-level enforcement layer for developer-built AI features. DeepInspect operates as a stateless proxy between authenticated users or agents and any LLM endpoint, enforcing identity-bound policy on every request and producing per-decision audit records that the deploying organization controls.

Both products sit in the AI traffic path. The buyer fit differs on the regulatory pressure the deployment faces.

I want to walk through what each product does at the architecture layer, where the two enforcement models converge, and where the difference shows up under a regulator's questions.

TL;DR

Prompt Security is a generative AI security suite covering shadow AI discovery, prompt-level threat inspection, and inline protection across SaaS and API surfaces. DeepInspect is a stateless proxy and audit layer for identity-bound policy enforcement and per-decision audit evidence in regulated environments, with explicit alignment to the EU AI Act, NIST AI RMF, ISO 42001, and sector-specific regimes.

Prompt Security: where it sits

Prompt Security's deployment surfaces include a browser extension for end users, a proxy that sits in front of SaaS AI tools to enforce policy on the way to those tools, and an API-side product for AI features developers ship inside their own applications. The product is positioned around generative AI security across the enterprise surface area.

The enforcement model is prompt-level inspection. The product analyzes prompts and responses for sensitive data, jailbreak attempts, prompt injection patterns, and policy violations defined by the deploying organization. The enforcement happens against the content of the request rather than against the identity behind it; identity is used as a routing or attribution field but is not the primary axis of policy decisions.

Prompt Security supports a wide catalogue of SaaS AI tools out of the box, including ChatGPT, Claude, Gemini, Copilot, and many others, and is built to handle the discovery side of shadow AI in addition to the enforcement side. The browser extension and the proxy work together to capture sanctioned and unsanctioned AI use across the user base.

Where DeepInspect sits

DeepInspect operates between authenticated users or agents and the LLM endpoints they call. The enforcement model is per-request policy evaluation against identity context that the application supplies, with per-route and per-role policies. Prompt-level classification runs at the same point, and the decision is permit, redact, or deny.

Every decision produces a per-decision audit record containing the identity, role, policy version, data classification, decision outcome, and timestamp. The record is signed, tamper-evident, and committed before the model response returns to the application. The retention windows are governed by the deploying organization, which is necessary for compliance regimes that require six months, three years, or longer.

DeepInspect is model-agnostic. It works in front of any HTTP-based LLM endpoint, including OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex AI, self-hosted Llama, self-hosted Mistral, and on-prem inference deployments.

Feature comparison

The catalog of SaaS surfaces Prompt Security covers is broader. The audit trail DeepInspect produces is built for the regulator's specific question.

Pick Prompt Security if

The buyer profile fits Prompt Security in several cases. The primary pain is shadow AI visibility across the workforce using a heterogeneous set of SaaS AI tools through browsers. The deployment needs a browser extension surface to enforce policy on traffic the user generates from their own machine. The catalogue of supported SaaS AI surfaces matters more than identity-bound audit evidence, and the organization does not face EU AI Act Article 12 logging obligations or sector-specific audit requirements that demand per-decision tamper-evident records. The team values discovery and inspection across many tools.

Pick DeepInspect if

The buyer profile fits DeepInspect when the deployment carries compliance obligations. The system falls under EU AI Act high-risk classification and needs Article 12 logging and Article 14 oversight evidence. The organization operates in healthcare, financial services, government, mortgage origination, or another sector with audit obligations. The architecture has to support identity-bound policy because the AI traffic carries authenticated user and agent contexts that have to land in the audit trail. The retention windows run beyond what an operational policy product provides. The deploying organization needs the audit trail to be tamper-evident and controlled by the organization, not the vendor.

Pricing approach

Prompt Security is sold through enterprise contracts with pricing tied to the deployment surface area and the user base. The product family is sold as a suite covering discovery, browser-side enforcement, and API-side enforcement. List pricing is not published.

DeepInspect is sold through enterprise contracts with pricing tied to the deployment scope, the number of authenticated identities, the policy complexity, and the retention windows. Pricing conversations include the compliance regimes in scope and the audit posture required. DeepInspect pricing is not published as a list price because the deployment shapes the cost.

Where DeepInspect and Prompt Security can coexist

A large enterprise might use Prompt Security's browser extension for shadow AI discovery and DeepInspect for the identity-bound policy enforcement layer on the developer-built AI features inside its own applications. The two products record at different layers and answer different questions. The browser-side product answers "what AI traffic is leaving the workstation and what tool is it going to." The enforcement layer answers "for the AI features we built, who made this specific decision, under which policy, against what data classification, and what was the outcome."

For deployments that need both answers, the architectures stack.

DeepInspect

This is the architectural pattern DeepInspect was built around. DeepInspect sits at the AI request boundary as an external enforcement layer that produces identity-bound, per-decision audit records, deterministic policy enforcement, and tamper-evident evidence under the deploying organization's control.

If your deployment falls under the EU AI Act high-risk obligations or under a sector-specific regime that asks for per-decision evidence, the August 2, 2026 effective date for the high-risk requirements is close. Book a technical deep dive at deepinspect.ai.