What is the best Lasso Security alternative?

It depends what you are replacing. If you want shadow-AI discovery and content threat detection across the GenAI lifecycle, Lasso is strong and the closest alternatives are other detection-first platforms. If what you actually need is a control that binds each HTTP AI call to a named identity, applies a per-role policy inline, and produces a signed audit record a regulator accepts, you are not looking for another content scanner. You are looking for an identity-aware policy and audit layer, which is a different architectural position.

Does Lasso Security detect prompt injection?

Yes, and it is a core strength. Lasso's content engine scans prompts and responses in real time for prompt injection, jailbreaks, and sensitive-data leakage, with token masking to remove secrets before they reach a model, and it reports fast classification with high accuracy. DeepInspect treats injection signals as one input to a policy decision rather than the decision itself. The authorization question, whether this person may send this data to this model, stands regardless of whether the specific prompt trips an injection classifier.

How does Lasso Security handle MCP?

Lasso ships an open-source MCP gateway that orchestrates multiple MCP servers, runs tool reputation analysis to judge server trustworthiness, and applies configurable filters to requests and responses. It is aimed at securing agentic tool use. DeepInspect governs MCP traffic that flows over HTTP, binding tool calls to an identity and authorizing each one, and it is explicit that a local stdio MCP server running as a child process sits outside the reach of a network proxy.

How is DeepInspect different from Lasso Security?

Lasso asks what is in this content and where unsanctioned AI is being used. DeepInspect asks whether this authenticated person, on this route, may send this class of data to this model right now, and what record proves it. Lasso spans discovery, testing, and runtime content defense across a wide surface. DeepInspect holds a narrow line, HTTP AI traffic, and enforces identity-bound authorization with a signed audit record on it. Different primitives, and in a regulated deployment they compose.

DeepInspect vs Lasso Security: Content Threat Detection and Identity-Bound Authorization

Lasso Security is a GenAI security platform, a 2024 Gartner Cool Vendor for AI Security, built around a five-stage framework: discover shadow AI, assess exposure, test with red teaming, enforce policy, and protect at runtime. It scans GenAI interactions for prompt injection and sensitive-data leakage, masks tokens, and ships an open-source MCP gateway that orchestrates Model Context Protocol servers and applies request and response filters. Lasso reports sub-50ms classification and high detection accuracy on its content controls.

Lasso and DeepInspect overlap in the sentence "control how AI is used." They separate on the primitive each one enforces. Lasso classifies content and detects threats. DeepInspect authorizes a named identity and signs the decision.

TL;DR

Lasso is a detection-first GenAI security platform: it finds shadow AI, scans prompts and responses for injection and data exposure, masks sensitive tokens, and secures MCP tool use. DeepInspect is an identity-bound policy and audit layer at the AI request boundary: every HTTP call to an LLM evaluated against the natural-person identity, the data classification, and the active policy, with a signed per-decision record. Lasso is strong where the question is what is in this content. DeepInspect is strong where the question is who is authorized and what proves it. For a regulated deployment, the two address different halves of the same problem.

Lasso Security: what it is and where it sits

Lasso covers a wide surface. Its discovery stage surfaces unsanctioned AI tool use across the organization, which reaches into browser activity and endpoint behavior. Its content controls scan prompts and responses in real time for prompt injection, jailbreaks, and sensitive-data leakage, with token masking to strip secrets before they reach a model. Its MCP gateway is plugin-based: it orchestrates multiple MCP servers, runs tool reputation analysis on them, and applies configurable filters to requests and responses.

That breadth is the point of the platform. Lasso positions itself across the full GenAI lifecycle, from finding shadow usage through runtime defense. Several of those capabilities sit outside the boundary a network proxy between users and LLMs can enforce. Browser-level shadow-AI discovery is an endpoint and network-visibility problem. Automated red teaming is a pre-production model-testing activity. MCP tool reputation analysis is a supply-chain judgment about a server, not an authorization decision about a request. These are real security functions, and they are a different job than authorizing HTTP AI traffic.

What DeepInspect is and where it sits

DeepInspect is a stateless proxy at the AI request boundary between authenticated users or agents and any HTTP LLM endpoint. It evaluates identity-bound policy per request, classifies prompt content against regulated data types, and commits a per-decision audit record with cryptographic integrity. Deterministic, fail-closed, model-agnostic.

Where the two separate

Detection versus authorization. Lasso's content engine answers whether a prompt or response contains something dangerous: an injection pattern, a secret, a leak. DeepInspect answers whether this authenticated person, in this role, is permitted to send this class of data to this model at all. A content scanner can pass a clean-looking prompt that a policy should still block because of who is sending it and what data class it carries. Authorization is a decision about the principal, and detection is a judgment about the payload.

What the record proves. Lasso provides monitoring dashboards and threat visibility across GenAI interactions. DeepInspect commits a signed, identity-bound record for each decision, on a write path the calling application never controls, before the response returns. A dashboard shows what was observed. An audit record proves who was authorized under which policy version, which is the artifact a regulator inspects.

Scope of the enforcement point. Lasso's coverage spans discovery, testing, and MCP orchestration. DeepInspect claims a narrower line and holds it: HTTP AI traffic between authenticated users or agents and LLMs. Everything DeepInspect enforces flows through that path, which is why the guarantee is a chokepoint rather than a set of layered detections.

Feature comparison

| Capability | Lasso Security | DeepInspect | |---|---|---| | Shadow AI discovery | Yes, across endpoint and browser | Out of scope, governs traffic in path | | Prompt injection and jailbreak detection | Yes, content engine | As one policy input | | Sensitive-data and secrets scanning | Yes, with token masking | Prompt-level classification | | Automated red teaming | Yes | Out of scope | | MCP tool governance | Yes, gateway plus reputation analysis | Governs MCP over HTTP | | Identity of the caller | Monitored per interaction | Natural person from the identity provider | | Per-role or per-route policy | Policy enforcement stage | Yes, architectural | | PHI and MNPI classification | Sensitive-data detection | Named taxonomy | | Signed per-decision audit record | Monitoring and dashboards | Yes, tamper-evident, identity-bound | | Write-path independence from the app | Not documented | Yes | | Enforcement is a non-bypassable chokepoint | Depends on deployment surface | Yes, at the request boundary |

Pick Lasso Security if

Pick Lasso if the pressing need is visibility into shadow AI across the workforce, because discovery is a first-class stage of the platform. Pick it if content threat detection is the priority, since prompt injection scanning, jailbreak detection, and token masking are its core strengths. Pick it if you are standing up MCP-based agents and want tool reputation analysis and request-response filtering on that traffic. Pick it if you want one platform spanning discovery, testing, and runtime content defense rather than a single enforcement point.

Pick DeepInspect if

Pick DeepInspect if the requirement is per-request authorization bound to a named person, decided inline before the call reaches the model. Pick it if the policy has to turn on the data classification in the prompt, including PHI or material non-public information. Pick it if an auditor will ask for a record binding a decision to an identity, a policy version, and a timestamp, on a write path the application cannot alter. Pick it if what you need is a non-bypassable chokepoint for HTTP AI traffic rather than a set of detections spread across the lifecycle.

Composition in production

Detection and authorization compose. Lasso can find shadow AI, scan content for injection and leakage, and secure MCP tool use. DeepInspect can bind each sanctioned HTTP call to a natural-person identity, apply the per-role policy, and commit the signed record. One tells you what is in the traffic and where unsanctioned traffic is hiding. The other decides whether a sanctioned call is authorized and produces the evidence. A regulated deployment that runs both gets threat visibility and identity-bound accountability without asking either tool to do the other's job.

Pricing approach

Lasso's MCP gateway is open source and free to run, and the broader GenAI security platform is a commercial product priced through sales, with no public price list. DeepInspect is priced through a sales conversation and deploys self-hosted or managed. Both companies scope pricing to the deployment, so a real comparison comes from a conversation about traffic volume, data sensitivity, and coverage.

DeepInspect

This is the gap DeepInspect closes. Lasso detects what is in the content. DeepInspect decides whether the person sending it is authorized, and it produces the record that proves the decision.

Every request is evaluated against the natural-person identity the application supplies, the role that person holds, the classification of the data in the prompt, and the policy version active at that moment. The decision is deterministic and fail-closed, and it happens inline before the request reaches the model. Every decision produces a signed, identity-bound audit record, committed before the response returns, on a write path the application never controls. That property is what separates a monitoring dashboard from audit-ready evidence: the system that made the AI call has no ability to alter the record describing it, which is what an EU AI Act Article 12 reviewer or a HIPAA auditor is testing. If you are evaluating a Lasso deployment and still need identity-bound authorization and signed evidence on your AI traffic, let's talk today.