How is DeepInspect different from Securiti AI?

Securiti is a data governance and privacy platform built around a knowledge graph. It discovers and classifies data across cloud storage, SaaS apps, and databases on a scan cadence, automates privacy workflows like DSARs on top of that catalog, and extends the graph to AI systems it has registered, scoring their risk against frameworks like the EU AI Act and NIST AI RMF. DeepInspect sits on a different layer: inline on the HTTP path between an authenticated user or agent and an LLM, evaluating each prompt and response against identity-bound policy the moment the request happens, then writing a signed record of the decision. One platform tells you what data and AI systems exist across the organization and how risky each one is. The other tells you, for a specific request at a specific moment, who sent what to which model and whether they were authorized to.

Can Securiti replace DeepInspect?

Securiti's controls are strongest for data and AI systems it has already discovered, classified, or built through Gencore, and that scope is architectural rather than a gap in execution. Its DSPM connectors and knowledge graph operate against repositories and registered AI systems, on a schedule or on write, not against arbitrary live HTTP traffic headed to a model endpoint outside that inventory. An engineering team calling Bedrock or the Anthropic API directly from a service Securiti has never connected to sits outside the Gencore firewall's coverage. Per-request enforcement on traffic like that requires a component physically in the HTTP path, making a permit-or-deny decision before the request reaches the model, something a connector-and-catalog architecture was built around a different job to provide.

Can DeepInspect replace Securiti?

No, and it was never built to. DeepInspect evaluates whether a specific request is authorized and applies policy to the prompt and response; it does not discover unstructured files on a file share, classify a Snowflake table, automate a DSAR workflow, or maintain an enterprise-wide inventory of sensitive data. A privacy team facing a GDPR erasure request still needs a system that knows every repository containing that person's data, a standing catalog problem DeepInspect was never designed to solve. The two address adjacent problems that happen to touch the same underlying data: one builds and maintains the inventory, the other enforces and records what happened to a specific request against a model.

Programs with both a data-governance obligation and a live-AI-traffic obligation typically end up running both, usually not by original design. Securiti, or a comparable governance platform, answers the inventory question: what sensitive data and AI systems exist, where do they sit, and how risky is each one under current regulation. DeepInspect answers the point-in-time question: was this specific request to this specific model authorized under current policy, and can the organization prove that months later during a review. A privacy officer preparing for a DSAR deadline cares most about the first question. A CISO facing an EU AI Act Article 12 audit cares most about the second, and running only one tool leaves a real gap on the other side.

Does Securiti sit inline on live HTTP traffic to LLM providers?

Outside of Gencore-built systems, no. Securiti's core architecture follows a connector-and-scan model: it authenticates to a repository or a SaaS API, pulls a snapshot of what it finds, classifies it, and writes the result to its knowledge graph, a process that runs independent of any live request a user happens to be making to an LLM at that moment. A prompt sent directly to an LLM API travels as an HTTPS call between the calling application and the model provider, terminating at the provider's endpoint in milliseconds. Securiti's scan connectors don't sit on that path unless the request flows through a system built or registered through Gencore. Products built to sit inline on that path for any calling application, DeepInspect among them, work as a proxy the request physically passes through, terminating TLS at the inspection point before the request continues to the model.

How does DeepInspect handle multiple LLM providers if Securiti's firewall is scoped to Gencore?

DeepInspect is model-agnostic by design. The proxy sits in front of any HTTP-based LLM endpoint, which today includes OpenAI, Anthropic, Amazon Bedrock, Azure OpenAI, Google Vertex, and self-hosted models running on infrastructure the enterprise controls. The same identity-aware policy applies regardless of which provider a given team calls, so a security team writes one policy surface instead of maintaining separate configuration per vendor and per build pipeline. Securiti's LLM firewall, by contrast, is strongest for systems assembled through Gencore or explicitly connected to its knowledge graph, so coverage tracks which AI systems have been built or registered inside that pipeline rather than which HTTP endpoints exist across the company. A product team calling Bedrock for one feature and OpenAI for another, outside any Gencore build, gets the same DeepInspect policy applied to both.

What does DeepInspect's audit record contain that a Securiti risk score does not?

A DeepInspect audit record is built around a single AI request: a timestamp, the verified identity of the person or agent behind the call, the role and policy version in effect, the data classification applied to the prompt, and the outcome (permit, redact, or deny), signed to prevent post-hoc modification. A Securiti risk score describes a model or an AI system instead: its cataloged purpose, the data sources feeding it, the regulatory category it falls under, and when the risk assessment last ran. A risk score answers a standing question about how dangerous a registered AI system is in general. An audit record answers a point-in-time question about what a specific request did, carrying a signature the calling application cannot alter after the fact.

Is a "Securiti AI alternative" the same thing as what DeepInspect does?

Not quite, and the distinction matters before a procurement cycle starts. Someone searching for a "Securiti AI alternative" is often looking for one of two things: another data governance and privacy platform doing the same job as Securiti, a genuine like-for-like swap, or an inline enforcement layer that a governance platform was never built to provide. If the gap is data discovery, classification, or privacy automation, the honest comparison is against other governance platforms. If the gap is per-request enforcement on live AI traffic or audit-ready evidence for a regulatory review, that sits in a different layer of the stack, and DeepInspect is built for exactly that layer.

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August 23, 2026

DeepInspect vs Securiti AI: Inline AI Request Enforcement vs Data Governance Platform

Securiti runs as a data governance and privacy platform: discovery, classification, DSAR automation, and AI governance scored against a registered inventory of models and Gencore-built systems. DeepInspect is a model-agnostic proxy that enforces identity-bound policy inline on live LLM traffic, cataloged or not, and produces a signed audit record per request. Teams evaluating a Securiti AI alternative for live enforcement need to know these sit at different layers.

ByParminder Singh· Founder & CEO, DeepInspect Inc.

Comparisons & Alternativesai-governanceai-securitydata-loss-preventionzero-trustinline-enforcementforensic-audit

DeepInspect vs Securiti AI: Inline AI Request Enforcement vs Data Governance Platform

Securiti's DSPM connectors authenticate against Azure, AWS, Salesforce, and Snowflake, then run a classification job across whatever they find, on a schedule most customers set to nightly. That job builds an accurate, enterprise-wide map of where regulated data sits. It does nothing for the finance analyst who pastes next quarter's unannounced revenue figure into a chat window at 6:50am to draft a summary before a board call, because the prompt has already reached the model and come back before the next scan even starts.

DeepInspect and Securiti keep showing up on the same shortlist, usually under an "AI governance" line item, and both touch data that eventually ends up in front of an LLM. The overlap stops there. Securiti's DataAI Command Center tells you what data and AI systems exist across the organization and how risky each one is. DeepInspect answers a narrower question, one request at a time: is this exact caller, right now, authorized to send this exact content to this exact model.

TL;DR

Securiti (the DataAI Command Center) discovers and classifies enterprise data, automates privacy operations, and scores AI risk against a registered inventory of models and Gencore-built systems.
DeepInspect sits inline on live HTTP traffic to any LLM, enforcing identity-bound policy per request, regardless of whether the calling application was ever cataloged.
Securiti answers what data and AI systems exist and how sensitive they are. DeepInspect answers who was allowed to send content to a model just now, and produces proof.
As a Securiti AI alternative for live-traffic enforcement, DeepInspect covers the per-request gap a catalog-and-classify architecture was never built to reach.

Securiti

Securiti markets its platform as the DataAI Command Center: a knowledge graph that captures data and AI assets across an organization and layers governance controls on top. The Data Security Posture Management (DSPM) piece connects to cloud storage, SaaS applications, and structured databases, then runs discovery and classification against whatever it finds. The output is a catalog entry: which repository holds regulated data, who owns it, which law applies.

Privacy automation sits on that same catalog. Data subject access requests and consent workflows pull from the classification the DSPM engine already produced, turning a GDPR erasure request into a workflow legal can run without a manual search across a dozen systems.

The AI governance layer extends the knowledge graph to AI systems themselves: cataloging models across public and private cloud and SaaS, scoring the risk each one carries, and mapping that risk against the EU AI Act and the NIST AI RMF. Securiti's closest approach to inline enforcement is Gencore AI, its platform for building enterprise copilots and agents out of proprietary data. Gencore ships with a context-aware LLM firewall, OWASP-aligned prompt, retrieval, and response controls, and that firewall is scoped to AI systems assembled inside Gencore or otherwise registered in Securiti's knowledge graph. A team calling the OpenAI or Anthropic API directly from its own service code, outside that build pipeline, falls outside the firewall's reach.

The operating cadence, discovery on a schedule, cataloging on write, risk scored against a registered inventory, answers "what data and AI systems exist, and how risky are they." That's a different question from "should this exact request, from this exact caller, reach this exact model right now."

The inline enforcement proxy

DeepInspect is a stateless proxy that sits inline 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, regardless of whether the calling application was built inside a governance platform's own pipeline or wired up independently by an engineering team last Thursday.

The evaluation happens per request, not against a registered inventory. DeepInspect looks at who is asking (the identity context the calling application supplies), what role or authorization they carry, and what policy decision point governs that combination, then makes a pass, redact, or block decision before the traffic reaches the model. The proxy is model-agnostic: it works in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or a self-hosted endpoint, because it operates on the HTTP layer rather than inside any one vendor's build pipeline. This is the same inline enforcement pattern I argued for 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 collapsed to 22 seconds. At that tempo, a decision that depends on a system first being cataloged arrives too late to matter.

Every decision produces a signed, tamper-evident audit record: identity, policy version, data classification, outcome, timestamp. The record commits before the response reaches the calling application, independent of any governance platform's own inventory. That write-path independence is the property a regulator or an internal security review actually wants to see.

Feature comparison

The two products rarely compete for the same procurement line item, because they answer different questions about overlapping traffic. The breakdown below lays out where each one actually operates.

What it covers: Securiti covers enterprise data at rest and the AI systems it has cataloged or built through Gencore. DeepInspect covers live HTTP traffic to any LLM endpoint, cataloged or not.
When it acts: Securiti evaluates on a discovery-and-registration cadence. DeepInspect evaluates every request, before it reaches the model.
Enforcement capability: Securiti's LLM firewall enforces inside Gencore-built or registered AI systems. DeepInspect enforces inline on any request path it sits in front of, regardless of who built the calling application.
Identity awareness: Securiti's controls attach primarily to a dataset, model, or registered AI system. DeepInspect's decisions bind to the authenticated caller's identity and role, per request.
Audit output: Securiti produces a data and AI inventory with risk scores. DeepInspect produces signed, per-decision audit records built for regulatory disclosure.
Build-pipeline dependency: Securiti's strongest AI controls apply to systems built through Gencore or explicitly registered in its knowledge graph. DeepInspect works the same way in front of any LLM endpoint because it sits at the HTTP boundary, outside any single vendor's build pipeline.
Primary buyer: Securiti is typically bought by privacy, data governance, or compliance teams standing up a DSPM and AI-inventory program. DeepInspect is typically bought by security or platform teams that own access control and audit obligations for live AI traffic.
Integration pattern: Securiti integrates through connectors to repositories and SaaS apps, plus its own Gencore build environment. DeepInspect integrates as a proxy in the request path, independent of which team or vendor produced the calling application.
Failure mode on error: Securiti's discovery jobs pick back up on the next scheduled cycle. DeepInspect can fail closed, denying the request by default when policy evaluation is ambiguous or unavailable.

Pick Securiti if...

Securiti fits programs whose open question is still about what data and AI systems exist, not about who is allowed to call a model right now.

Your priority is an enterprise-wide map of where sensitive data lives, across cloud storage, SaaS apps, and structured databases.
You're automating privacy operations, DSAR and consent workflows specifically, on top of a data catalog you already trust.
You're building new copilots or agents from proprietary data and want firewall-style controls built into that pipeline from the start.
You need a registered inventory of AI models and systems, scored against the EU AI Act or NIST AI RMF, for a governance committee or a board report.

Pick DeepInspect if...

DeepInspect fits teams whose question has shifted from what exists to what just happened, and who was allowed to make it happen.

You need per-request, identity-aware enforcement on live AI traffic that never touches a repository Securiti has cataloged, including direct calls against OpenAI, Anthropic, Bedrock, or a self-hosted model.
A regulator or auditor can ask "who accessed this data through the model, under what policy, and can you prove it," and a data inventory alone can't answer that question.
You are running toward the EU AI Act's Article 12 logging deadline, which took effect August 2, 2026, and your evidence needs to come from a source the calling application doesn't control.
Engineering teams are calling model APIs faster than any governance program can register and catalog the resulting AI systems.

Teams that run both rarely planned it that way from day one. The data governance program usually gets funded first, because a privacy regulator or a DSPM audit forces the question. The enforcement gap surfaces later, once someone in security notices that a large share of the LLM traffic in the building never passes through a system Securiti's knowledge graph has ever registered. Watching a well-cataloged Securiti deployment stall in a security review over a question about live, uncataloged API traffic is an avoidable, and slightly frustrating, way to lose a quarter.

DeepInspect

This is the live-traffic enforcement gap a catalog-and-classify architecture was never built to close. Securiti's knowledge graph tells you which data and AI systems it knows about. It has no mechanism to evaluate a request against a model endpoint it has never registered, made by an application its connectors have never touched.

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, independent of whether the calling application was built through a governance platform's own pipeline. It runs alongside data governance tooling rather than replacing it: Securiti answers what data and AI systems exist and how risky they are, DeepInspect answers who was allowed to send this exact request to this exact model, right now, and proves it afterward. The same distinction applies to model-side safety training, which I covered in Model Guardrails Are Not a Security Control: a firewall scoped to systems you built yourself is a different thing from enforcement that covers every request regardless of where it originated.

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

Frequently asked questions

How is DeepInspect different from Securiti AI?: Securiti is a data governance and privacy platform built around a knowledge graph. It discovers and classifies data across cloud storage, SaaS apps, and databases on a scan cadence, automates privacy workflows like DSARs on top of that catalog, and extends the graph to AI systems it has registered, scoring their risk against frameworks like the EU AI Act and NIST AI RMF. DeepInspect sits on a different layer: inline on the HTTP path between an authenticated user or agent and an LLM, evaluating each prompt and response against identity-bound policy the moment the request happens, then writing a signed record of the decision. One platform tells you what data and AI systems exist across the organization and how risky each one is. The other tells you, for a specific request at a specific moment, who sent what to which model and whether they were authorized to.
Can Securiti replace DeepInspect?: Securiti's controls are strongest for data and AI systems it has already discovered, classified, or built through Gencore, and that scope is architectural rather than a gap in execution. Its DSPM connectors and knowledge graph operate against repositories and registered AI systems, on a schedule or on write, not against arbitrary live HTTP traffic headed to a model endpoint outside that inventory. An engineering team calling Bedrock or the Anthropic API directly from a service Securiti has never connected to sits outside the Gencore firewall's coverage. Per-request enforcement on traffic like that requires a component physically in the HTTP path, making a permit-or-deny decision before the request reaches the model, something a connector-and-catalog architecture was built around a different job to provide.
Can DeepInspect replace Securiti?: No, and it was never built to. DeepInspect evaluates whether a specific request is authorized and applies policy to the prompt and response; it does not discover unstructured files on a file share, classify a Snowflake table, automate a DSAR workflow, or maintain an enterprise-wide inventory of sensitive data. A privacy team facing a GDPR erasure request still needs a system that knows every repository containing that person's data, a standing catalog problem DeepInspect was never designed to solve. The two address adjacent problems that happen to touch the same underlying data: one builds and maintains the inventory, the other enforces and records what happened to a specific request against a model.
Do we need both?: Programs with both a data-governance obligation and a live-AI-traffic obligation typically end up running both, usually not by original design. Securiti, or a comparable governance platform, answers the inventory question: what sensitive data and AI systems exist, where do they sit, and how risky is each one under current regulation. DeepInspect answers the point-in-time question: was this specific request to this specific model authorized under current policy, and can the organization prove that months later during a review. A privacy officer preparing for a DSAR deadline cares most about the first question. A CISO facing an EU AI Act Article 12 audit cares most about the second, and running only one tool leaves a real gap on the other side.
Does Securiti sit inline on live HTTP traffic to LLM providers?: Outside of Gencore-built systems, no. Securiti's core architecture follows a connector-and-scan model: it authenticates to a repository or a SaaS API, pulls a snapshot of what it finds, classifies it, and writes the result to its knowledge graph, a process that runs independent of any live request a user happens to be making to an LLM at that moment. A prompt sent directly to an LLM API travels as an HTTPS call between the calling application and the model provider, terminating at the provider's endpoint in milliseconds. Securiti's scan connectors don't sit on that path unless the request flows through a system built or registered through Gencore. Products built to sit inline on that path for any calling application, DeepInspect among them, work as a proxy the request physically passes through, terminating TLS at the inspection point before the request continues to the model.
How does DeepInspect handle multiple LLM providers if Securiti's firewall is scoped to Gencore?: DeepInspect is model-agnostic by design. The proxy sits in front of any HTTP-based LLM endpoint, which today includes OpenAI, Anthropic, Amazon Bedrock, Azure OpenAI, Google Vertex, and self-hosted models running on infrastructure the enterprise controls. The same identity-aware policy applies regardless of which provider a given team calls, so a security team writes one policy surface instead of maintaining separate configuration per vendor and per build pipeline. Securiti's LLM firewall, by contrast, is strongest for systems assembled through Gencore or explicitly connected to its knowledge graph, so coverage tracks which AI systems have been built or registered inside that pipeline rather than which HTTP endpoints exist across the company. A product team calling Bedrock for one feature and OpenAI for another, outside any Gencore build, gets the same DeepInspect policy applied to both.
What does DeepInspect's audit record contain that a Securiti risk score does not?: A DeepInspect audit record is built around a single AI request: a timestamp, the verified identity of the person or agent behind the call, the role and policy version in effect, the data classification applied to the prompt, and the outcome (permit, redact, or deny), signed to prevent post-hoc modification. A Securiti risk score describes a model or an AI system instead: its cataloged purpose, the data sources feeding it, the regulatory category it falls under, and when the risk assessment last ran. A risk score answers a standing question about how dangerous a registered AI system is in general. An audit record answers a point-in-time question about what a specific request did, carrying a signature the calling application cannot alter after the fact.
Is a "Securiti AI alternative" the same thing as what DeepInspect does?: Not quite, and the distinction matters before a procurement cycle starts. Someone searching for a "Securiti AI alternative" is often looking for one of two things: another data governance and privacy platform doing the same job as Securiti, a genuine like-for-like swap, or an inline enforcement layer that a governance platform was never built to provide. If the gap is data discovery, classification, or privacy automation, the honest comparison is against other governance platforms. If the gap is per-request enforcement on live AI traffic or audit-ready evidence for a regulatory review, that sits in a different layer of the stack, and DeepInspect is built for exactly that layer.

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