How is DeepInspect different from BigID?

BigID discovers and classifies data across repositories on a scheduled scan: databases, file shares, cloud storage, SaaS apps. Its output is a catalog of where sensitive data lives, who owns it, and which regulation applies, which is what privacy compliance and DSPM programs run on. DeepInspect operates on a different layer. It doesn't scan stored data at all. It sits inline on the HTTP path between an authenticated user or agent and an LLM, evaluating each prompt and response as it happens against identity-bound policy, then writing a signed record of that decision. One tool tells you where your sensitive data lives across the organization. The other tells you, for a specific request at a specific moment, who sent what and whether they were authorized to. Programs with both a privacy compliance obligation and a live-traffic obligation typically end up running both tools, because the two records answer different audit questions.

Can BigID's shadow AI features replace live AI traffic enforcement?

BigID's shadow AI discovery operates one step removed from live enforcement, and that gap is architectural rather than a product shortcoming. It cross-references its existing data catalog against signs that a cataloged dataset reached an AI tool, usually through browser history, SaaS activity logs, or a vendor's conversation records. That process runs after the fact, on a schedule, against data BigID already knows about. It cannot intercept a prompt as a user types it, and it cannot evaluate whether this specific authenticated user was permitted to send this specific content to this specific model right now. Live enforcement requires a component that sits inside the HTTP path itself, ahead of the model, making a permit-or-deny decision before the request completes. BigID's scanning architecture operates one layer removed from that path by design, built around its core cataloging job.

Do we need both BigID and DeepInspect, or does one replace the other?

Regulated programs typically end up running both, because the two products answer different audit questions. BigID produces the inventory a GDPR or CCPA data subject access request needs: what personal data exists, where it sits, and under what legal basis it's processed. DeepInspect produces the per-decision record a regulation like EU AI Act Article 12 needs: what a specific AI system did with a specific request from a specific identified person at a specific moment. A regulator asking "where does our customer data live" and a regulator asking "who authorized this AI decision and under what policy" are asking two separate questions. One tool's evidence doesn't substitute for the other's, and buying more of one doesn't shrink the gap the other one covers. A mortgage lender working through Fannie Mae's AI governance letter, for example, needs BigID-style discovery to map where borrower data sits across legacy systems and needs a DeepInspect-style record to show a regulator what a specific underwriting prompt did.

Does BigID sit inline on HTTP traffic to LLM providers?

BigID's architecture follows a connector-and-scan model: it authenticates to a repository, a database, or a SaaS API, pulls a snapshot of what's there, classifies it, and writes the result to its catalog. That process happens independent of any live request a user is making to an LLM at that moment. A prompt sent to an LLM API travels as an HTTPS POST directly between the calling application and the model provider, terminating at the provider's endpoint in milliseconds. BigID's scan connectors don't sit on that path, so a specific prompt in flight is invisible to a BigID deployment unless the data it contains later surfaces in a repository BigID already monitors. Products built to sit inline on that HTTP path, 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 BigID does not cover this at all?

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 or application calls, so a security team writes one policy surface instead of maintaining separate configuration per vendor. A product team calling Bedrock for one feature and OpenAI for another gets evaluated against the same policy at the same proxy, with a single audit trail spanning both. BigID's product was not built to intercept live model traffic for any provider, so a provider-by-provider comparison doesn't apply here the way it does for DeepInspect. Its relevant surface is the repositories and SaaS connectors it supports, upstream of any model call.

What does DeepInspect's audit record contain that a BigID catalog entry 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. Article 19 of the EU AI Act specifically calls for identifying the natural persons involved in a high-risk AI decision, which a shared service credential or an API key alone cannot do. A BigID catalog entry describes a dataset or a repository: its location, its classification, its owner, the regulation it falls under, and when the classification job last ran against it. Neither record substitutes for the other. A catalog entry answers a standing question about where data lives; an audit record answers a point-in-time question about what a specific request did, and it does so with a signature the application generating the request cannot alter after the fact.

← Blog

August 22, 2026

DeepInspect vs BigID: Live AI Traffic Enforcement Versus Data-at-Rest Discovery

BigID discovers and classifies sensitive data across structured and unstructured repositories, cloud storage, and SaaS applications on a scheduled scan, supporting GDPR and CCPA compliance and data security posture management. DeepInspect is a stateless, identity-aware proxy that inspects live HTTP traffic between authenticated users or agents and any LLM, enforcing policy per request and producing signed audit records. This piece compares the two architectures.

ByParminder Singh· Founder & CEO, DeepInspect Inc.

Comparisons & Alternativesdata-loss-preventiondlpai-governanceai-securityzero-trustcompliance

DeepInspect vs BigID: Live AI Traffic Enforcement Versus Data-at-Rest Discovery

BigID's connectors sync against Snowflake, S3, SharePoint, and roughly a dozen other repository types on a scan schedule most customers set to nightly or weekly. That cadence is fine for a data catalog. It does nothing for a support engineer who pastes a customer's card number into a chat window at 11:40pm to draft a refund email, because the prompt is already gone by the time the next scan runs. DeepInspect and BigID land on the same shortlist constantly, and the two products are built to answer different questions.

TL;DR

BigID scans data at rest on a schedule, cataloging where sensitive data lives across repositories, cloud storage, and SaaS apps for privacy compliance and DSPM.
DeepInspect sits inline on live HTTP traffic between users and any LLM, evaluating and recording every prompt and response against identity-bound policy in real time.
BigID answers where your data lives. DeepInspect answers who is allowed to send what to a model right now, and can prove the decision afterward.
As a BigID alternative for live traffic control, DeepInspect adds the per-request layer BigID's scan architecture was never built to provide.

BigID

BigID is a data intelligence platform organized around discovery and classification. It connects to structured databases, unstructured file shares, cloud storage buckets, and SaaS applications such as Salesforce and Workday, then runs classification jobs against whatever it finds. The output is a catalog: this repository holds this category of data, subject to GDPR or CCPA, owned by this business unit. That catalog underwrites data subject access requests, retention policy enforcement, and data security posture management (DSPM) scoring.

BigID has extended the same scanning engine toward AI governance, adding features that flag when a dataset it already catalogs shows signs of having reached a shadow AI tool, typically by cross-referencing browser activity, SaaS logs, or a vendor's stored conversation history. That's genuinely useful for building a risk picture after something has already happened. IBM's Cost of Data Breach Report puts the average detection window for shadow-AI-linked breaches at 247 days, a gap a periodic scan is well positioned to eventually close and poorly positioned to prevent in the moment. The product reads repositories and logs. It does not sit on the wire watching a prompt as it leaves a browser tab or an API client.

DeepInspect

DeepInspect answers a narrower, later question: for this specific request, right now, who is asking, and what is that person authorized to send or receive. It runs as a stateless proxy in the HTTP path between authenticated users or agents and any LLM endpoint, OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or a self-hosted model. Every request carries the identity and role context the calling application supplies. DeepInspect evaluates that request against per-route and per-role policy, classifies the prompt content, and permits, redacts, or blocks it before the model ever receives it.

The decision becomes evidence in the same motion. Each one is written to a signed, tamper-evident audit record before the response returns to the calling application, so the record exists independent of whatever the application chooses to log on its own. That record carries identity, policy version, classification, and outcome, the fields a regulator or a customer's security team asks for under EU AI Act Article 12 when they want to know what an AI system did with a specific request. I walked through what Article 12 actually requires in more depth separately.

I think a lot of procurement teams treat a completed DSPM rollout as proof of an AI security program. That's a category error: BigID answers where your data lives. What happened in last Tuesday's prompt to a production model is a separate question, one its scan architecture was never built to answer. Traditional DLP has the same blind spot: it inspects file transfers and email, and an HTTPS POST to a model API travels underneath that visibility entirely. If your BigID deployment can't produce that second answer, that's the gap DeepInspect closes.

Book a demo today.

Feature comparison

The two products diverge on almost every axis except the fact that both touch sensitive data eventually.

What it inspects. BigID inspects data at rest inside repositories: databases, file shares, cloud storage, SaaS content stores. DeepInspect inspects HTTP AI traffic, the prompt going out and the response coming back, at the moment the request happens.
When the check happens. BigID runs on a scan schedule, typically nightly or weekly depending on the connector and repository size. DeepInspect evaluates every request inline, before it reaches the model.
What it produces. BigID produces a data catalog: location, classification, ownership, applicable regulation. DeepInspect produces a per-decision audit record: identity, policy version, classification, and outcome, signed and tamper-evident.
Identity binding. BigID's classification attaches to a repository or a dataset. DeepInspect's binds to the authenticated identity and role behind a specific live request.
AI-tool visibility. BigID's AI-related features focus on discovering where cataloged data has already reached an AI tool. DeepInspect operates model-agnostically in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, and self-hosted endpoints, enforcing one policy across all of them.
Regulatory fit. BigID's catalog underwrites GDPR and CCPA data subject access requests and DSPM scoring. DeepInspect's per-decision record is built for regimes that require traceability at the moment of an AI decision, including EU AI Act Article 12.

Pick BigID if...

BigID is the right call when the open question is still about where data lives, not what a live prompt just sent.

Your primary requirement is an enterprise-wide inventory of where sensitive data lives, across structured databases, file shares, and SaaS apps, most of which predate any AI initiative.
You're building privacy compliance workflows, GDPR and CCPA data subject access requests specifically, and need a working data map to answer them.
Data-at-rest governance, retention policy, and DSPM scoring are the program's current priority.
You want a backward-looking view of where cataloged data has already reached an AI tool, for an incident review or a risk assessment.

Pick DeepInspect if...

You need to evaluate and act on a specific prompt or response as it happens, before the next scheduled scan would even surface it.
Your audit obligation requires an identity-bound, per-decision record, the granularity EU AI Act Article 12 or a customer's security review asks for.
You need to redact or block PII in a prompt before it reaches the model, rather than discover afterward that it was sent.
Your environment spans more than one LLM provider and you want a single policy layer instead of a separate control per vendor.

For a broader look at the category beyond BigID specifically, see the BigID alternatives roundup.

Frequently asked questions

How is DeepInspect different from BigID?: BigID discovers and classifies data across repositories on a scheduled scan: databases, file shares, cloud storage, SaaS apps. Its output is a catalog of where sensitive data lives, who owns it, and which regulation applies, which is what privacy compliance and DSPM programs run on. DeepInspect operates on a different layer. It doesn't scan stored data at all. It sits inline on the HTTP path between an authenticated user or agent and an LLM, evaluating each prompt and response as it happens against identity-bound policy, then writing a signed record of that decision. One tool tells you where your sensitive data lives across the organization. The other tells you, for a specific request at a specific moment, who sent what and whether they were authorized to. Programs with both a privacy compliance obligation and a live-traffic obligation typically end up running both tools, because the two records answer different audit questions.
Can BigID's shadow AI features replace live AI traffic enforcement?: BigID's shadow AI discovery operates one step removed from live enforcement, and that gap is architectural rather than a product shortcoming. It cross-references its existing data catalog against signs that a cataloged dataset reached an AI tool, usually through browser history, SaaS activity logs, or a vendor's conversation records. That process runs after the fact, on a schedule, against data BigID already knows about. It cannot intercept a prompt as a user types it, and it cannot evaluate whether this specific authenticated user was permitted to send this specific content to this specific model right now. Live enforcement requires a component that sits inside the HTTP path itself, ahead of the model, making a permit-or-deny decision before the request completes. BigID's scanning architecture operates one layer removed from that path by design, built around its core cataloging job.
Do we need both BigID and DeepInspect, or does one replace the other?: Regulated programs typically end up running both, because the two products answer different audit questions. BigID produces the inventory a GDPR or CCPA data subject access request needs: what personal data exists, where it sits, and under what legal basis it's processed. DeepInspect produces the per-decision record a regulation like EU AI Act Article 12 needs: what a specific AI system did with a specific request from a specific identified person at a specific moment. A regulator asking "where does our customer data live" and a regulator asking "who authorized this AI decision and under what policy" are asking two separate questions. One tool's evidence doesn't substitute for the other's, and buying more of one doesn't shrink the gap the other one covers. A mortgage lender working through Fannie Mae's AI governance letter, for example, needs BigID-style discovery to map where borrower data sits across legacy systems and needs a DeepInspect-style record to show a regulator what a specific underwriting prompt did.
Does BigID sit inline on HTTP traffic to LLM providers?: BigID's architecture follows a connector-and-scan model: it authenticates to a repository, a database, or a SaaS API, pulls a snapshot of what's there, classifies it, and writes the result to its catalog. That process happens independent of any live request a user is making to an LLM at that moment. A prompt sent to an LLM API travels as an HTTPS POST directly between the calling application and the model provider, terminating at the provider's endpoint in milliseconds. BigID's scan connectors don't sit on that path, so a specific prompt in flight is invisible to a BigID deployment unless the data it contains later surfaces in a repository BigID already monitors. Products built to sit inline on that HTTP path, 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 BigID does not cover this at all?: 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 or application calls, so a security team writes one policy surface instead of maintaining separate configuration per vendor. A product team calling Bedrock for one feature and OpenAI for another gets evaluated against the same policy at the same proxy, with a single audit trail spanning both. BigID's product was not built to intercept live model traffic for any provider, so a provider-by-provider comparison doesn't apply here the way it does for DeepInspect. Its relevant surface is the repositories and SaaS connectors it supports, upstream of any model call.
What does DeepInspect's audit record contain that a BigID catalog entry 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. Article 19 of the EU AI Act specifically calls for identifying the natural persons involved in a high-risk AI decision, which a shared service credential or an API key alone cannot do. A BigID catalog entry describes a dataset or a repository: its location, its classification, its owner, the regulation it falls under, and when the classification job last ran against it. Neither record substitutes for the other. A catalog entry answers a standing question about where data lives; an audit record answers a point-in-time question about what a specific request did, and it does so with a signature the application generating the request cannot alter after the fact.

← All posts