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 and who owns it, which is what privacy compliance and data security posture management require. DeepInspect operates on a different layer: live HTTP traffic between authenticated users or agents and LLM APIs. It doesn't scan stored data. It evaluates each prompt and response as it happens, against identity-bound policy, and produces a signed record of that decision. A team that needs both a data catalog and live prompt enforcement usually runs both tools, because one answers where sensitive data lives and the other answers what a specific user is allowed to send to a model right now, and neither tool's records substitute for the other's evidence in an audit.

What does cloud-native DSPM mean and how does it differ from BigID?

Cloud-native DSPM platforms, Cyera being the most commonly cited example, classify sensitive data specifically within cloud environments (AWS, Azure, GCP, major SaaS platforms), often with an agentless discovery approach that skips installing connectors on every source. BigID's architecture spans a broader set of environments, including on-prem systems and legacy databases, which matters if your data estate predates a cloud-first strategy and still runs mainframe or file-server workloads a cloud-only scanner never reaches. Both categories answer the same underlying question: where is our sensitive data and how is it classified, and both produce a catalog rather than a live decision. The discovery mechanics differ. Neither operates on live AI traffic. Both get evaluated on deployment speed, connector coverage, and classification accuracy for stored data, a different scorecard than the one used for an enforcement proxy sitting on live requests.

Do we need both a DSPM tool and an AI enforcement layer?

Regulated organizations typically end up running both, because the two answer different audit questions. A DSPM tool like BigID produces the data inventory a GDPR or CCPA access request requires: what personal data exists, where, under what legal basis, and which system owns it. An AI enforcement layer produces the per-decision record a regulation like EU AI Act Article 12 requires: what a specific AI system did with a specific request, from a specific identified person, at a specific moment, under what policy version. A regulator asking where your customer data lives and a regulator asking who authorized a given AI decision under what policy are asking two different questions, and one tool's records aren't built to answer the other one. Budgeting for both, rather than picking one, is usually the realistic outcome once the two audit questions get separated out this way.

What is shadow AI discovery and does BigID cover it?

Shadow AI discovery means identifying where sensitive data has flowed into AI tools that weren't sanctioned or governed by IT, often surfaced through a browser history trace, a SaaS activity log, or an AI vendor's stored conversation history. BigID added features here, extending its classification engine to flag when a dataset it already catalogs shows signs of having been queried through or used to train an AI tool. That's valuable for incident response and for building an initial risk picture. It's backward-looking by design: it tells you what already happened, not what's happening in a session right now. Preventing the next occurrence of the same exposure requires a control positioned at the point where the prompt gets sent, evaluating it before it leaves, rather than a later scan that finds evidence the exposure already occurred.

Is DeepInspect a replacement for our existing DLP?

DeepInspect doesn't replace network or endpoint DLP. It covers a channel those tools were never built to inspect. Network-layer DLP runs underneath TLS encryption and reads packet metadata; the content of an HTTPS POST to an LLM API stays invisible to it without AI-specific TLS inspection and payload parsing, which most DLP deployments don't have configured today, even mature ones with years of policy tuning behind them. Endpoint DLP can block a browser tab or flag a file transfer but has no concept of prompt context the way a proxy purpose-built for LLM traffic does, since it was never designed to parse a JSON request body against a model API. Keep existing DLP for the channels it already covers, file transfers, email, removable media, and add AI-traffic enforcement as the layer that closes the gap those tools were never built to reach.

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August 22, 2026

BigID Alternatives: How to Pick When Data-at-Rest Scanning Is Not the Whole Job

BigID discovers and classifies sensitive data across repositories, cloud storage, and SaaS for privacy compliance and DSPM. It scans data at rest on a schedule. Teams that also need to inspect live LLM prompt and response traffic need a second, different layer. This piece walks through what BigID covers and six categories of alternative to evaluate alongside it.

ByParminder Singh· Founder & CEO, DeepInspect Inc.

Comparisons & Alternativesdata-loss-preventiondlpai-governanceai-securityshadow-aizero-trust

BigID Alternatives: How to Pick When Data-at-Rest Scanning Is Not the Whole Job

BigID's product pages describe a scanning engine that connects to databases, file shares, cloud storage, and SaaS apps, then classifies what it finds against privacy and security policy. That scan runs on a schedule, not on the wire. If your evaluation started because you need something to watch what an employee pastes into a live ChatGPT session, BigID's architecture answers a different question than the one you're asking.

I get versions of this question every few weeks, usually from a CISO or an AI Platform Lead who inherited a DSPM tool and a new AI-traffic problem in the same quarter. The two look similar on a slide. They sit at different layers of the stack, and treating one as a drop-in replacement for the other is, frankly, the fastest way to fail a security review.

TL;DR

BigID discovers and classifies sensitive data at rest for privacy compliance and data security posture management, and now tracks where that data has flowed into AI tools.
BigID does not sit inline on the HTTP path between a user and an LLM API, so it cannot inspect or block a specific prompt as it happens.
Cyera and other cloud-native DSPM platforms compete with BigID on the same data-at-rest ground, with a different deployment model.
If your gap is live prompt and response traffic rather than stored files, you need an inline enforcement layer alongside, not instead of, a DSPM tool.
DeepInspect fits that second category: an identity-aware proxy that enforces policy on AI requests as they happen and signs a record of every decision.

What BigID actually does

BigID connects to your data stores and runs classification jobs against what it finds. The output is a catalog: this repository holds this category of sensitive data, owned by this system, subject to this regulation. That catalog backs GDPR and CCPA data subject access requests, retention policy enforcement, and now shadow AI discovery, meaning BigID can flag that a dataset containing customer PII was uploaded to an unsanctioned AI tool at some point in the past.

That's a real, useful capability. IBM's Cost of Data Breach Report found that one in five breached organizations studied had a breach linked to shadow AI, so knowing where sensitive data has touched an AI tool already matters. It's a forensic answer built from periodic scans. It doesn't tell you whether the prompt a specific user is sending right now carries data they aren't authorized to send.

Six categories of BigID alternative

Match the category to the actual gap you're evaluating for, not the marketing label the vendor sells under.

Cloud-native DSPM platforms. Cyera is the clearest example: a cloud-native data security posture platform classifying data across cloud environments, often positioned against BigID on deployment speed and agentless discovery. Best for: cloud-first teams wanting BigID's discovery job without on-prem connectors.
Enterprise DLP suites with AI-tool blocking. Several DLP vendors added browser-extension blocking for known AI domains. This stops the browser tab, not the API call. Best for: a fast-to-deploy control on managed endpoints while a longer program gets built.
CASB and SSE platforms with AI app visibility. Brokers now flag "AI application" as a discovered category, the way they flag file-sharing apps. That's visibility into access, not what data left the prompt. Best for: teams on an SSE stack wanting AI app discovery in the same console.
A homegrown proxy plus in-house classification. Engineering-heavy teams sometimes build a reverse proxy with regex redaction in front of approved endpoints. Cheap to start, expensive to maintain once policy spans multiple providers. Best for: teams with one or two endpoints and the capacity to own it long-term.
Model-provider-native guardrails. AWS Bedrock Guardrails, Azure AI Content Safety, and similar features filter content inside the inference call for one provider's endpoints. Near-free, quick to turn on, locked to that provider. Best for: single-cloud deployments where provider lock-in is already the strategy.
DeepInspect. An identity-aware proxy sitting inline on the HTTP path between authenticated users or agents and any LLM API, evaluating each request against per-role policy and signing an audit record before the response reaches the application. Model-agnostic across OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, and self-hosted endpoints. Best for: teams whose gap is live prompt traffic, not stored data, needing per-decision evidence for a security review.

Pick BigID if...

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

Your driver is a GDPR or CCPA access-request backlog and you need a working catalog of where personal data lives.
You need to inventory unstructured file shares and legacy databases that predate any AI initiative.
Your immediate question is what sensitive data has already been exposed to AI tools historically.
You have the internal team for a multi-quarter classification rollout across a large, mixed data estate.

Pick DeepInspect if...

DeepInspect earns its place once the open question shifts to live traffic rather than stored data.

Your evaluation started because someone asked whether you can stop a specific prompt before it reaches the model, and today's answer is no.
You need a per-decision, tamper-evident record of AI traffic for a regulation like EU AI Act Article 12, not a periodic data inventory.
You run more than one LLM provider and want one policy layer instead of one guardrail configuration per vendor.
You already run a DSPM tool and the piece still missing is the live traffic layer, not a second data catalog.

DeepInspect

The categories above are strong at the job they were built for: finding and classifying data that already exists somewhere. None of them, BigID included, sit inline on the HTTP request between a user and an LLM API, or can redact and block a specific prompt in flight. I wrote a fuller side-by-side in DeepInspect vs BigID.

DeepInspect was built for that narrower, later stage: a stateless proxy in front of any HTTP-based LLM endpoint. Every request carries the identity and role context the calling application supplies, evaluated against per-route and per-role policy before the model sees it. The outcome, permit, redact, or deny, gets written to a signed audit record independent of the application that made the call.

If you already run BigID or a similar DSPM platform and the piece still missing is prompt-level enforcement, that's the boundary DeepInspect sits on. If you are facing the August deadline, let's talk.

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 and who owns it, which is what privacy compliance and data security posture management require. DeepInspect operates on a different layer: live HTTP traffic between authenticated users or agents and LLM APIs. It doesn't scan stored data. It evaluates each prompt and response as it happens, against identity-bound policy, and produces a signed record of that decision. A team that needs both a data catalog and live prompt enforcement usually runs both tools, because one answers where sensitive data lives and the other answers what a specific user is allowed to send to a model right now, and neither tool's records substitute for the other's evidence in an audit.
What does cloud-native DSPM mean and how does it differ from BigID?: Cloud-native DSPM platforms, Cyera being the most commonly cited example, classify sensitive data specifically within cloud environments (AWS, Azure, GCP, major SaaS platforms), often with an agentless discovery approach that skips installing connectors on every source. BigID's architecture spans a broader set of environments, including on-prem systems and legacy databases, which matters if your data estate predates a cloud-first strategy and still runs mainframe or file-server workloads a cloud-only scanner never reaches. Both categories answer the same underlying question: where is our sensitive data and how is it classified, and both produce a catalog rather than a live decision. The discovery mechanics differ. Neither operates on live AI traffic. Both get evaluated on deployment speed, connector coverage, and classification accuracy for stored data, a different scorecard than the one used for an enforcement proxy sitting on live requests.
Do we need both a DSPM tool and an AI enforcement layer?: Regulated organizations typically end up running both, because the two answer different audit questions. A DSPM tool like BigID produces the data inventory a GDPR or CCPA access request requires: what personal data exists, where, under what legal basis, and which system owns it. An AI enforcement layer produces the per-decision record a regulation like EU AI Act Article 12 requires: what a specific AI system did with a specific request, from a specific identified person, at a specific moment, under what policy version. A regulator asking where your customer data lives and a regulator asking who authorized a given AI decision under what policy are asking two different questions, and one tool's records aren't built to answer the other one. Budgeting for both, rather than picking one, is usually the realistic outcome once the two audit questions get separated out this way.
What is shadow AI discovery and does BigID cover it?: Shadow AI discovery means identifying where sensitive data has flowed into AI tools that weren't sanctioned or governed by IT, often surfaced through a browser history trace, a SaaS activity log, or an AI vendor's stored conversation history. BigID added features here, extending its classification engine to flag when a dataset it already catalogs shows signs of having been queried through or used to train an AI tool. That's valuable for incident response and for building an initial risk picture. It's backward-looking by design: it tells you what already happened, not what's happening in a session right now. Preventing the next occurrence of the same exposure requires a control positioned at the point where the prompt gets sent, evaluating it before it leaves, rather than a later scan that finds evidence the exposure already occurred.
Is DeepInspect a replacement for our existing DLP?: DeepInspect doesn't replace network or endpoint DLP. It covers a channel those tools were never built to inspect. Network-layer DLP runs underneath TLS encryption and reads packet metadata; the content of an HTTPS POST to an LLM API stays invisible to it without AI-specific TLS inspection and payload parsing, which most DLP deployments don't have configured today, even mature ones with years of policy tuning behind them. Endpoint DLP can block a browser tab or flag a file transfer but has no concept of prompt context the way a proxy purpose-built for LLM traffic does, since it was never designed to parse a JSON request body against a model API. Keep existing DLP for the channels it already covers, file transfers, email, removable media, and add AI-traffic enforcement as the layer that closes the gap those tools were never built to reach.

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