DeepInspect vs WitnessAI: Coverage, Boundary, and the Audit Gap

Evaluated as a cybersecurity company, WitnessAI is well capitalized and moving fast: a $58 million strategic round in January 2026 pushed total funding past $85 million on more than 500% year-over-year ARR growth (SecurityWeek). The platform covers three jobs: Observe maps AI activity across the enterprise at the network level, without a browser extension, showing which tools are in use, who is using them, and what data they touch. Control enforces intent-based policies that go past a binary allow-or-block, letting a security team allow, warn, block or route an interaction based on conversational context. Protect runs a bidirectional AI firewall against prompt injection, jailbreaks, and harmful content, with data tokenization to shield sensitive information before it reaches a model. The company reports governing more than 4,000 AI applications and over 100 model types.

Its boundary is visibility and intent, not identity. WitnessAI and DeepInspect both enforce policy on AI traffic, but they anchor to different things: WitnessAI reads the intent and content of the conversation, DeepInspect reads the identity making the call and signs the record. That difference decides what each platform can hand a forensic investigator or an auditor after the fact, which is the question the rest of this piece works through.

TL;DR

WitnessAI is a broad AI security and governance platform: network-level visibility into AI usage, intent-based policies keyed to conversational context, and an AI firewall with data tokenization. DeepInspect is a focused identity-bound policy and audit layer: each HTTP call to an LLM evaluated against the natural-person identity, the data classification, and the active policy version, with a signed per-decision audit record. WitnessAI is strong for enterprise-wide visibility and intent-based guardrails. DeepInspect is strong for per-request identity authorization and tamper-evident evidence. In a regulated deployment they cover different requirements.

WitnessAI: what it is and where it sits

WitnessAI is designed to govern the whole workforce, human and agentic, from one platform. Observe gives network-level visibility into AI usage, which is a genuine strength: it surfaces shadow AI without depending on endpoint agents or browser plugins. Control is the module that carries the policy model, and its design point is intent. Rather than a flat allow or block, a policy can warn a user, block an action or route the interaction elsewhere based on the conversational context it reads. Protect adds a pre-execution and response AI firewall, blocking injection and jailbreak attempts and tokenizing sensitive data before it leaves for a model. The platform also runs automated red teaming to harden models before production, and it has extended into agentic control that governs how agents interact with systems, tools, and MCP servers.

That is a wide platform, and several of its functions reach past the boundary a proxy between users and LLMs enforces. Automated red teaming is pre-production model testing. Enterprise-wide discovery across 4,000-plus applications is a visibility function that spans more than the HTTP AI request path. These are useful security capabilities, and they are a different job than authorizing and recording each call to a model.

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, running deterministic and fail-closed against any model.

Where the two separate

Intent versus identity as the anchor. WitnessAI's Control reads conversational context and acts on what the interaction appears to intend. DeepInspect evaluates who the authenticated caller is, what role they hold, and what data class the prompt carries, then applies a deterministic policy. Intent-based control is expressive for shaping user behavior. Identity-bound authorization is the primitive a regulator recognizes, because it answers which named person was permitted to do this, not what the conversation seemed to be about.

What the record proves. WitnessAI's Observe module and dashboards show AI activity across the enterprise. DeepInspect commits a signed, identity-bound record for each decision, on a write path the calling application never controls, before the response returns. Visibility shows what happened, where a signed audit record proves who was authorized under which policy version, and that second thing is the artifact an audit turns on.

Deterministic policy versus context reading. An intent-based control that reads conversational context is doing a probabilistic classification of what the user meant. DeepInspect's authorization decision is deterministic: identity, role, data class, policy version, pass or block. Both have a place, and the deterministic decision is the one that fails closed and produces a record that reads the same way every time.

What each hands an incident responder

Evaluated on incident response and forensic reconstruction specifically, the two platforms hand an investigator different artifacts. WitnessAI's Observe dashboards show AI usage across the enterprise: which tools, which users, which data categories, aggregated for a security team to review. That is real value for scoping an incident's blast radius fast. It is not the same thing as evidence, because a dashboard reconstructed from telemetry after the fact carries no cryptographic guarantee that nothing was dropped or altered between the event and the review. DeepInspect's model is narrower and built for exactly this: a signed, identity-bound record committed per decision, before the response returns, on a write path the calling application never had custody of. Reconstructing what happened, who was authorized, under which policy version, is a query against records built for that question rather than a dashboard built for trend visibility. The immutability and chain-of-custody properties that make a record admissible are the difference between "the dashboard showed unusual activity" and "here is the signed decision record naming the identity, the policy, and the timestamp."

Feature comparison

WitnessAI's side of the comparison: enterprise-wide AI discovery at the network level with no browser extension, intent-based policy that can allow, warn, block or route, a bidirectional AI firewall for injection and jailbreak defense, data tokenization, automated red teaming, and agentic and MCP governance through a dedicated control plane. Caller identity is observed, not bound. Per-request authorization runs on intent and context, sensitive-data handling is a set of tokenization controls, the audit trail is observability and dashboards, and write-path independence from the calling application is not documented.

DeepInspect's side: it governs traffic in the request path instead of discovering it after the fact, and applies deterministic identity-bound policy in place of intent classification. Firewall-style defense becomes one input to that policy, not a separate product, and prompt content gets classified against a named regulated-data taxonomy instead of general tokenization. Agentic and MCP traffic is governed as it crosses HTTP. The caller is the natural person resolved from the identity provider, authorization is architectural, not advisory, and every decision produces a signed, tamper-evident, identity-bound record on a write path the application never controls.

Pick WitnessAI if

Pick WitnessAI if enterprise-wide visibility into AI usage is the pressing need, because network-level discovery without browser agents is a strong capability. Intent-based guardrails that can warn or route, rather than only allow or block, are the other draw, alongside an AI firewall with data tokenization if that matches your runtime-defense requirement. It also fits a team that wants one platform to govern both employees and agents, including red teaming and broad application coverage.

Pick DeepInspect if

Pick DeepInspect if the requirement is per-request authorization bound to a named person, decided deterministically and inline. A policy that has to turn on the data classification in the prompt, including PHI or material non-public information, is the second fit. The third is an auditor who 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, which calls for a non-bypassable chokepoint on HTTP AI traffic and evidence that reads the same way every time.

Composition in production

Visibility, intent shaping, and identity-bound authorization compose. WitnessAI can map AI usage across the enterprise, apply intent-based guardrails, and defend at runtime with its firewall. DeepInspect can bind each sanctioned HTTP call to a natural-person identity, apply the per-role policy deterministically, and commit the signed record. One governs behavior across a wide surface. The other authorizes each call and produces evidence a regulator accepts. A deployment that runs both gets breadth of governance and depth of accountability.

Pricing approach

WitnessAI is a commercial platform priced through sales, with no public price list; the company has raised outside funding and sells to enterprises. DeepInspect is priced through a sales conversation and deploys self-hosted or managed. Both scope pricing to the deployment, so a real comparison comes from a conversation about coverage, data sensitivity, and volume.

DeepInspect

This is the gap DeepInspect closes. WitnessAI governs AI usage by reading intent and shaping behavior across the enterprise. DeepInspect decides whether a specific authenticated person is permitted to make a call, 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 separates a governance dashboard from audit-ready evidence, and it is the exact thing an EU AI Act Article 12 reviewer or a HIPAA auditor tests for. If you are evaluating WitnessAI and still need identity-bound authorization and signed evidence on your AI traffic, see what that looks like. Book a demo today.