DeepInspect vs WitnessAI: Intent-Based Guardrails and Identity-Bound Audit

WitnessAI is a unified AI security and governance platform built around three modules. 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.

WitnessAI and DeepInspect both enforce policy on AI traffic. They differ on what the policy is anchored to. WitnessAI anchors control to the intent and content of the conversation. DeepInspect anchors it to the identity making the call, and signs the record.

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. Deterministic, fail-closed, model-agnostic.

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. A signed audit record proves who was authorized under which policy version, which 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.

Feature comparison

| Capability | WitnessAI | DeepInspect | |---|---|---| | Enterprise-wide AI discovery | Yes, network-level, no browser extension | Governs traffic in path | | Intent-based policy | Yes, allow, warn, block, route | Deterministic identity-bound policy | | AI firewall, injection and jailbreak defense | Yes, bidirectional | As one policy input | | Data tokenization | Yes | Prompt-level classification | | Automated red teaming | Yes | Out of scope | | Agentic and MCP governance | Yes, agentic control | Governs MCP over HTTP | | Identity of the caller | Observed | Natural person from the identity provider | | Per-request identity-bound authorization | Intent and context | Yes, architectural | | PHI and MNPI classification | Sensitive-data controls | Named taxonomy | | Signed per-decision audit record | Observability and dashboards | Yes, tamper-evident, identity-bound | | Write-path independence from the app | Not documented | Yes |

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. Pick it if you want intent-based guardrails that can warn or route rather than only allow or block. Pick it if an AI firewall with data tokenization matches your runtime-defense requirement. Pick it if you want 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. 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 you need a non-bypassable chokepoint for 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 is what separates a governance dashboard from audit-ready evidence, and it is what an EU AI Act Article 12 reviewer or a HIPAA auditor is testing. If you are evaluating WitnessAI and still need identity-bound authorization and signed evidence on your AI traffic, let's talk today.