DeepInspect vs Lakera: An Architectural Comparison for Enterprise AI Audit Programs

DeepInspect and Lakera show up on the same shortlist for enterprises building an AI security program. The two products solve overlapping problems with different architectural placements. This piece is a head-to-head comparison on the axes I see buyers care about: where enforcement sits relative to the request boundary, what identity gets bound to the record, what the audit record contains, and how the coverage extends across multiple LLM providers.

TL;DR

DeepInspect is an identity-aware HTTP-proxy that authenticates the caller against the corporate IdP at the request boundary and commits a per-decision record with identity, classification, policy version, decision, and an integrity signature. Lakera is a content classifier that scores prompts and responses against an adversarial-pattern library, with SDK and proxy deployment modes. Pick DeepInspect if the program needs identity-bound per-request records across multiple LLM providers. Pick Lakera if the program needs adversarial-pattern classifier coverage inside specific application teams.

Where DeepInspect sits

DeepInspect sits inline on the HTTP path between authenticated users or agents and any LLM. The proxy terminates TLS at the inspection layer, authenticates the caller against the corporate identity provider (Okta, Entra ID, Ping, or any OIDC-compatible IdP), runs deterministic classification against the prompt content, evaluates policy against the identity context and the classification label, and commits a per-decision audit record before the model receives the request. The records carry timestamp, identity, classification, policy version, decision, and an integrity signature on a tamper-evident series.

The placement gives DeepInspect three operating properties that the comparison hinges on. Identity binding at the request boundary, because the IdP integration happens at the proxy. Multi-model coverage by default, because the proxy is model-agnostic and routes to any provider behind it. A canonical record series, because every decision passes through the same enforcement point.

Lakera: where it sits

Lakera ships in two deployment modes. The SDK runs inside the application code: the developer calls the classifier before invoking the LLM. The proxy mode places a Lakera-managed HTTP proxy in front of the LLM endpoint and routes the application's traffic through it. Both modes run the same content classifier, which scores prompts and responses against a library of adversarial patterns (prompt injection variants, PII categories, organizational policy violations). After the 2025 Check Point acquisition, the product line continues under the Check Point AI security umbrella.

The placement gives Lakera strong classification coverage on the prompt content. The identity-binding step depends on how the application passes the user identity through to the classifier (in the SDK case) or how the proxy is wired against the IdP (in the proxy case). The record contains the classification decision and the prompt text. Identity is present when the integration carries it through.

Feature comparison

| Axis | DeepInspect | Lakera | |---|---|---| | Primary placement | HTTP proxy at the request boundary | SDK in application code or HTTP proxy | | IdP integration | Built in at the proxy (Okta, Entra ID, Ping, OIDC) | Application or proxy-side integration | | Identity binding | Every record carries natural-person identity | Carried when application or proxy passes it through | | Classification | Deterministic categories (PII, PHI, source code, customer data, custom) | Adversarial-pattern library plus PII categories | | Prompt injection coverage | Pattern detection plus policy on suspicious tool calls | Strong adversarial-pattern library | | Audit record shape | Identity, classification, policy version, decision, timestamp, integrity signature | Classification decision, prompt text, rule that fired | | Multi-model coverage | Model-agnostic across OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, self-hosted | Model-agnostic | | Tamper-evident records | Yes (signed series) | Available as platform feature | | Article 19 natural-person field | Yes by default | Yes when identity is wired through | | Latency overhead | Under 50 ms in internal testing | Comparable on the proxy path |

Pick Lakera if

• The program's primary risk model is prompt injection and the adversarial-pattern library is the principal control the team wants to deploy.
• The application teams own the integration and prefer an SDK they call from inside their code.
• The Check Point AI security umbrella is already in the broader security stack and consolidation matters.
• The audit obligations live primarily at the application layer rather than at a centralized AI request boundary.

Pick DeepInspect if

• The program is building toward an EU AI Act Article 12 record series with Article 19 identification of natural persons on every record across multiple application teams.
• The deployment spans multiple LLM providers (OpenAI, Anthropic, Bedrock, Azure OpenAI, self-hosted) on the same policy surface.
• HIPAA or financial-services audit obligations require a tamper-evident per-decision record series the enterprise controls.
• The identity provider integration belongs at the inspection boundary rather than inside each application team's code.

Pricing approach

DeepInspect prices per protected endpoint and per request volume tier. Lakera prices per seat or per request depending on the deployment shape. Both vendors quote against the specific deployment after a scoping conversation. Neither product publishes a public price list, which is standard for enterprise AI security products at this stage of the market.

Regulatory framing under EU AI Act Article 12 and 19

Article 12 requires automatic recording of events over the lifetime of the system sufficient to ensure traceability. Article 19 specifies that records identify natural persons involved. Article 99 sets penalties at €15 million or 3% of global annual turnover for high-risk non-compliance. The August 2, 2026 deadline applies to high-risk AI systems including credit scoring, employment screening, education access, and biometric identification.

The record-series question for the audit program reduces to which placement supplies identity, classification, and policy state on every decision. The HTTP-proxy placement with IdP integration supplies them at the boundary. The SDK placement supplies them when the integration carries the identity. The auditor receives the same fields either way when the integration is complete, but the operational cost of keeping the integration complete across dozens of application teams is the variable that decides the boundary placement for most multi-team programs.

DeepInspect

DeepInspect is the HTTP-proxy enforcement gateway shape of this comparison. The proxy authenticates the caller against the corporate IdP at the request boundary, classifies the prompt content, evaluates policy against the identity and classification, and commits a per-decision audit record before the response returns to the application. The records carry the fields EU AI Act Article 12 and Article 19 expect on the series HIPAA Security Rule references.

For programs choosing between DeepInspect and Lakera, the framing I find useful is to start from the audit obligation and the multi-model scope. If the program needs identity-bound records across multiple providers on a shared series, the proxy placement is the one that supplies them by default.

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