DeepInspect vs Nightfall: AI-Specific Enforcement Versus Cloud DLP for LLM Traffic

DeepInspect and Nightfall sit on the same shortlist for enterprises trying to control sensitive data flowing into LLM endpoints. The two products approach the problem from different lineages. DeepInspect was built as an identity-aware HTTP-proxy for AI runtime traffic. Nightfall was built as a cloud DLP across SaaS applications and recently added LLM API coverage. This piece walks through what each one covers and where the audit record obligation steers the buying decision.

TL;DR

DeepInspect sits inline on the HTTP path between authenticated users or agents and any LLM, binds identity at the proxy, and commits per-decision audit records. Nightfall is a cloud DLP that classifies sensitive data across many SaaS apps and storage targets, with LLM API integrations on the inbound side. Pick DeepInspect if the program needs identity-bound per-request records and inline enforcement at the LLM request boundary. Pick Nightfall if the program needs cloud DLP coverage across many SaaS apps and the LLM piece is one of several.

Where DeepInspect sits

DeepInspect sits inline on the HTTP path between authenticated users or agents and any LLM. The proxy terminates TLS, authenticates against the corporate IdP, classifies the prompt content, evaluates policy against identity and classification, and commits a per-decision audit record before the model receives the request. The records carry identity, classification, policy version, decision, timestamp, and an integrity signature on a tamper-evident series.

The placement is purpose-built for the LLM request boundary. The classifier is tuned for prompt content: PII, PHI, source code with secret patterns, customer data fields bound to data tenants, and free-form sensitive categories defined per organization.

Nightfall: where it sits

Nightfall is a cloud DLP that classifies sensitive data across SaaS applications including Slack, Google Drive, Salesforce, GitHub, and Confluence, plus the file storage and source code surfaces. Nightfall added LLM API integrations that classify data inbound to LLM endpoints, often through SDK integrations the application calls before invoking the model or through specific proxy connectors.

Nightfall's strength is breadth across SaaS apps. The classifier catalog covers the common PII, PHI, financial data, and code-secret patterns enterprises monitor. The platform's compliance surface aggregates findings across all the connected apps for a consolidated view of where sensitive data sits in production.

Feature comparison

| Axis | DeepInspect | Nightfall | |---|---|---| | Primary surface | Runtime HTTP enforcement on LLM traffic | Cloud DLP across SaaS apps plus LLM integrations | | Primary placement | HTTP proxy at LLM request boundary | Connectors per app, SDK, or proxy for LLM | | IdP integration | Built in at proxy | Application or proxy-side integration | | Identity binding on every record | Yes by default | When the integration carries it through | | Classification | Deterministic categories tuned for prompt content | Cloud DLP catalog tuned for SaaS data | | Multi-provider LLM coverage | Yes | Coverage depends on the integration set | | Coverage across SaaS apps beyond LLM | Outside primary scope | Yes (Slack, Drive, Salesforce, GitHub, etc.) | | Tamper-evident record series | Yes (signed) | Available as platform feature | | Inline enforcement at request boundary | Yes (proxy can fail closed) | Depends on integration mode | | Article 19 natural-person field | Yes by default | When the integration carries it through | | Latency overhead | Under 50 ms in internal testing | Comparable on proxy path |

Pick Nightfall if

• The program needs broad cloud DLP coverage across many SaaS apps (Slack, Drive, Salesforce, GitHub) and the LLM piece is part of a larger surface.
• The team already runs Nightfall for SaaS DLP and wants to extend the same product into LLM coverage rather than introduce a separate enforcement layer.
• The risk model centers on sensitive data sprawl across SaaS rather than identity-bound per-request enforcement at the LLM boundary.
• The compliance program tracks findings across many surfaces in one consolidated view.

Pick DeepInspect if

• The program centers on identity-bound per-request records under EU AI Act Article 12 or HIPAA audit obligations.
• The deployment spans multiple LLM providers on the same policy surface.
• Enforcement happens at the LLM request boundary before the model receives the request, with the identity context on every record by default.
• The risk model is specifically the LLM request path: prompt content classification, policy decisions, and the per-decision audit record auditors sample.

Where the products overlap and where they do not

Both products classify sensitive data. The overlap is real on the inspection side. The divergence is on the enforcement surface and the identity-binding surface. Nightfall's identity context comes from the SaaS app's session model (Slack user, Drive principal, Salesforce login) and from the application-side integration on the LLM path. DeepInspect's identity context comes from the corporate IdP at the proxy boundary, on every request, with no application-side wiring required.

A program that needs both LLM-specific identity-bound enforcement AND broader SaaS DLP across many apps runs the two products on adjacent surfaces. DeepInspect handles the LLM request boundary. Nightfall handles the broader SaaS DLP coverage. The audit program references DeepInspect's records for the AI decision audit and Nightfall's findings for the broader sensitive-data inventory.

Regulatory framing

EU AI Act Article 12 requires automatic recording of events sufficient to ensure traceability of the AI system. Article 19 specifies identification of natural persons involved on the record. HIPAA Security Rule 45 CFR 164.312(b) expects audit controls on systems that process PHI.

The Article 12 record series sits naturally on the HTTP-proxy enforcement layer because the identity context and the policy state are present at the LLM request boundary by default. A SaaS-DLP-driven record series can supply the field when the integration carries it through, but the operational cost of keeping the identity binding correct across every application integration is the variable that steers most multi-team programs toward the centralized proxy placement.

Pricing approach

Both vendors quote against the deployment after scoping. DeepInspect prices per protected endpoint and request volume tier. Nightfall prices across the SaaS DLP coverage scope and the volume of classifications. Public price lists are not available for either product.

DeepInspect

DeepInspect is the identity-aware HTTP-proxy enforcement gateway for LLM traffic. The proxy authenticates the caller against the corporate IdP, classifies the prompt content, evaluates policy against identity and classification, and commits a per-decision audit record before the response returns. The records carry the fields EU AI Act Article 12 and Article 19 expect on the series HIPAA Security Rule references.

For programs comparing DeepInspect to Nightfall, the question reduces to where the LLM request path needs to sit. If the program needs identity-bound enforcement records on every LLM request across multiple providers, the proxy placement is the one that supplies them by default. If the program needs broad SaaS DLP coverage with LLM as one of many surfaces, Nightfall's platform shape fits that.

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