Does the inspection layer change the analyst's experience?

The inspection layer adds under 50 ms to the request path. LLM inference takes 800 ms to 3 seconds and the warehouse query takes 200 ms to 30 seconds. The overhead is invisible relative to the rest of the cycle. The analyst's experience changes when the policy decision blocks or modifies the request. A block surfaces as a structured message ("the query references columns outside your role's scope"). A modify surfaces as a visible redaction in the result summary ("the user-identifier column was masked because your role does not include PII access"). The analyst's UI handles each outcome explicitly.

How does the inspection layer handle the schema-retrieval step?

The inspection layer reads the column catalog the analytics tool uses for schema retrieval. The catalog carries the role-to-column permissions as metadata. The layer trims the schema fragments the retrieval surfaces to match the analyst's role before the fragments reach the model. The model does not see the columns the role excludes, so the SQL the model emits cannot reference them. The layer logs the schema-trim decision on the audit record.

What does the inspection layer do when the model emits SQL that references a forbidden column?

The layer reads the generated SQL before execution and parses it against the analyst's role catalog. If the SQL references a column outside the role's scope, the layer commits a block decision. The SQL does not reach the warehouse. The block record carries the SQL text, the referenced column, the role's permitted columns, and the model and version that emitted the SQL. The block record is the artifact the audit reviewer reads when the question is "did the analyst ever execute a query touching column X."

How does the inspection layer integrate with the warehouse's existing access controls?

The warehouse's existing row-level and column-level access controls remain in place. The inspection layer adds a control point upstream of the warehouse, at the SQL-generation boundary. The warehouse executes the SQL under the analyst's warehouse role, and the warehouse's controls enforce the role's permissions at execution. The inspection layer prevents the SQL from reaching the warehouse at all if the SQL violates the policy. The two control points compose: the inspection layer catches policy violations at the SQL-generation step, the warehouse catches policy violations at the SQL-execution step.

What audit records does the analytics tool produce versus the inspection layer?

The analytics tool produces application-level records of analyst sessions, prompts, and queries. The records live inside the tool's boundary and serve the tool's operational needs. The inspection layer produces per-decision records at the HTTP boundary outside the tool. The records carry the seven fields the regulator expects. The two record series compose: the tool's records cover the application state, the inspection layer's records cover the EU AI Act Article 12, GDPR Article 22, and Fannie Mae LL-2026-04 audit obligations.

AI Security for Product Analytics: The Inspection Layer Between the Analyst Prompt and the Customer Data Warehouse

Product analytics teams have moved a significant share of their exploratory work onto LLMs. The analyst opens a chat interface, types a question in natural language ("how many users on the Enterprise plan signed up in May and churned by June"), and the LLM emits SQL that runs against the customer data warehouse. The warehouse returns the result set. The LLM summarizes the rows and the analyst pastes the summary into a Notion doc or a Slack thread. The cycle is short, the surface is wide, and the customer data the workflow touches at every step carries PII, billing identifiers, behavioral records, and cohort signals that regulators care about.

I want to walk through the request-time data an analyst LLM workflow reads, the identity-aware policy decisions the deployment has to commit at the prompt-to-SQL boundary, the audit record format that satisfies EU AI Act Article 12 and GDPR Article 22 obligations, and the architectural pattern that closes the post-authentication gap most analyst workflows leave open today.

The data the analyst workflow reads at request time

The analyst LLM workflow reads four classes of data at each turn. The session identity (who is asking) carries the natural-person identifier from the SSO, the analyst's team and role, the warehouse role the analyst's SQL will execute under, and any session-scoped scopes the chat tool propagates. The request content (what is being asked) carries the analyst's natural-language prompt, the system prompt the analytics tool sets, the model selection, and the database schema context the tool retrieves to ground the SQL. The retrieved context (what the workflow pulled in to answer) carries the schema fragments, the column comments, and any sample rows the schema retrieval surfaced to the model. The result-set context (what came back from the warehouse) carries the rows the executed SQL returned, the column types, and any PII fields the rows contain.

The four classes compose the decision surface the policy evaluates against. An analyst on the marketing team can ask the same prompt that an analyst on the security team can ask, and the policy outcome differs because the marketing analyst's warehouse role excludes the columns the security analyst's role includes. The same analyst can ask the same prompt on a Wednesday and a Thursday and the policy outcome differs because the warehouse role was rotated in between. The identity-aware policy reads the specific combination at the specific moment.

The identity-aware policy decisions the deployment has to commit

The deployment commits five decisions at the prompt-to-SQL boundary before the SQL reaches the warehouse, and a sixth before the result-set reaches the model.

The first decision is the prompt classification. The classifier reads the analyst's natural-language prompt and tags the data classes the prompt is reaching for. A prompt that mentions "churned users on the Enterprise plan" reaches PII (user identifier) and billing data. A prompt that mentions "median time-on-page by feature" reaches behavioral data but no PII. The classification result is what the next four decisions evaluate against.

The second decision is the schema-retrieval scope. The schema retrieval reads from a column catalog and surfaces the relevant fragments. The policy evaluates the analyst's role against the catalog and trims the surfaced schema to the columns the role is permitted to see. An analyst whose role excludes the users.email column does not see the column in the schema surfaced to the model, so the SQL the model emits cannot reference it.

The third decision is the generated SQL inspection. The policy reads the SQL the model emits and verifies the SQL only references the columns the analyst's role is permitted. A model that emits SQL referencing a column outside the role's scope produces a block at this boundary. The SQL does not execute.

The fourth decision is the row-volume cap. The policy evaluates the SQL's projected row volume against the analyst's role limits. A prompt that would return 50 million rows produces a modify (the policy injects a LIMIT) or a block (the policy refuses the query). The decision is recorded on the audit record.

The fifth decision is the result-set classification. The classifier reads the rows the warehouse returned and tags the actual PII present. A SQL query that should not return email addresses but does (due to a join the model emitted) produces a redact decision: the email column is masked before the rows reach the model context.

The sixth decision sits at the model boundary. The redacted result-set forwards to the model with the system prompt the analytics tool sets. The policy commits the decision to the audit record series and the model produces the summary the analyst sees.

The audit record the workflow has to produce

EU AI Act Article 12 takes effect August 2, 2026 for high-risk systems and requires automatic event recording over the system lifetime, identification of the natural persons involved, and retention for at least six months. GDPR Article 22 covers automated decision-making and applies to any analytics workflow whose output influences a decision about a data subject. Fannie Mae LL-2026-04 covers lender adoption and audit obligations for AI used in mortgage origination. NIST AI RMF MANAGE 1.3 requires evidence that AI risks are tracked and managed across the system lifecycle.

The audit record format the analyst workflow has to produce carries the same seven fields regardless of which regulation surfaces the request. The record carries the natural-person identifier the analyst authenticated with, the analyst session identifier, the prompt-to-SQL route, the data classification the prompt and result-set carry, the policy version that evaluated the request, the decision outcome (allow, modify, redact, or block), the upstream model and version, and the integrity metadata that proves the record was not altered after the fact.

The record series satisfies EU AI Act Article 12, GDPR Article 22, Fannie Mae LL-2026-04, NIST AI RMF MANAGE 1.3, and ISO 42001 record-keeping obligations from the same pipeline. End-to-end inspection-layer overhead measures under 50 ms in production, which matters because the analyst's experience already absorbs 800 ms to 3 seconds of LLM response time per turn.

The architectural pattern that closes the gap

The pattern places an inspection layer between the analytics tool and the LLM endpoint, and a second inspection layer between the LLM-generated SQL and the warehouse. Both layers read the analyst's identity from the propagated SSO context. The first layer evaluates the prompt-classification, schema-trim, and result-redaction decisions. The second layer evaluates the SQL-inspection and row-volume decisions. Both layers commit per-decision audit records to a tamper-evident store with hash chaining across records.

The inspection layers are stateless. They evaluate each request against the analyst's identity, the policy version of record, and the data the request carries. The audit record series is the deliverable the regulator reads. The analytics tool keeps its own application logs for operational reasons. The two record series compose: the application records cover the tool's internal state, the inspection records cover the regulatory obligations.

DeepInspect

DeepInspect is the inspection layer the architectural pattern requires. The product sits between the analytics tool and the LLM endpoint, reads the analyst's identity from the SSO propagation, commits the five prompt-to-SQL decisions and the result-set decision per request, and writes the seven-field audit record to a tamper-evident store with hash chaining across records. The same product covers the SQL-inspection and row-volume decisions at the warehouse boundary.

If your product analytics team has moved exploration onto LLM-generated SQL and the audit reviewer asks who saw what customer data through the analyst workflow, the AI Readiness Check walks the deployment and identifies the inspection-layer gaps in 20 minutes.