Feature · Response Inspection

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AccuroAI/Feature

Catch leaks and hallucinations on output.

The risk doesn't end when the prompt is sent. AccuroAI inspects every model response inline — stopping leaked credentials, hallucinated customer data, and policy violations before they reach your employee.

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Feature · Response Inspection — AccuroAI dashboard

Sound familiar?

The problems this exists to solve.

The leak can come back out

Models can reconstruct sensitive data from context or training — the risky text isn't always the one the employee typed.

Outputs travel unreviewed

AI responses get pasted into tickets, emails, and docs in seconds. Whatever was wrong with them ships too.

Injection speaks through output

A poisoned document's real payload is often the response — an exfil link or instruction the user is about to trust.

Capabilities

Built for enterprise AI.

Output DLP

Scans model responses for PII, credentials, customer records, and source code that the model may have generated or reconstructed.

Hallucination flagging

Detects fabricated names, email addresses, phone numbers, and financial figures — surfaced to the user with a confidence indicator.

Toxic content filtering

Harmful, offensive, or policy-violating content blocked or redacted before it renders. Configurable by team and use case.

Cross-model consistency

Same inspection policy applied to ChatGPT, Claude, Gemini, Copilot, and internal models. No blind spots as you switch providers.

Policy-driven actions

Block, redact, warn, log — or trigger a human review queue. Actions configurable per classifier, per team, per data type.

Full response audit trail

Every inspected response logged alongside the corresponding prompt. Reproducible evidence for compliance and incident review.

In practice

How it works for your team, day to day.

The return path is inspected too

Response Inspection scans model output before it renders — the same classifier set that guards prompts, pointed at what comes back. Reconstructed PII, credentials in generated code, and policy-violating content are caught on arrival, not after they've been pasted into a ticket.

Same 40+ classifiers applied to model output

Catches reconstructed and hallucinated sensitive data

Runs before the response reaches the employee

Injection outcomes get intercepted

When an upstream injection survives to the output stage — a crafted link, an instruction to forward data, a fabricated 'system message' — response inspection is the second tripwire. The payload is stripped or the response blocked, and the session is flagged for review.

Malicious link and instruction patterns detected

Sessions flagged and risk-scored on output events

Pairs with prompt-side injection defense

One policy engine, both directions

Inbound rules live in the same policies as outbound ones, with the same actions — redact, warn, block — and the same evidence trail. Auditors see one coherent control over the full exchange, not two half-measures.

Unified policies across prompt and response

Redact, warn, or block on output

Full-exchange audit records

FAQ

The questions we hear most.

Does this add latency to streamed responses?

Inspection is incremental and stays within the sub-38ms envelope — streaming UX is preserved.

Can it tell truth from hallucination?

No tool can adjudicate truth. What it detects is patterns: sensitive-data shapes, credential formats, unsafe instructions, and policy-violating content in output.

What actions are available on output?

The same set as prompts: redact inline, warn the user, block the response, or flag the session — per policy.

Is output logged like prompts are?

Yes — response events carry the same classifier, policy, and verdict metadata, so the audit trail covers the whole exchange.