Prompt firewalls — Use case

Block jailbreaks, redact PII, detect injection, and enforce policy on every prompt and every completion — before either touches the model or the user.

AI teams · Safety · For AI teams

Inbound and outbound. Prompt and completion. Same firewall.

Apinizer's AI Gateway applies policy on the way in and on the way out — jailbreak detection, PII redaction, injection scoring, regex denylists, and policy-driven blocks — without changing the application.

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The problem

The problem

LLM safety isn't a model property. It's a runtime property.

Every model has a jailbreak; every prompt can carry PII; every completion can leak. The fix isn't waiting for a perfect model — it's putting a firewall in front of every model and a filter in front of every completion. Apinizer's AI Gateway does both: detect, redact, score, block, audit. Same plane as the API.

Capabilities

Jailbreak detection

Preset pattern libraries score every inbound prompt, and an external guardrail engine — an LLM-as-judge endpoint, or any OpenAI-compatible judge (vLLM, Ollama) — gives a second opinion when a pattern is not enough. Run it inline, async, or in shadow mode while you tune it.

PII redaction

Twelve built-in identifier types — TCKN, IBAN, Turkish phone, card, email, IP, passport, SSN, and more — validated by checksum before masking, so real identifiers get redacted and ordinary numbers do not. Applied to the prompt on the way in and the completion on the way out, including across streaming chunk boundaries.

Conversation-integrity checks

Forged role sequences and model control tokens are caught structurally before the model sees a tampered conversation. MCP and A2A tool results run through the same masking pipeline as prompts, so a poisoned tool response is not a side door.

Outbound content filters

DLP presets catch credentials and secrets in both directions, and your own regex and element rules catch whatever else must not leave the model. A block sticks across the whole exchange, not just the offending chunk.

Policy as data

Firewall rules ship as data, not code. Review in Git, apply via APIops, propagate to every Worker in seconds.

Audit and explainability

Every block and redaction captured with reason, score, and policy reference. Auditors and developers see the same explanation.

Real-world examples

Banking

Scenario: Istanbul bank blocks account-number leaks in chatbot completions

Outcome: Outbound filter detects 16-digit patterns adjacent to keywords. Blocked completions log an explanatory event; the user gets a safe fallback message.

Healthcare

Scenario: Munich hospital redacts patient identifiers before LLM ingest

Outcome: Inbound redaction strips names, IDs, dates. The model summarizes; the completion is re-keyed back to the patient on the gateway side, never inside the model.

Metric: 0 PHI to model

Public sector

Scenario: Paris agency scores jailbreak attempts in real time

Outcome: 1.8% of citizen-chatbot prompts flagged jailbreak-suspicious. Half rerouted to a hardened model with a stricter system prompt; half blocked outright.

Insurance

Scenario: Madrid insurer detects RAG-injection in customer documents

Outcome: Documents uploaded by customers occasionally carry 'ignore previous instructions' patterns. Scorer blocks the prompt; SOC reviews the document offline.

Media

Scenario: Milan publisher prevents source-code leaks in AI-assisted editor

Outcome: Outbound filter blocks any response containing API keys or repo paths. Editorial productivity unchanged; risk posture significantly improved.

Telecom

Scenario: Amsterdam carrier enforces locale-specific PII rules

Outcome: Each jurisdiction's national identifiers — tax IDs, citizen numbers, social-security formats — redacted with the right pattern in the right locale. Same firewall, different rules per region.

Energy

Scenario: Prague utility blocks tool-poisoning attempts on operations agents

Outcome: An adversarial document tried to coerce an operations agent into changing SCADA parameters. Injection scorer caught it; the agent never saw it.

Government

Scenario: Caspian-region ministry runs prompt firewall in front of the national chatbot

Outcome: Per-locale PII patterns, jailbreak rules, and outbound denylist tuned for the local language. The compliance officer signs the audit pack without changes.

Recommended modules

AI Gateway — Prompt firewall built in — jailbreak detection, PII redaction, injection scoring, outbound filters.
Analytics Engine — Blocks and redactions appear in the same telemetry as cost and latency.
Monitoring — Severity-aware alarms when block rate spikes or new jailbreak patterns appear.
Identity Manager — Tie firewall outcomes to consumer identity — repeat offenders revoked at the auth layer.

Resources

Prompt firewall overview — Inbound and outbound rules, scoring blends, policy-as-data — how the firewall composes.
AI Gateway — The lane the firewall runs on — alongside routing, caching, and audit.
Monitoring — Anomaly detection on block rates and emerging patterns.
APIops manifests — Firewall rules ship as data, review in Git, apply idempotently.
Compliance lane — How firewall outcomes feed KVKK / GDPR / BDDK evidence.
Architecture overview — Where the firewall sits in the AI lane.

MCP server governance — For AI teams
AI observability — For AI teams
KVKK / GDPR / BDDK compliance — For executives
Agent-to-Agent (A2A) — For AI teams

Next step

Safety as a runtime property

Block before the model. Filter before the user.

A 30-minute walkthrough — jailbreak, PII, injection, outbound filters — on a Kubernetes of your choice.

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