P95 policy overhead
Private-preview target for lightweight rules; model-assisted detectors are measured and reported separately.
Private preview
AI runtime governance
Set boundaries for models and agents—and retain context for what they do.
Poxek LLM Firewall is a model-agnostic policy and observability layer for prompts, responses, retrieved context, and agent tool calls. Teams can begin in observe mode, tune workload-specific thresholds, and move selected rules to redact, challenge, require approval, or block while retaining an explainable decision trail.
Poxek LLM FirewallContext graph / live
Private-preview benchmark
Targets for the private preview.
These figures are private-preview evaluation targets—not measured customer results, guarantees, or contractual SLAs.
Policy decision trail
Every allow, redact, challenge, approval, or block decision should retain a reason and policy version.
Graduated enforcement
Observe, redact, challenge, require approval, and block let teams tune controls before hard enforcement.
Poxek LLM Firewall
Capabilities
Bidirectional inspection, sensitive-data controls, capability boundaries, and evidence for production LLM applications and agents.
Bidirectional AI inspection
Evaluate prompts, model outputs, retrieved documents, URLs, and tool arguments for injection, jailbreak, unsafe content, malicious links, and sensitive-data exposure.
Workload and tool policy
Define allowed models, destinations, data classes, tools, arguments, users, budgets, and approval gates per application instead of relying on one global threshold.
Explainable enforcement
Attach the policy version, matched signal, confidence, action, latency, and redaction record to every decision, with privacy-aware payload retention controls.
Operating flow
Evidence moves with the decision.
Describe workload boundaries
Observe model and agent traffic
Evaluate activity against policy
Escalate explainable events
Operational use cases
Where the workflow should earn its place.
Govern agent tool use
Constrain which identities can call high-impact tools, validate arguments and destinations, and require human approval for destructive or externally visible actions.
Protect sensitive context
Detect credentials, regulated data, and internal identifiers in prompts, retrieved content, and responses; redact or deny them according to workload policy.
Investigate prompt injection
Link direct and indirect injection signals to retrieved documents, model responses, tool calls, and the final policy action without treating a detector score as proof by itself.
Outcome
A tunable runtime control plane that limits model and agent actions while giving application, platform, and security teams the evidence to investigate each decision.
Private preview
Bring the perimeter and the response into the same conversation.
Tell us which operating constraint you need to examine. Private Preview discussions begin with scope, governance, and evaluation context.