Bias accumulates slowly. Hallucinations compound through pipelines. Models drift from the behaviour you validated.

But your gateway logs everything as normal. By the time a failure surfaces in a regulator's letter, a legal claim, a board incident review, the evidence of what went wrong is scattered across thousands of unlogged inference cycles.

You can't reconstruct it. You can't explain it. You can't prove it won't happen again.

Monitors your AI systems from the inside.

Not what goes in and comes out — what happens in between, where real failures occur. Every decision pathway logged. Every anomaly flagged. Every incident documentable with forensic precision.

This is what audit-ready looks like in practice.

Who this is for

Regulated industries where AI decisions carry legal, financial, or safety consequences. Organisations deploying high-risk AI under EU AI Act Annex III. Risk and compliance functions that need to brief boards, satisfy external auditors, and respond to regulators without scrambling. Technology teams that need to demonstrate ongoing model integrity, not just initial validation.

What you get

Everything in Sentinel Lite, plus internal process audit, bias detection in reasoning. Not just output, hallucination monitoring with cascade analysis, full MTL audit trail with hash-verified traceability, custom sector framework integration, multi-jurisdiction regulatory reporting, advanced forensic analysis, and dedicated compliance support.

On-premise deployable. Your data stays in your infrastructure.

Investment

Pricing on request. Dedicated support and SLA included.

Sentinel Complete operates at the inference layer, not the gateway. It captures the internal reasoning process of your AI systems using Multi-Trace Logging (MTL). A structured, append-only evidence chain designed for regulatory scrutiny.

Each frame captures session ID, trace reference, applicable framework, status, confidence score, and a structured metadata payload. Frames are SHA-256 hashed and chained — any tampering is detectable. The chain can be independently verified by an external auditor without access to internal systems.

Operates on decision pathway data, not output classification. Detects distributional drift in reasoning patterns across protected attribute proxies. Configurable thresholds per use case and jurisdiction.

Structural signature analysis across inference outputs. Cascade detection identifies amplification patterns where a single hallucination propagates through downstream pipeline steps. Each detected event is logged with its originating trace.

Continuous comparison of current inference behaviour against a validated baseline snapshot. Drift metrics per model version, configurable alerting thresholds, rollback evidence generation.

Rule sets available for Finance (MiFID II + DORA), Healthcare (MDR + GDPR Art. 9), HR/Recruitment (AI Act Annex III), Government (Awb + Algoritmeregister), Energy (NIS2 Critical Infrastructure), Insurance (Solvency II). Custom framework development available.

On-premise (all processing within your infrastructure), cloud-native (Azure, AWS, GCP), hybrid, air-gapped for classified environments.

Real-time dashboard, structured audit reports per regulatory framework, board-level summary, incident reconstruction reports, regulator-ready evidence packages.

When a regulator, auditor, or court asks why your AI decided what it did, Sentinel Complete has the answer ready. Before they ask.

 

Request a demoDownload: The AI Gateway Blindspot (technical brief)