Observability & Monitoring

StackSeal is the observability framework for AI compliance. Ingest metrics from your AI systems — model performance, data quality, operational telemetry, and human oversight actions — and correlate them with regulatory obligations in real-time. Build programmatic regulatory oversight into your infrastructure with the SDK, or deploy agents alongside your serving layer for zero-code monitoring.

StackSeal

Compliance monitoring, not just performance monitoring

Traditional observability tracks whether your system is up. StackSeal tracks whether your system is compliant — in real-time, with cryptographic evidence.

Real-Time Compliance Monitoring

Continuous

StackSeal monitors your AI systems against their documented regulatory posture in real-time. Performance metrics, output distributions, and behavioural patterns are continuously compared against the baselines established during the TRACE Architect phase. When a system deviates from its documented behaviour, you know immediately — not at the next quarterly review.

Drift Detection

Automated

Model performance drift, data distribution shift, and output deviation from expected ranges — detected automatically with configurable thresholds. StackSeal distinguishes between natural drift (gradual distribution changes) and anomalous drift (sudden behavioural changes that may indicate data quality issues, adversarial inputs, or model degradation).

Divergence Alerting

Post-Quantum

When an AI system's operational behaviour diverges from its documented intended purpose, StackSeal raises a regulatory divergence alert. This is distinct from performance drift — divergence means the system is doing something different from what the documentation says it does. Post-quantum cryptographic verification ensures alert integrity cannot be compromised.

Cryptographic Evidence Chain

Tamper-evident

Every monitoring observation, alert, and response action is recorded in a cryptographically chained evidence log. Post-quantum cryptographic signatures ensure that the monitoring record cannot be tampered with retroactively — critical for regulatory investigations and audit proceedings.

Metrics Ingestion

What StackSeal monitors

Six categories of metrics ingested from your AI infrastructure — each correlated with the regulatory obligations they satisfy.

Model Performance Metrics

Accuracy, precision, recall, F1 score, AUC-ROC, inference latency, confidence distributions. Connect your model serving infrastructure and StackSeal ingests performance data continuously.

Data Quality Signals

Input data distribution statistics, feature drift scores, missing value rates, out-of-distribution detection. Monitor the data flowing into your models against the training data profile.

Operational Telemetry

Request volumes, error rates, timeout frequencies, resource utilisation, queue depths. Operational health correlated with compliance obligations — a degraded system may not meet Article 15 accuracy requirements.

Human Oversight Actions

Override decisions, escalation triggers, manual review outcomes, feedback signals. Track how human oversight is actually exercised — critical evidence for Article 14 compliance.

Incident Signals

Anomaly scores, safety boundary violations, fairness metric deviations, unexpected output patterns. Early warning signals that may indicate a serious incident requiring Article 73 reporting.

Business Outcome Metrics

Downstream business metrics correlated with AI system behaviour. Customer satisfaction, decision accuracy, process efficiency — ensuring AI systems deliver their intended business value within regulatory boundaries.

SDK & APIs

Build oversight into your infrastructure

Three integration paths — from a Python SDK for ML pipelines to infrastructure agents that require no code changes.

Python SDK

Primary

Full-featured Python SDK for ML pipelines. Instrument your training loops, inference servers, and data pipelines with a few lines of code. Automatic metric collection for popular frameworks — PyTorch, TensorFlow, Hugging Face, scikit-learn. Async by default for zero-overhead production deployment.

REST & gRPC APIs

Universal

Language-agnostic APIs for any platform. Push metrics from Java, Go, Rust, or any language with HTTP or gRPC support. OpenTelemetry-compatible metric format for organisations already using observability infrastructure. Bulk ingestion endpoints for batch processing pipelines.

Infrastructure Agents

Deploy & Forget

Lightweight agents for Kubernetes, Docker, and cloud platforms. Deploy alongside your AI serving infrastructure — the agent auto-discovers model endpoints, collects inference metrics, and forwards to StackSeal. No code changes required for supported serving frameworks (TorchServe, Triton, Seldon, KServe).

Regulatory Mapping

Every metric maps to an obligation

StackSeal doesn't just collect metrics — it correlates them with the specific regulatory obligations they evidence.

Regulatory Obligation	StackSeal Monitoring
Art. 9 — Risk management system	Continuous risk metric tracking, threshold alerting, automatic risk score updates
Art. 14 — Human oversight	Override action logging, escalation tracking, oversight exercise evidence
Art. 15 — Accuracy, robustness, cybersecurity	Performance metrics, adversarial input detection, security event correlation
Art. 72 — Post-market monitoring	Systematic data collection on AI system performance throughout lifecycle
Art. 73 — Serious incident reporting	Automated incident detection, severity classification, 72-hour notification workflow
GDPR Art. 35 — DPIA monitoring	Processing activity logging, data subject impact tracking, consent compliance