v1.0, June 2026
A practitioner's framework for putting LLMs into production in KRITIS-regulated environments — observability, governance, MLOps, and platform self-service.
3 min skim · 5 min full read
Enterprise teams in energy, insurance, and telecom are moving from AI pilots to production systems that serve millions of customers under KRITIS and GDPR constraints. This playbook captures five principles that recur across regulated platform engagements — not generic advice, but patterns validated in production.
Principle 1
Structured logs, metrics, and traces per inference — not after the first incident.
KRITIS environments cannot debug AI in production by reading raw prompts in a log aggregator. Every LLM call needs correlation IDs, latency histograms, token usage, and output-quality signals wired into the same observability backbone your platform team already trusts. Instrumentation is a release gate, not a backlog item.
Principle 2
OpenTelemetry as the single standard across IT, OT, and application tiers.
Fragmented monitoring tools create alert fatigue and make AI-assisted diagnostics impossible — models cannot reason across signals with inconsistent naming. Consolidate traces, metrics, and logs on one vendor-neutral backbone. Semantic conventions matter as much as the platform choice.
Principle 3
BSI, NIS2, and DSFA requirements shape design decisions — not post-launch audits.
Regulated enterprises treat AI as critical infrastructure once it touches customer or grid data. Build compliance into architecture reviews: data residency, human-in-the-loop guardrails, model promotion gates, and audit trails. Governance that arrives after deployment costs quarters, not sprints.
Principle 4
MLOps pipelines with automated testing — and explicit escalation paths for edge cases.
Model deployment in regulated environments needs automated regression suites, canary promotion, and rollback playbooks. Agentic diagnostics can accelerate root-cause analysis, but remediation paths that touch production systems require human approval. Speed and safety are not trade-offs when the pipeline enforces both.
Principle 5
Templates and golden paths so teams scale without bottlenecking on a central platform group.
AI-native operations fail when every team reinvents collectors, SLOs, and deployment patterns. Publish reusable onboarding playbooks: standard dashboards, alert routing, and environment provisioning that teams can adopt in days. The platform team sets standards; product teams own their services.
These principles were developed through embedded platform leadership at E.ON (KRITIS energy, 2M+ monthly LLM interactions), Allianz (70+ country cloud platform), and Telefónica (unified customer data platform).
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