The classic trap
Recital 155 clarifies Articles 72 and 73 of the AI Act on post-market monitoring and serious incident reporting. In practice, providers of high-risk AI systems design a beautiful risk management system before market placement, then abandon it once the product is deployed. The EU AI Office and market surveillance authorities (Luxembourg designation pending) expect a living mechanism that captures real-world usage experience, detects continuous learning drift, and triggers reporting within 15 days (72 hours for critical infrastructure or serious health impacts) as soon as a serious incident occurs. Without traceability, sanctions under Article 99 apply (up to 15M EUR or 3% of global turnover).
What the recital concretely imposes on your framework
- A documented post-market monitoring plan, proportionate to the risk level of the system and its usage context.
- Explicit analysis of interactions with other AI systems, devices and downstream software (value chain).
- A drift detection mechanism for systems that continue learning after deployment (model drift, data drift, concept drift).
- Express exclusion of sensitive operational data of deployers that are law enforcement authorities.
- A structured feedback channel from deployers to provider, and from provider to competent authority.
- Automatic severity qualification: death, serious health damage, critical infrastructure disruption, fundamental rights violation, property or environmental damage.
The specific trap of continuous learning
For systems that retrain in production (continuously fine-tuned LLMs, monthly retrained credit scoring models, RLHF recommendation engines), Recital 155 imposes heightened vigilance. A model compliant at market placement can become non-compliant three months later without a single line of code changing. Proof of compliance must be continuous, not point-in-time.
How Luxgap automates this risk
Our Luxgap AI Drift Sentinel transforms the declarative obligation of post-market monitoring into timestamped technical evidence, enforceable before the EU AI Office and the Luxembourg market surveillance authority. The agent connects directly to your MLOps pipelines (MLflow, Azure ML, Vertex AI, AWS SageMaker, Databricks) and your product telemetry stack (Datadog, Grafana, Sentry) to capture drift signals in real time, without asking your data scientists to fill in a single form.
- Automatically detects statistical drift (population stability index, KL divergence, accuracy decay) on each production model and triggers Teams or Slack alerts as soon as a critical threshold is crossed.
- Classifies each reported incident against the Article 3(49) AI Act grid (death, health, critical infrastructure, fundamental rights, environment, property) and pre-calculates the applicable notification deadline (72 hours or 15 days).
- Maps the downstream AI interaction chain (which system your model feeds, which libraries it integrates, which APIs it exposes) to materialize the analysis required by Recital 155.
- Auto-generates the pre-filled serious incident report in the format expected by the EU AI Office, with cryptographic timestamping and sealed chain of evidence.
- Natively excludes data flows tagged as law enforcement from the telemetry scope, in line with the Recital 155 exemption.
- Produces a continuous post-market monitoring log, enforceable during an inspection, demonstrating Article 72 compliance throughout the system's lifecycle.
Available as a complement to a Luxgap CISO or DPO mandate or as a dedicated SaaS module depending on your scope. Request a personalized quote and our teams will prepare a demonstration on your real production models, with a free 48-hour blank audit to measure your drift exposure before any engagement.
