GenAI under DORA needs a control layer
Intellectum Lab AI Control delivers auditability, reproducibility, and exit readiness for GenAI in financial services — turning black boxes into governed systems with clear audit trails.
2026 is the first full year of operating under DORA. Audit questions have shifted from "why GenAI?" to "how will you prove it works safely?" Intellectum Lab AI Control is the mandatory observability layer that answers those questions.
Why GenAI pilots struggle under DORA
By 2026, many GenAI pilots suddenly look less like innovation and more like operational risk: hard to explain, difficult to reproduce, and impossible to defend in an audit.
Accountability doesn't transfer
Under DORA, responsibility stays with the financial entity — not the model provider. LLMs are non-deterministic by nature, and without the right architecture you cannot explain outcomes at the level expected by risk, compliance, and audit functions.
Multi-vendor in theory, lock-in in practice
Prompts tuned to specific model behaviour, embeddings tied to one provider, safety controls bolted on ad-hoc. When exit strategy becomes an exercise scenario, "we'll rewrite it in a week" won't survive scrutiny.
Shadow AI & Register gaps
Unofficial GenAI usage emerges naturally. Under DORA, if a service is being used but not recorded, the register is incomplete, you can't evidence control, and the risk isn't controlled.
Invisible subcontracting chains
GenAI supply chains are multi-layered: integrator, model provider, cloud platform, vector DB, moderation services. If you can't show which services participate in processing, audit becomes a guessing game.
Model drift goes undetected
LLM providers update models silently. Yesterday's validated output becomes today's compliance gap. Without continuous monitoring and version tracking, you can't prove the model behaves the same way it did during your last assessment.
Incident root cause is untraceable
When a GenAI-powered decision leads to customer harm or regulatory breach, you need to reconstruct exactly what happened. Without per-request logging and context capture, post-incident analysis becomes forensic guesswork.
Intellectum Lab AI Control
A mandatory observability and control layer that sits across GenAI systems, so DORA requirements can be evidenced — not just described. Intellectum Lab AI Control keeps GenAI in financial workflows controllable, delivering auditability, reproducibility, and exit readiness.
Per-Request Audit Trail
Full context capture for every GenAI interaction — making any material output reconstructable step by step.
- Who initiated the request
- What prompt was sent
- Which RAG sources were retrieved
- Generation parameters used
- Model version that ran
- Output produced
- Policy controls applied
Data Lineage & Dependency Mapping
Understand the actual processing chain and dependencies — critical for third-party and subcontracting oversight.
- External service mapping
- Vector database tracking
- Moderation service logs
- Orchestration layer visibility
- Model access paths
- Data flow documentation
Pre & Post-Generation Guardrails
Controls before and after generation — because in a DORA environment, a single incorrect customer response costs far more than a technical uptime metric.
- PII handling policies
- Restricted topic blocking
- Output format validation
- Business constraint enforcement
- Hallucination detection
- Commitment tracking
Model-Agnostic Exit Readiness
Exit readiness is the hardest part. Intellectum Lab AI Control supports architectures where switching models — or deployment mode — is technically feasible without rewriting business logic.
- Provider abstraction layer
- Prompt portability
- Embedding migration paths
- Cloud/on-prem flexibility
- Fallback configuration
- Exercise scenario support
Real-Time Monitoring & Alerting
Continuous oversight of GenAI system behaviour — detecting anomalies, quality degradation, and policy violations before they become incidents.
- Response quality scoring
- Latency & throughput metrics
- Model drift detection
- Cost attribution tracking
- Threshold-based alerting
- Dashboard visualization
Compliance Reporting & Documentation
Automated report generation for audits, risk committees, and regulators — evidence that's ready when asked, not assembled under pressure.
- Register of Information sync
- Audit trail exports
- Incident timeline reconstruction
- Third-party risk reports
- Policy compliance summaries
- Scheduled report generation
How Intellectum Lab AI Control maps to DORA requirements
Direct mapping between platform capabilities and regulatory expectations.
ICT Risk Management
Real-time monitoring, risk quantification, and evidence that GenAI systems operate within defined risk appetite.
Third-Party Risk
Complete visibility into ICT service providers, subcontracting chains, and concentration risk across GenAI dependencies.
Exit Strategies
Technical readiness to transition away from critical ICT providers without service degradation.
Register of Information
Current, accurate documentation of all ICT arrangements supporting critical or important functions.
Incident Reporting
Rapid root-cause analysis and evidence collection when GenAI-related incidents occur.
Testing & Resilience
Ability to test GenAI system behaviour under stress, model failure, and provider unavailability scenarios.
Where Intellectum Lab AI Control sits
A control plane layer between your applications and GenAI services.
Where Intellectum Lab AI Control applies
Any GenAI deployment that affects customers, operations, or risk needs a control layer.
Customer Service Chatbots
Track every customer interaction, ensure no unauthorized commitments, evidence policy compliance.
Analyst Assistants
Full audit trail of data sources, model outputs, and human review steps for investment decisions.
Document Generation
Reproducible document creation with versioned templates, input tracking, and output validation.
RAG / Knowledge Search
Track which sources were retrieved, what context was used, and how answers were generated.
A practical 2026 roadmap
Moving from GenAI pilots to production systems that survive audit scrutiny.
Classify Honestly
If it affects customers, operations, or risk — it isn't "just a pilot" anymore.
Add Control Early
Not after the MVP. As part of the architecture from the start.
Map Dependencies
Real calls, real services, real integrations — not just contracts in a folder.
Test Exit Scenarios
Not a document. A live exercise: what happens on day X?
Govern Shadow AI
Move value into secure, governed corporate workflows.
See how Intellectum Lab AI Control works on your architecture
We can walk through what this looks like on real architectures — RAG, agents, contact-centre flows, document systems — and show how Intellectum Lab AI Control turns a "black box" into a governed system with a clear audit trail.