AI Control Plane for Regulated Finance
GenAI under DORA needs a control layer. Intellectum Lab AI Control delivers auditability, reproducibility, and exit readiness - turning black boxes into governed systems.
2026 is the first full year under DORA. Audit questions have shifted from "why GenAI?" to "how will you prove it works safely?"
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.
01 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.
02 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. "We'll rewrite it in a week" won't survive scrutiny.
03 Shadow AI & Register Gaps
Unofficial GenAI usage emerges naturally. Under DORA, if a service is being used but not recorded, the register is incomplete and the risk isn't controlled.
04 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, audit becomes guesswork.
05 Model Drift Goes Undetected
LLM providers update models silently. Yesterday's validated output becomes today's compliance gap. Without version tracking, you can't prove the model behaves as it did during assessment.
06 Incident Root Cause Is Untraceable
When a GenAI-powered decision leads to customer harm or breach, you need to reconstruct exactly what happened. Without per-request logging, 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.
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
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. 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
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
How AI Control Maps to DORA Requirements
ICT Risk Management
Third-Party Risk
Exit Strategies
Register of Information
Incident Reporting
Testing & Resilience
Where AI Control Sits
A control plane layer between your applications and GenAI services.
See How 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 AI Control turns a "black box" into a governed system with a clear audit trail.
Request Architecture Review