Your AI system failed during market hours. Can you prove you were prepared?
DORA requires financial entities to demonstrate operational resilience for all ICT systems — including AI. We build the infrastructure, monitoring, and governance that turns compliance from paperwork into reality.
DORA's Five Pillars Applied to AI Operations
Most financial institutions treat AI as a separate governance track. DORA doesn't. Here's how we align AI operations with each pillar.
ICT Risk Management
Model risk registers, AI-specific threat modelling, data pipeline resilience assessments, and automated risk scoring for every AI workload.
Incident Management
AI incident classification taxonomy, automated detection and escalation, 4h/72h reporting templates, and post-incident model impact analysis.
Resilience Testing
Chaos engineering for ML pipelines, GPU failover testing, model serving degradation drills, and annual threat-led penetration testing of AI endpoints.
Third-Party Risk
AI vendor dependency mapping, concentration risk scoring, exit strategy playbooks, and contractual provisions for model API SLAs and data handling.
Information Sharing
Threat intelligence feeds for AI-specific attacks (prompt injection, model extraction, data poisoning) shared across your financial services network.
Real Problems We Solve
These aren't hypotheticals. They're scenarios we've seen in financial services AI deployments.
Your AI system went down during market hours
No failover. No incident classification. The regulator wants your response timeline.
Our approach: Automated incident detection, classification, and reporting workflows that meet DORA's 4-hour initial notification and 72-hour intermediate report requirements.
Your ML pipeline failed and nobody noticed for 3 days
Model drift, stale predictions, no alerting. Decisions were made on degraded outputs.
Our approach: Continuous model monitoring with automated drift detection, data quality checks, and escalation paths — integrated with your ICT risk management framework.
The regulator asked for your AI vendor risk assessment
You have 6 AI vendors. No concentration risk analysis. No exit strategies documented.
Our approach: Third-party AI risk register with vendor dependency mapping, concentration risk scoring, exit strategy playbooks, and contractual DORA provisions.
Your resilience test plan doesn't cover AI systems
You test infrastructure failover, but not model serving, feature stores, or inference pipelines.
Our approach: DORA-aligned resilience testing programme covering AI-specific failure modes: model serving degradation, feature store corruption, inference latency spikes, and GPU cluster failover.
Your AI governance and DORA compliance are in separate silos
AI governance team reports to CDO. DORA compliance reports to CISO. Neither talks to the other.
Our approach: Unified operational resilience framework that maps AI governance controls to DORA's five pillars, with shared dashboards and joint incident response procedures.
You can't prove your AI system's recovery time objective
RTO exists on paper but has never been tested for AI workloads. GPU provisioning alone takes hours.
Our approach: Documented and tested RTOs for every AI workload, including GPU cluster recovery, model redeployment, feature store rebuild, and inference pipeline restart sequences.
Built for Multi-Regulation Compliance
Financial services AI doesn't exist in a single regulatory bubble. We design for all of them simultaneously.
Operational resilience, incident reporting, resilience testing, third-party risk
Risk classification, conformity assessment, transparency, human oversight
Cybersecurity risk management, supply chain security, incident notification
Data residency, DPIAs, consent management, right-to-erasure for ML data
Algorithmic trading controls, best execution monitoring, transaction reporting
ICT outsourcing requirements, cloud exit strategies, audit access
Frequently Asked Questions
What is DORA and how does it affect AI systems?
The Digital Operational Resilience Act (DORA) requires financial entities in the EU to ensure ICT systems — including AI — can withstand, respond to, and recover from disruptions. AI systems used in trading, credit scoring, fraud detection, or customer decisions fall under DORA's ICT risk management requirements.
When does DORA compliance become mandatory?
DORA applies from January 17, 2025. Financial entities must have ICT risk management frameworks, incident reporting procedures, resilience testing, and third-party risk management in place. AI systems deployed after this date must be DORA-compliant from day one.
How do you make AI infrastructure DORA-compliant?
We implement four layers: ICT risk management for AI (model drift monitoring, data pipeline resilience), incident classification and reporting automation, digital operational resilience testing (chaos engineering for ML pipelines), and third-party AI vendor risk management with exit strategies.
Does DORA apply to AI model vendors and cloud providers?
Yes. DORA's third-party risk management framework covers critical ICT service providers including AI model vendors, cloud infrastructure providers, and data providers. You need contractual provisions, exit strategies, and concentration risk assessments for every AI vendor in your supply chain.
What is the difference between DORA and NIS2 for AI systems?
DORA is sector-specific to financial services with prescriptive ICT risk requirements. NIS2 is cross-sector with broader cybersecurity obligations. Financial entities running AI must comply with both, but DORA's requirements are more detailed — especially for incident reporting timelines and resilience testing.
DORA compliance isn't optional. Your AI resilience shouldn't be either.
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