What is automated regulatory compliance?
Automated regulatory compliance is the use of software to evaluate an organisation’s policies, controls, data and system behaviour against regulatory requirements expressed as machine-executable rules. Instead of periodic manual reviews, checks run continuously or on demand, each finding cites the requirement and source provision it relates to, and results feed existing governance, risk and compliance processes. It supports compliance teams; it does not replace their judgement, and it makes no legal claims.
How AI supports regulatory compliance
AI helps where the work is reading and interpreting: extracting requirements from regulatory text, mapping an organisation’s policies to those requirements, and spotting where a policy addresses a topic without meeting the requirement. AI is less suited to the decision itself, where the same inputs must give the same answer every time and the answer must be explainable to an auditor.
NormLogic uses AI for extraction, classification and mapping, structured regulatory models for validation and consistency, and formal rules for the deterministic evaluation. Every finding carries provenance to the regulation and to the document checked.
What the NormLogic compliance checker does
- Reads policies, procedures, control descriptions, contracts and evidence.
- Maps them to the structured requirements of the regulations that apply, for example DORA, MiCA, PSD2, GDPR, NIS2 or the EU AI Act.
- Assesses coverage per obligation and lists gaps, conflicts and unsupported claims.
- Attaches the source provision to each finding so it can be verified or challenged.
- Exports findings and remediation tasks to existing GRC tooling.
Real-time compliance checks and regulatory change
Because the rules are executable and linked to their sources, checks can run whenever a policy changes or a regulation is amended. Regulatory change management becomes a re-evaluation rather than a project: the affected requirements, policies and controls are identified from the model.
What automated compliance does not do
It does not make an organisation compliant, and it does not make legal determinations. It provides structured, traceable evidence about where policies and controls meet requirements and where they do not, so that people can decide faster and on a documented basis.
Frequently asked questions
Can NormLogic make us compliant with DORA or the EU AI Act?
No tool can make an organisation compliant. NormLogic supports traceability, structured regulatory reasoning and auditable workflows, and reports where policies and controls meet or miss requirements. Compliance decisions remain with the organisation and its advisers.
How is this different from a compliance chatbot?
A chatbot answers questions from text and cannot show deterministic evidence. The NormLogic compliance checker evaluates documents against formal rules and returns findings with the source provision attached. The result is a report that can be audited, not a conversation.
Which regulations are supported?
The approach is regulation-independent. Current work centres on European financial and digital regulation such as DORA, MiCA, PSD2, GDPR, NIS2 and the AI Act, and new regulations are modelled with the same pipeline.
Related topics
- What is machine-executable regulation?
Machine-executable regulation expresses requirements as formal rules software evaluates deterministically, with provenance to the source provision.
- What is auditable AI?
Auditable AI produces conclusions that can be inspected, challenged and reproduced, with traceability from decision back to the authoritative source.
- What is a regulatory digital twin?
A regulatory digital twin is a maintained, computable model of a regulatory environment that can be queried, tested and updated when legislation changes.
- NormLogic, the platform
How Sireto applies Regulatory Engineering to machine-readable and executable regulation.
Check a policy against its regulation.
Bring one policy and the regulation it must satisfy. We will show the coverage, the gaps and the evidence behind each finding.