AI Governance & Compliance Services for Regulated AI
Reviewed by Umar Abbas • CTO & Principal AI Architect
Last reviewed: 14 August 2026
AI governance and compliance makes your AI auditable and lawful. We map systems to the EU AI Act and ISO 42001, classify their risk, build model cards and audit trails, and design the human oversight regulators expect. The result is documentation and controls that let you deploy AI in a regulated environment with confidence.
Documentation an auditor can read
Compliance is not a slide that says you care about ethics. It is a risk classification, a paper trail, and controls a regulator can inspect and a court can rely on.
Risk classification
Place each system in its EU AI Act category, because every obligation follows from that.
Gap analysis
Measure each system against the EU AI Act and ISO 42001 and list what is missing.
Model cards & audit trails
Transparency documentation and logging that record what a model is and what it did.
Human oversight design
Real controls to review, override, or stop the system, matched to human-in-the-loop gates.
From classification to a maintained control set
Compliance is not a one-time report. Classify, close the gaps, document, and maintain as the system changes. The maintenance loop is what keeps you compliant after the audit, not just for it.
The dashed return loop is the point most programs miss: a system that changes without its documentation changing is no longer the system that was assessed.
How we deliver a governance engagement
Run under our core engineering process. Classification first, because every obligation and deadline follows from where a system sits.
1. Inventory and classify
List your AI systems and place each in its EU AI Act risk category, the step everything else depends on.
2. Gap analysis
Compare each system to the EU AI Act and ISO 42001 requirements and record every gap with its risk.
3. Document and remediate
Build model cards, audit trails, and oversight controls, and prioritize fixes by risk and deadline.
4. Set up maintenance
Define who keeps documentation current as systems change, so compliance survives past the first audit.
Obligations follow classification
The single most consequential step is getting the risk category right. Everything you must do, and the penalty for not doing it, flows from that classification. Guess it and you either over-build or miss a legal requirement.
{{TODO: publish an anonymized gap-analysis summary and remediation timeline from an engagement}}
Compliance is maintained, not filed
A system that changes after its assessment is no longer the system that was assessed. Documentation that is not maintained is documentation that is wrong.
Frameworks & standards
Oversight built into agent workflows and tool access over the Model Context Protocol.
Industries where AI is regulated
Governance matters most where AI decisions affect people’s money, health, or rights, and where a regulator can ask to see your records.
High-risk classification, model cards, and audit trails for decision systems.
Human oversight and data governance where AI touches patient outcomes.
See every sector where we build AI governance.
Case studies
Governing a Live AI Pipeline
Risk classification, model cards, and audit trails retrofitted onto a document system in production.
Read Case Study →More production systems
Browse builds delivered with governance and documentation from the start.
View Case Studies →What goes wrong with AI governance
1. Ethics theater
The failure: A principles document is published with no controls behind it, which fails the moment it is tested.
Our prevention: Concrete controls, documentation, and logging an auditor can inspect.
2. Wrong risk classification
The failure: A high-risk system is treated as limited, so mandatory obligations are simply missed.
Our prevention: Classify carefully first, since every obligation depends on it.
3. Nominal human oversight
The failure: A rubber-stamp approval step is called oversight but gives a person no real control.
Our prevention: Oversight controls that can genuinely review, override, or stop, with logs.
4. Documentation that drifts
The failure: Records are written once, the system evolves, and the paperwork no longer matches reality.
Our prevention: A maintenance process tied to change, so docs track the system.
Where this service starts and stops
For the runtime technical defenses such as prompt injection and guardrails, see AI security. For a broad architecture audit, see AI consulting. For where governance fits your wider plan, see AI strategy and roadmap. This page is the policy, documentation, and regulatory layer.
Terms used on this page
Frequently asked questions
What does the EU AI Act require of us?↓
It classifies AI systems by risk and imposes obligations that rise with the risk level. High-risk systems need risk management, data governance, technical documentation, human oversight, and conformity assessment. The first step is knowing which category each of your systems falls into, because the obligations, and the penalties for missing them, follow directly from that classification.
How is governance different from AI security?↓
Security is the technical layer that stops attacks at runtime, such as prompt injection defense and guardrails. Governance is the policy and documentation layer: risk classification, audit trails, model cards, and regulatory mapping. You need both. This page covers making the system lawful and auditable; the runtime defenses live under AI security, and the two connect.
What is a model card and why does it matter?↓
A model card documents what a model does, its data, its limits, and its intended use, in a form auditors and stakeholders can read. It matters because regulators increasingly expect this transparency, and because it forces honest answers about a model's boundaries. We generate model cards as part of the documentation set the EU AI Act and ISO 42001 call for.
Do we need ISO 42001 as well as EU AI Act work?↓
They serve different purposes. The EU AI Act is law you must comply with if you operate in scope. ISO 42001 is a management-system standard you can certify against to show a structured approach to AI governance. Many organizations pursue both: the Act for legal compliance, the standard to demonstrate maturity to customers and partners.
How do you design human oversight?↓
We identify the decisions a person must be able to review, override, or stop, then build those controls into the system with logging so the oversight is real, not nominal. Regulators look for meaningful human control, not a rubber-stamp step. We connect this to the human-in-the-loop gates in the system itself, so the documentation matches how it actually runs.
Can you help if our AI is already in production?↓
Yes, and that is often the more urgent case. We assess deployed systems against the relevant framework, find the gaps, and prioritize remediation by risk and deadline. Retrofitting governance is harder than designing it in, but a documented gap analysis with a remediation plan is exactly what a regulator or auditor wants to see you acting on.
How does this connect to GDPR?↓
AI governance and data protection overlap wherever a model touches personal data. We align the two, covering lawful basis, data minimization, and subject rights alongside the AI-specific obligations, so you are not solving them separately. Where the model processes personal data, the GDPR requirements and the EU AI Act requirements are handled as one coherent set of controls.
What do we actually receive?↓
A risk classification for each system, a gap analysis against the EU AI Act and ISO 42001, model cards and technical documentation, an audit-trail and human-oversight design, and a prioritized remediation plan. The deliverables are built to be shown to an auditor or regulator, and to be maintained as your systems change, not filed once and forgotten.
Make your AI audit-ready
Book a 45-minute session. We will classify one of your systems against the EU AI Act and show you the gaps that matter most.
Book a Compliance Review