AI Governance Consulting

Q: Which regulations does AI Governance cover exactly?

AI governance is primarily based on the European AI Regulation (AI Act), ISO/IEC 42001, and other references related to risk management, security, and data protection in AI and digital environments.

Q: What is the difference between the AI Act and ISO 42001?

The AI Act is a European regulation that sets legal obligations for certain artificial intelligence systems and uses. ISO/IEC 42001 is an international standard that defines how to implement and improve an AI system.

Q: What changed in August 2026, and what is postponed to 2027?

On August 2, 2026, most of the AI Act began to apply, including transparency obligations for chatbots, AI-generated content, and deepfakes. Its application to high-risk systems in Annex III is postponed until December 2, 2027, and until August 2, 2028 for high-risk systems integrated into products regulated by the safety legislation in Annex I.

Q: Do we need to have Data Governance already implemented?

No. Although it makes the work easier, a company can start defining its AI governance without having a data governance framework in place; both areas can be developed at the same time.

Q: Should AI Governance be an independent area or part of Data Governance?

It depends on each company’s data and risks. AI governance can be integrated into data governance, leveraging its policies and control mechanisms, or operate as an independent area.

Q: How are systems classified by risk level?

The AI Act defines four risk levels: unacceptable risk (prohibited practices), high risk (systems that may affect health and safety), limited risk (with transparency obligations), and minimal risk (no additional obligations specific to their category).

Q: How is bias audited in our models?

The audit is tailored to each organization and model. For in-house systems, we review the training data. For third-party models, we review data, prompts, outputs, and how the company uses them. Each finding is documented with corrective action and rechecked periodically.

Q: Who should take on oversight roles within my company?

Typically, an AI committee is created within the company to approve policies and make decisions on risks. In addition, each system has a trained accountable owner responsible for its operation and use.

Q: How do you ensure explainability and traceability in production?

To ensure explainability and traceability, it is necessary to document what each system does, what data it uses, how it generates outputs, and who is accountable for its oversight. It is also advisable to log model versions, changes, and incidents.

Deploying AI solutions without a clear governance framework multiplies legal, reputational, and operational risk. At LISDATA, we define policies, roles, and controls to manage the use of artificial intelligence in line with the European AI Regulation (AI Act) and ISO/IEC 42001.

We connect AI governance with your data governance to establish accountability, assess risks, and ensure that the use of artificial intelligence in the company is responsible, controlled, and auditable. Because a model is not under control if the data that feeds it is not governed.

Talk to an AI Governance Specialist

Why choose LISDATA?

At LISDATA, we see our AI governance services as an evolution of data governance. We combine our data management experience with technical expertise in delivering artificial intelligence projects.

AI governance connected to data governance

We start from a foundation we already know: data management. We extend data governance practices—quality, lineage, ownership, and security—to AI models to improve traceability and control. Because if data is not well governed, it is harder to control risks.

AI Act, risks, roles, and auditable controls

We translate the requirements of the European AI Regulation (AI Act) and other regulations into specific policies, responsibilities, and controls. We address areas such as risk classification, bias, reliability, transparency, explainability, and human oversight, tailoring measures to each project.

Governance tailored to the technology your company already uses

We design the AI governance framework with the company’s existing architecture and systems in mind, from cloud platforms to hybrid and legacy environments. We also address risks associated with using third-party AI tools, such as potential leaks of confidential information or intellectual property. This way, controls adapt to the organization’s technological reality without requiring system changes.

A team with experience in AI and data governance

The LISDATA team working on AI governance is the same team that delivers data governance projects. This experience allows us to connect policies and regulations with more technical aspects such as data quality and maturity, metadata, lineage, and security on AI platforms. In addition, we have a DAMA-certified team (International in Data Fundamentals).

An agile approach focused on implementation and results

We tailor the scope of AI governance consulting to the company’s size, objectives, and potential risks. We prioritize the solutions each organization needs and connect them with its existing processes, data, and systems, so AI governance translates into real actions and tangible results.

Ongoing support and training in data and AI

We train teams in AI and data in small groups so they can manage the technologies and apply AI governance practices autonomously, reducing dependency while maintaining control and best practices as their technology environment evolves.

aisQ AGENT, our proprietary AI governance platform

At LISDATA, we have developed our own platform for customized AI agents, asiQ AGENT, which enables companies to store corporate data, manage permissions, and track AI usage in a secure environment, making work easier and more streamlined.

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Why does AI Governance fail even when AI is already in use?

Using artificial intelligence tools does not mean the company has its risks under control. Many organizations launch AI projects without defining who is responsible for each system, what risks its use entails, or what controls are needed.

Without system classification, clear roles, and governance of the data that feeds the models, risks remain.

Legal and regulatory risks

Using AI systems without adequate control measures can expose the company to non-compliance with the European AI Regulation (AI Act) and other emerging AI regulations.

Bias and discriminatory decisions

Models can reproduce biases that lead to unfair decisions and harm employees or customers.

Lack of transparency and explainability

The company cannot justify how a decision was reached or which factors influenced a model’s outcome.

Unclear roles and responsibilities

There are no defined owners to oversee AI systems, assess their risks, and verify they are used in line with the organization’s policies.

Lack of traceability and control in production

There is no adequate tracking of the models’ lifecycle, changes, outputs, or the controls applied.

Ungoverned data

Information is fragmented, lacks clear ownership, or has quality issues. This makes it harder to control the data that feeds AI models.

Shadow AI and unauthorized tools

Employees use AI applications or agents outside the company-controlled environment, with the risk of exposing confidential information or intellectual property.

Policies that do not translate into practice

The AI Act is treated as an exclusively legal or IT matter, without connecting regulatory requirements to the processes, teams, and systems that use AI.

AI Act and ISO 42001 framework

We analyze the applicable obligations under the AI Act and translate them into specific policies and controls. We use ISO/IEC 42001 as a framework to structure AI management, define responsibilities, and establish continuous improvement mechanisms.
System classification and inventory

We identify in-house and third-party AI systems and document their purpose, owners, and risks. We classify systems by their risk level and analyze potential use of unauthorized tools or agents, known as Shadow AI.
Risks, bias, explainability, and traceability

We identify risks related to bias, reliability, and transparency, and define measures to reduce them. We establish traceability mechanisms to document how systems work and their controls, including, where applicable, data lineage and control of data sources in RAG architectures.
Roles, policies, and oversight

We define who is responsible for overseeing AI, establish internal policies, and set up continuous monitoring mechanisms to verify compliance. We can also analyze token consumption and its cost to the organization, identifying opportunities to improve efficiency in AI usage.
Data and AI environment security

We review security controls for data and AI platforms, including authentication, encryption, credential management, and isolation. We also identify model-specific threats such as evasion, poisoning, or information extraction, and define potential mitigation measures.
AI ethics

We assess the impact that AI use may have on employees and customers. We identify risks related to bias, discrimination, or lack of human oversight and propose measures to promote responsible use, with controls tailored to each project.

1. Inventory and classification

We identify the company’s in-house and third-party AI systems, analyze their purpose and risk level, and determine which obligations apply. This establishes a baseline to prioritize next actions and set a clear roadmap.
2. Gap assessment

We assess the maturity of data and AI governance to identify gaps in roles, policies, quality, security, and controls. If the company does not yet have a data governance framework, we can work on both areas in parallel.
3. Governance framework

We design a framework tailored to the organization, with policies, responsibilities, risk management measures, and procedures aligned with AI Act obligations and ISO/IEC 42001, also defining auditable evidence.
4. Controls, oversight, and awareness

We carry out technical and organizational controls, verifying compliance with regulations and implemented measures. We detect potential risks to address them in time and train teams so they understand how to use the technology and apply best practices in their day-to-day work to comply with regulations.

Situations we solve with AI Governance

AI governance helps companies control how they use artificial intelligence systems, from regulatory compliance to risk management and data quality.

Comply with the AI Act

Move from legal requirements to concrete measures that can be implemented and monitored.

Classify high-risk systems

Classify AI systems and prepare the evidence required for potential AESIA inspections.

Connect AI governance with data governance

Combine both approaches, whether the company already has a data management model or needs to create one.

Reduce bias and improve explainability

Detect potential bias in models and make their results easier to understand.

Control Shadow AI usage

Identify unauthorized AI tools and protect intellectual property with proprietary cloud environments.

Govern AI in regulated or industrial environments

Ensure that data, its metadata, and its quality meet the requirements needed to use AI models reliably in sensitive sectors.

Tangible results

Several companies across different sectors have chosen LISDATA to launch their AI governance projects.

In this use case, an institution relied on us to ensure compliance before deploying a new artificial intelligence system.

The Challenge

Before going into production, the organization needed to ensure the new AI system was compliant with data protection regulations, the European AI Regulation, and national security and interoperability frameworks.

Our Solution

At LISDATA, we produced a regulatory compliance report, analyzing regulatory frameworks, identifying the specific obligations of each regulation, and locating the technical and organizational evidence that supports them.

Technical Execution

We carried out a risk classification under the AI Act, reviewed regulations and legal bases related to data protection, examined third-party contracts and their certifications, such as Microsoft’s, and finally assessed the system’s traceability and control.

Results and KPIs

We produced a roadmap with six concrete actions.

We delivered a report confirming that the system complied with the required regulations after completing the six proposed actions.

The institution was able to deploy the AI system without fear of legal penalties for non-compliance.

Talk to our AI Governance team

AI Governance and Data Governance: how are they related?

Data governance and AI governance are related, but they address different needs. Data governance focuses on how a company’s data is managed, protected, and maintained, while AI governance defines how data is developed, used, and overseen within artificial intelligence systems.

If the challenge is data quality, traceability, ownership assignment, or data access policies, the starting point is data governance. If the need is to manage AI risks, define roles and controls over models, or comply with the European AI Regulation (AI Act), the project should focus on AI governance.

At LISDATA, we always recommend integrating AI governance into the data governance framework to leverage existing policies and control mechanisms. Although it is not necessary to have data governance implemented to get started, both areas can be developed in parallel, adapting to each organization’s maturity and needs.

Which regulations does AI Governance cover exactly?

AI governance is primarily based on the European AI Regulation (AI Act), ISO/IEC 42001, and other references related to risk management, security, and data protection in AI and digital environments.

What is the difference between the AI Act and ISO 42001?

The AI Act is a European regulation that sets legal obligations for certain artificial intelligence systems and uses. ISO/IEC 42001 is an international standard that defines how to implement and improve an AI system.

What changed in August 2026, and what is postponed to 2027?

On August 2, 2026, most of the AI Act began to apply, including transparency obligations for chatbots, AI-generated content, and deepfakes. Its application to high-risk systems in Annex III is postponed until December 2, 2027, and until August 2, 2028 for high-risk systems integrated into products regulated by the safety legislation in Annex I.

Do we need to have Data Governance already implemented?

No. Although it makes the work easier, a company can start defining its AI governance without having a data governance framework in place; both areas can be developed at the same time.

Should AI Governance be an independent area or part of Data Governance?

It depends on each company’s data and risks. AI governance can be integrated into data governance, leveraging its policies and control mechanisms, or operate as an independent area.

How are systems classified by risk level?

The AI Act defines four risk levels: unacceptable risk (prohibited practices), high risk (systems that may affect health and safety), limited risk (with transparency obligations), and minimal risk (no additional obligations specific to their category).

How is bias audited in our models?

The audit is tailored to each organization and model. For in-house systems, we review the training data. For third-party models, we review data, prompts, outputs, and how the company uses them. Each finding is documented with corrective action and rechecked periodically.

Who should take on oversight roles within my company?

Typically, an AI committee is created within the company to approve policies and make decisions on risks. In addition, each system has a trained accountable owner responsible for its operation and use.

How do you ensure explainability and traceability in production?

To ensure explainability and traceability, it is necessary to document what each system does, what data it uses, how it generates outputs, and who is accountable for its oversight. It is also advisable to log model versions, changes, and incidents.

How do you inventory unauthorized tools (Shadow AI)?

The first step is to identify which AI tools employees use. This can be done through internal surveys, access reviews, and analysis of corporate tools. Then, risks are assessed and usage rules are defined for each tool, with authorized alternatives.

How are models and intellectual property protected?

To protect sensitive data, you must control what information is entered into tools, who can access them, and where data is stored. Provider terms, permissions, secrets management, and extraction or exposure risks should also be reviewed.

How long does it take to implement an AI governance framework?

It depends on the number of AI systems and their risk level, but the work is organized in phases so there are results from the first weeks. As a reference, an initial operational framework is usually launched within three to six months.

Does this apply if I only use third-party AI tools?

Yes. Using third-party tools does not remove your company’s legal obligations. That is why it is important to review the provider’s terms, shared data, and potential risks, even if it is not an in-house tool.

How is this different from your Artificial Intelligence consulting or Discovery and Adoption?

AI governance focuses on defining controls for responsible AI use. Artificial intelligence consulting addresses business needs that can be solved with custom AI development, and discovery and adoption help identify opportunities, prioritize, and prepare the implementation of artificial intelligence in the company.

Ready to govern AI with real control over your data?

Prepare your company to use in-house or third-party AI tools in compliance with regulations. Define the controls, responsibilities, and security measures needed to protect your data and ensure responsible and ethical use of artificial intelligence in your business.

Talk to an AI Governance Specialist