AI governance specialist: the new profile for controlling AI risks and compliance

The adoption of artificial intelligence within companies is creating a need that goes beyond developing good models or integrating generative tools. Organizations also need to decide which systems can be used, what information can be processed, who is responsible for overseeing them, and what controls should be applied throughout their lifecycle.

When different departments begin to use generative assistants, predictive models, RAG systems, or agents capable of executing actions, these decisions can no longer be managed individually in each project. This is where AI governance comes in, a discipline aimed at establishing policies, responsibilities, and mechanisms to control how an organization develops, acquires, and uses artificial intelligence.

With this, a professional specialization begins to consolidate: the AI governance specialist. This profile works at the intersection of technology, risk management, security, and compliance. Its goal is to ensure that the adoption of AI progresses within a defined framework, where risks can be identified, assessed, documented, and managed.

What is AI governance and why is it gaining importance

AI governance is the set of policies, responsibilities, processes, and controls used to direct and oversee the use of AI systems within an organization. A governance policy can determine who is authorized to approve a system, what information it can process, what assessments it needs before production, and what documentation must be retained.

Controls can also vary based on risk. An internal assistant used to summarize public documentation presents different implications than a system used to evaluate candidates, analyze financial information, or recommend customer-related decisions. Governance allows for recognizing those differences and assigning proportional controls.

NIST reflects this cross-cutting nature in its AI Risk Management Framework, which organizes AI risk management around four functions: Govern, Map, Measure, and Manage. Governance is integrated throughout the risk management process.

From isolated projects to enterprise capability

During the early phases of adoption, AI projects can remain relatively isolated. Complexity increases when they begin to connect with real business processes, raising issues about privacy, security, data ownership, vendors, response quality, human oversight, and responsibilities.

AI agents add another dimension because they can have tools and execute actions. The organization needs to establish under what conditions these capabilities can be used, what limits exist, and what operations require oversight.

AI governance provides a common framework to prevent each team from independently defining its own risk and control criteria.

What does an AI governance specialist do

The AI governance specialist translates general principles regarding the responsible use of AI into applicable processes within an organization. They do not necessarily develop the models or personally implement all technical controls; they coordinate how those systems should be governed and help define responsibilities.

Their work may include internal policies, system inventories, classification of use cases by risk, documentation requirements, privacy and security assessments, human oversight, and preparation of evidence for audits or compliance.

The GOVERN function of NIST includes precisely the establishment of policies, processes, and procedures to identify, measure, and manage risks, as well as understanding and documenting applicable legal and regulatory requirements.

The AI inventory as a starting point

It is difficult to govern systems whose existence the organization is unaware of. A company may have internally developed models, AI APIs integrated into applications, SaaS tools with generative functions, and employees using different external assistants.

A centralized inventory can record internal owner, purpose, provider, models used, data sources, users, level of autonomy, integrations, identified risks, and approval status. This information allows for applying different policies based on the characteristics of each case.

It also facilitates identifying shadow AI: tools or models used within the organization without having gone through the corresponding internal review mechanisms.

Governance throughout the lifecycle

Risks can change after deployment. A provider may update the model, a new data source may be incorporated, the purpose of the application may change, or a vulnerability that was previously unidentified may arise.

NIST proposes risk management as a continuous activity throughout the lifecycle. This involves establishing controls from design and acquisition to operation, update, and withdrawal.

An approved system under certain conditions may require a new evaluation if its functioning or context changes substantially.

AI governance, compliance, ethics, and security: different responsibilities

Governance and compliance are related but represent different areas. AI compliance focuses specifically on identifying and meeting legal, regulatory, or contractual obligations. AI governance establishes a broader framework for controlling how the organization uses artificial intelligence.

Governance can incorporate internal controls that do not directly stem from regulation, such as restrictions on the use of confidential documents in public generative tools. It also integrates risk tolerance, responsibilities, security, assessment, and oversight.

The boundaries are not absolute. A data leak through a generative application can simultaneously represent a problem of security, privacy, compliance, and governance. What changes is the specific responsibility that each function assumes to prevent or respond to it.

The relationship between AI governance and cybersecurity

As AI systems gain access to more information and tools, governance and cybersecurity need to coordinate closely. A model may process confidential information, a RAG system may retrieve business documents, and an agent may connect via APIs to internal applications.

Cybersecurity specialists work on identities, permissions, infrastructure, vulnerabilities, monitoring, and incident response. AI governance establishes the framework that determines what controls must exist, who must verify them, and what level of risk the organization is willing to accept.

Coordination prevents security from becoming an isolated review at the end of the project.

How to govern generative AI, RAG, and agents

Generative models present characteristics that require specific controls. Responses may vary to similar queries, the system may process data entered by users, and utilize content retrieved from external sources.

When the application incorporates RAG, user permissions should be maintained during the retrieval process to prevent a conversational interface from becoming an alternative access route to restricted information.

Agents add greater complexity because they can use tools. Policies must establish what actions can be executed automatically, which require human approval, what credentials the agent can use, and how each operation is recorded.

Responsible AI needs verifiable mechanisms

Principles such as transparency, accountability, privacy, or fairness need to be translated into concrete processes. Establishing that a system must be supervised by people requires defining who performs that supervision, when they intervene, what information they receive, and what capacity they have to modify a decision.

Transparency may also involve informing the user that they are interacting with AI, documenting known limitations, or providing information about the functioning and use of the model, depending on the context.

NIST includes elements such as validity and reliability, security and resilience, accountability and transparency, explainability, privacy, and management of harmful biases among the characteristics of trustworthy AI.

The EU AI Act and operational governance

In Europe, the AI Act introduces a risk-based regulatory framework that establishes different obligations based on the characteristics and use of artificial intelligence systems. Its implementation timeline is progressive and turns certain governance issues into operational requirements for organizations.

This first requires knowing what AI systems the company uses and what role it plays regarding them. Developing a system and using a solution provided by a third party may entail different responsibilities, and use cases do not necessarily present the same level of risk.

The AI governance specialist can connect regulatory analysis with inventories, classification, documentation, responsibilities, and internal controls, working alongside Legal and Compliance when appropriate.

Providers, documentation, and traceability

An organization does not directly control all the models it uses. Many business applications consume models via APIs or incorporate AI functionalities offered by external providers.

Before integrating these solutions, it is necessary to understand what data they receive, how they process it, what security options they offer, how they communicate changes, and what responsibilities correspond to each party. A provider update may change the risk if it incorporates new tools, persistent memory, or agentic capabilities.

Third-party management must continue after contracting

The initial evaluation does not always represent the future functioning of a solution. Therefore, third-party management must be integrated into the governance cycle and reviewed when capabilities, conditions, or risks change.

This continuity allows for detecting relevant changes and deciding whether an application needs a new evaluation, additional controls, or different restrictions.

Documentation and traceability to reconstruct decisions

Documentation serves an operational function in addition to a regulatory one. When an incident occurs, the organization needs to reconstruct which model was active, what version of the application was using it, what controls had been approved, and who was responsible for the system.

Traceability allows knowing what evaluations were conducted before deployment and what risks were accepted. A mature program establishes what evidence must be retained based on risk, avoiding both the absence of documentation and disproportionate bureaucratic processes.

AI governance needs to connect different teams

Governing artificial intelligence involves coordinating different perspectives. Technical teams understand how the system works; cybersecurity identifies threats and controls; Legal interprets regulatory obligations; Compliance supervises requirements; Privacy analyzes data processing, and business units know the context in which the solution will be used.

The AI governance specialist connects these perspectives through a common process. To do this, they need sufficient technical knowledge about generative models, APIs, RAG, agents, training data, inference, and monitoring, even if they are not the ones developing those components.

They must also translate complex requirements into controls that can be incorporated into the development, acquisition, and everyday use of AI.

When does a company need AI governance

Not all organizations need to immediately create an independent AI governance department. The need increases when AI adoption crosses different areas, uses sensitive information, or begins to influence relevant processes and decisions.

Signs that formal control mechanisms are needed

• Multiple departments use AI tools without a centralized inventory of applications and providers.

• Employees input business information into external models, and there are no clear rules about allowed data.

• The organization develops systems that influence decisions about customers, employees, or users.

• AI agents capable of accessing applications or executing actions are implemented.

• Each project defines its own security and approval criteria without an organizational standard.

• There are operations subject to regulatory requirements related to data, automated decisions, or artificial intelligence.

• No one has clearly assigned responsibility for risks once systems go into production.

Multiple departments use AI tools without a centralized inventory of applications and providers.

Employees input business information into external models, and there are no clear rules about allowed data.

The organization develops systems that influence decisions about customers, employees, or users.

AI agents capable of accessing applications or executing actions are implemented.

Each project defines its own security and approval criteria without an organizational standard.

There are operations subject to regulatory requirements related to data, automated decisions, or artificial intelligence.

No one has clearly assigned responsibility for risks once systems go into production.

When several of these situations coincide, governance becomes an operational necessity. The goal is to have common criteria before complexity is distributed among numerous projects and providers.

What skills does an AI governance specialist need

The profile requires a combination of technical knowledge and management ability. They must sufficiently understand AI systems to assess real risks and communicate with engineers, architects, and data teams, as well as handle risk management methodologies, corporate policies, documentation, and regulatory frameworks.

Communication is especially important because a technically correct policy that teams do not understand or cannot apply adds little operational value. They also need to work with uncertainty, as technologies, threats, and regulations evolve, and the governance model must adapt.

AI governance can facilitate more predictable AI adoption

Introducing governance should not turn every AI project into a chain of approvals. Low-risk cases can have simplified processes and previously authorized tools, while more sensitive projects receive additional evaluations based on predefined criteria.

This approach provides known rules before starting. NIST does not present its AI Risk Management Framework as a rigid checklist: practices can be adapted to the characteristics, needs, and risk tolerance of each organization.

Maturity consists of applying proportional controls and reserving the most demanding evaluations for systems where the potential impact justifies it.

The AI governance specialist gains weight in the AI ecosystem

Organizations are moving from experimenting with isolated tools to incorporating artificial intelligence within business processes, products, and decisions. This shift increases both the value of profiles that build models, agents, and platforms, as well as the need for professionals capable of establishing the conditions under which they can be used.

The AI governance specialist connects technology with risk, security, internal responsibilities, and compliance. Their role complements cybersecurity, legal, compliance, and technical teams through a framework where these disciplines can collaborate on the same systems.

As this capability becomes cross-cutting, having the right profiles also becomes part of the governance model. Outsourcing IT models can allow for the incorporation of specialists in AI, security, and architecture according to the needs of each project. If your organization is defining how to develop or deploy these capabilities, lateam can help you analyze the necessary profiles.