AI Governance in the Public Sector: How to Start Without Risk

Generative artificial intelligence has arrived in public administration. And with it, a recurring question among leadership teams: can we use it? In which processes? With what guarantees?

The answer is neither a definitive yes nor no. It is an "it depends" that requires analysis, judgment, and a plan. That is exactly what an AI governance framework provides.

Why governance comes before technology

Many organizations make the mistake of starting with the tool: contracting an AI service, testing a use case, and hoping for results. The problem is that without a prior governance framework, any incident (an erroneous result, a complaint, a query from the Data Protection Officer) can paralyze the entire project.

A governance framework establishes the rules of the game before playing:

• Which processes are candidates for AI integration and which are not.
• What levels of human oversight are required in each case.
• What data can be used and under what conditions.
• Who is accountable for AI-assisted decisions.
• How each use is documented to be auditable and defensible.

The regulatory landscape

Public administrations in Spain operate under a strict regulatory framework that directly affects AI adoption:

Spanish Data Protection Agency (AEPD): Has published specific guidance on AI and personal data usage. AI systems processing citizen data require a Data Protection Impact Assessment (DPIA).

National Security Framework (ENS): Any AI system deployed in a public administration must meet ENS requirements according to the system's category (basic, medium, or high).

EU AI Act: Classifies AI systems by risk level. Public sector uses, especially those affecting citizens' rights, are considered high-risk and require specific controls.

Three pillars of an initial assessment

Before implementing any AI solution, we recommend a structured assessment covering three pillars:

1. Criteria: where to apply AI and where not to

Not all processes benefit from AI. Some are ideal candidates (repetitive tasks, high document volumes, objective criteria). Others involve discretionary decisions that require more careful analysis.

The assessment evaluates each process based on its automation potential, associated risk, and impact on citizens' rights.

2. Direction: a defensible action plan

With the process analysis complete, a roadmap is developed:

• Prioritization of use cases by impact and feasibility.
• Definition of technical and organizational requirements.
• Realistic implementation timeline.
• Measurable success metrics.

3. Security: compliance by design

Each use case incorporates the necessary controls from the outset:

• Data classification by sensitivity.
• Human oversight protocols.
• Audit and traceability mechanisms.
• Contingency procedures.

Common mistake: analysis paralysis

The opposite risk to improvisation is paralysis. Some administrations have spent months (or years) debating internally without taking a concrete step.

A good assessment should take no more than a few weeks. It is a practical exercise, not an academic one. The goal is to make informed decisions quickly, not to produce an exhaustive document that nobody will read.

Conclusion

AI governance is not an obstacle to innovation — it is its enabler. Administrations that invest time in this initial step move faster and with less friction than those that improvise.

Decide well before deploying. That is the starting point.