AI Training

AI training tied to your team’s real work.

Training is only valuable when learners can do the work after the course. SITECH designs each program around the audience, real tasks, and the organization’s security requirements — practicing on real work, not learning by rote.

The problem

Generic training does not change how people work

Training that teaches tools without teaching judgment leaves everything unchanged after the course.

After a generic course, staff still paste customer data into public AI tools. They still forward unverified AI output to their managers. Managers still cannot tell good AI use from risky AI use. The problem is not slow learners — it is training that is not tied to their real tasks, real data, and real responsibilities.

SITECH training does the opposite: it starts from each group’s real work — customer care, document processing, report compilation, document lookup — and teaches how to use AI in those very tasks, with that data, under that responsibility.

What the program includes

Learning through real work, not by rote

Each program is designed around the audience, the target outcomes, and the organization’s security requirements.

By audience: leaders (decision-making, risk assessment), staff (using AI in daily tasks), technical teams (deployment, integration, monitoring).
By real task: drafting documents, looking up internal documents, compiling reports, customer care, processing documents — each task has its own way of using AI and its own risks.
Practice on real data, in a safe environment: exercises use the organization’s own situations and data (under a confidentiality agreement), not unfamiliar examples.
Verification habits: how to check sources, recognize AI fabrication, and know when to stop and ask a person.
Guardrails in practice: learning to use AI within the organization’s rules — what AI may be used for, what it may not be used for, and who is responsible.
Materials to keep after the course: a handbook, checklists, and learning materials for the team to review and apply after training.

International references

Training to recognized standards

SITECH references the international standards and frameworks below when designing training programs — so content follows widely recognized AI governance practice, rather than isolated opinions. The standards are referenced to design the way we work; SITECH does not claim certification under these standards.

EU AI Act — Regulation (EU) 2024/1689, Article 4. The AI literacy obligation applies to all providers and deployers of AI systems (applicable from 2/2/2025). Training is a substantive way for an organization to meet this obligation — not through a token talk, but through a program tied to real work.

ISO/IEC 42001:2023 — AI management systems. The standard requires organizations to identify the competence and awareness needed for roles working with AI, along with roles, responsibilities, and accountability. SITECH training follows this requirement: each group learns exactly the competence its role needs.

NIST AI RMF 1.0 — AI Risk Management Framework. Four functions: Govern – Map – Measure – Manage. SITECH training ties into the first two: Govern (team culture, policies, roles, responsibilities, and practices) and Map (understanding the context, stakeholders, and risks of each AI system).

UNESCO — Recommendation on the Ethics of Artificial Intelligence (2021). Principles of raising AI awareness and understanding, together with human oversight. Learners do not just know how to use AI — they know when to oversee it and when to stop.

Safety

Training on real data, without trading away security

Data used in training stays only within the agreed scope — nothing is collected beyond the objective.

Practice exercises use the organization’s own situations and data, but with transparency on data storage, access, editing rights, and deletion. Learners practice the principle “sensitive data does not go into public tools” by doing it correctly in class, not just hearing about it. With deployment on a local server, data never leaves the organization’s infrastructure; the AI control layer (Guardrails) and AI gateway (Gateway) keep AI use within the rules. See how SITECH controls AI →

How we work

From survey to a team that can do the work

Step 1 — Survey the audience, work, and risks: who learns, what work they do, which risks recur.
Step 2 — Design the program by group: clear target outcomes for each audience group.
Step 3 — Hands-on training: exercises on real tasks, with direct instructor guidance.
Step 4 — Verify results: learners can do the work after the course, against criteria agreed in advance.
Step 5 — Materials and post-training support: handbook, checklists; support as the team applies learning to real work.

Frequently asked questions

Questions organizations often ask

Who is the training for?

Leaders, staff, technical teams — each group has its own program, because each group uses AI for different work and carries different responsibilities.

What form does training take?

The training format (in person, online, or blended) is agreed with the organization when the program is designed, according to team distribution and the nature of the tasks.

Does training use our data?

Yes, but only within the agreed scope. Data is used for practice exercises, nothing is collected beyond the objective, and storage, access, and deletion are transparent.

How do we know the training works?

Measured by output criteria agreed before the course: which tasks learners can perform, and with what level of verification — not by general impressions.

What if the team forgets after training?

The handbook and checklists left after the course let the team review and apply on their own. SITECH supports the team as they apply it to real work.