Technical · Program, outcomes and prerequisites

Secure AI SDLC

Put AI-specific controls into the software lifecycle: design, build, test, release and operation (planning, construction, testing, release and running in production).

Request path adaptation All courses

The problem it solves

Companies often approach secure AI software lifecycle through scattered experiments: a few prompts, a few enthusiastic users, many doubts about data, quality and responsibility. This course turns that uncertainty into an operating method. Participants work on realistic scenarios, learn where AI helps, where human review remains essential and how to make the practice repeatable inside the company.

Duration: 4-6 hours, customisable
Mode: In-person or online lab, with guided exercises and materials adapted to the client.
Profile: For technical teams with programming and software architecture basics.
Final output: Operating canvas for secure AI software lifecycle.

Audience

For technical teams with programming and software architecture basics.

When to choose it

Choose this course when the company wants concrete progress on secure AI software lifecycle and needs training that produces usable workflows, not abstract theory.

Concrete outcomes

Map the work and the decisions where AI can reduce friction.
Build practical instructions, checklists and review criteria.
Identify data, privacy and responsibility boundaries.
Create reusable examples for the team.
Define next steps for adoption and governance.

Program

1
Architecture and requirements
Goals, boundaries, data, services and risk assumptions.
2
Build and integration patterns
Pipelines, interfaces, context, permissions and testing.
3
Evaluation and quality
Metrics, review, regression tests and failure modes.
4
Production and governance
Monitoring, security, audit, cost and maintenance.

Practical exercises

Map a realistic process connected to secure AI software lifecycle.
Create AI-assisted outputs and review them critically.
Define escalation and human review points.
Build a reusable checklist for daily work.

Materials delivered

Operating canvas for secure AI software lifecycle.
Prompt and instruction templates.
Quality and privacy checklist.
Risk/control matrix.
Adoption notes for the team.

Data, privacy and limits

The course uses synthetic, public, anonymised or client-approved materials. It explains how to minimise data exposure, protect confidential information, verify outputs and keep human responsibility explicit.

Prerequisites

Basic technical familiarity with software, data or system architecture is recommended.

Related courses

Technical60-90 minuti o modulo breve

Semantic search and AI knowledge bases

Turn archives and internal knowledge into search by meaning, with embeddings: numerical representations of the meaning of a text.

Choose it if: When the company wants concrete progress on semantic search and knowledge bases and needs training that produces usable workflows, not abstract theory.
Profile: For technical teams with programming and software architecture basics.
Final output: Operating canvas for semantic search and knowledge bases.

Program, outcomes and prerequisites

Technical5 hours, two 2.5-hour sessions

RAG Engineering for reliable AI systems

Design RAG (Retrieval-Augmented Generation) systems: they retrieve company documents and generate the answer citing them.

Choose it if: When the company wants concrete progress on RAG engineering and needs training that produces usable workflows, not abstract theory.
Profile: For technical teams with programming and software architecture basics.
Final output: Operating canvas for RAG engineering.

Program, outcomes and prerequisites

Technical4 hours, two 2-hour sessions

AI Coding Agents for software teams

Use coding agents as controlled parts of the software cycle: development, review, tests and release, with named responsibility.

Choose it if: When the company wants concrete progress on AI coding agents and needs training that produces usable workflows, not abstract theory.
Profile: For technical teams with programming and software architecture basics.
Final output: Operating canvas for AI coding agents.

Program, outcomes and prerequisites

FAQ

Is the course tool-specific?

No. Patterns and workflows are adapted to the tools and policies chosen with the client.

Can company data be used?

Only when accounts, contracts and internal policies allow it. Otherwise synthetic or anonymised data is used.

What remains after the course?

Reusable materials, examples, checklists and a clear set of next steps.

Is it theoretical?

No. The course is built around practical exercises and decisions close to real work.

Contact

A short conversation is enough to understand where to start.

The first call lasts 30 minutes, is free and clarifies the process, goal, available data, constraints and next useful step: training, consulting, operational start, data analysis or a controlled technical prototype.

Write to dtr@ar-tik.com